Predictive Policing and The Construction of The 'Criminal': An Ethnographic Study of Delhi Police (Palgrave's Critical Policing Studies) 3031401018, 9783031401015

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Predictive Policing and The Construction of The ‘Criminal’ An Ethnographic Study of Delhi Police Shivangi Narayan

Palgrave’s Critical Policing Studies

Series Editors Elizabeth Aston, School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK Michael Rowe, Department of Social Sciences Newcastle City Campus, Northumbria University, Newcastle upon Tyne, UK

In a period where police and academics benefit from coproduction in research and education, the need for a critical perspective on key challenges is pressing. Palgrave’s Critical Policing Studies is a series of high quality, research-based books which examine a range of cutting-edge challenges and developments to policing and their social and political contexts. They seek to provide evidence-based case studies and high quality research, combined with critique and theory, to address fundamental challenging questions about future directions in policing. Through a range of formats including monographs, edited collections and short form Pivots, this series provides research at a variety of lengths to suit both academics and practitioners. The series brings together new topics at the forefront of policing scholarship but is also organised around who the contemporary police are, what they do, how they go about it, and the ever-changing external environments which bear upon their work. The series will cover topics such as: the purpose of policing and public expectations, public health approaches to policing, policing of cyber-crime, environmental policing, digital policing, social media, Artificial Intelligence and big data, accountability of complex networks of actors involved in policing, austerity, public scrutiny, technological and social changes, over-policing and marginalised groups, under-policing and corporate crime, institutional abuses, policing of climate change, ethics, workforce, education, evidence-based policing, and the pluralisation of policing. Editorial Board Dr. Matthew Bacon (University of Sheffield) Dr. Isabelle Bartkowiak-Theron (University of Tasmania) Professor Sofie de Kimpe (Free University Brussels) Professor Jacques du Maillard (CESDIP, France) Professor Nick Fyfe (Robert Gordon University) Dr. Laura Huey (University of British Columbia) Dr. Bethan Loftus (Bangor University) Dr. Ali Malik (Northumbria University) Professor Monique Marks (Durban University of Technology) Dr. Angus Nurse (Middlesex) Dr. Louise Porter (Griffith University) Dr. Pamela Ugwudike (Southampton University) Professor James Willis (George Mason University) Dr. Andrew Wooff (Edinburgh Napier University) Prof. K. Jaishankar, (Raksha Shakti University)

Shivangi Narayan

Predictive Policing and The Construction of The ‘Criminal’ An Ethnographic Study of Delhi Police

Shivangi Narayan Noida, India

ISSN 2730-535X ISSN 2730-5368 (electronic) Palgrave’s Critical Policing Studies ISBN 978-3-031-40101-5 ISBN 978-3-031-40102-2 (eBook) https://doi.org/10.1007/978-3-031-40102-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover credit: © John Rawsterne/patternhead.com This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicated to my Father, late Dr. Ram Narayan

Acknowledgements

This book is unequivocally dedicated to my father, Dr. Ram Narayan, who was my biggest cheerleader and who passed away before he could see a book with my name on it as an author. My father supported me in every way he could and he knew how, and if this book is a reality, it is because of him. I want to thank a number of people for making this book a reality but especially the journalist, Karn Pratap Singh, who wrote the article on predictive policing in Delhi in an English daily, Hindustan Times, which changed my life forever. I read the report, took an appointment to meet the Special Commissioner at the Police HQ who was overseeing this project and consequently got the chance to investigate an important governance venture in India. Predictive policing in India was in its infancy in 2017, and researching it gave me important insights into the pushes and pulls of the use of technology in governance in India. Permissions were hard to come by and here I must acknowledge my privilege of being a Ph.D. candidate/student at Jawaharlal Nehru University, (JNU), New Delhi. Access to the PHQ was possible partly because of the prestige of the university and partly because most of the India Police Service (IPS) officers in Delhi Police are alumni of JNU. I am thankful to the entire team of Digital Mapping Division at the Delhi PHQ for allowing me to talk to them for two years and help me understand the process of crime mapping in Delhi. Thank you to all the call takers and dispatchers who took time out of their busy schedules

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to answer my questions. Thank you to all the officers at police stations in South Delhi and North East Delhi for entertaining my request for records and other information. It was because you answered my questions so patiently that I could write this book. Thank you to the University Grants Commission, New Delhi’s Junior and Senior Research Fellowship for funding this study. Big thank you to the Algorithmic Governance and Cultures of Policing (AGOPOL) project, 2021–2014, funded by the Norwegian Research Council and Oslo Metropolitan University, Norway—A project I am currently part of, that has helped me further understand digital policing in India, for funding the conversion of my thesis into a book manuscript. Thank you Tereza Østbø Kuldova and Christin Thea Watane, this could not have been possible without you. Thank you to Tomas Salem, Veronika Nagy, Tessa Diphoorn, Paulo Terra, Jardar Østbø, Helene Gundhus, Ella Paneyakh, Ashwin Varghese, Dean Wilson, Kjetil Bøhler and Simon Egbert for listening to my draft presentations, providing useful suggestions and pushing me to publish it. You guys are not just colleagues but friends for a lifetime! A big thank you to Prof Radhika Singha for arousing my interest in crime and policing. It was her books and articles that helped me understand the trajectory of policing from preventive to predictive (also one of the questions you asked me in my fellowship interview). I’d be happy if I could ever be half the researcher you are! Thank you, Dr Divya Vaid, my doctoral supervisor and Prof. Vivek Kumar, Chairperson, Centre for Study of Social Systems, Jawaharlal Nehru University. Thank you to Vidushi Marda, Os Keyes, Noopur Raval, Nikhil Pahwa, Sandeep Mertia, Akaash Solanki, Gurshabad Grover, and many researchers in the AI and tech space, especially ones at the Centre for Internet and Society (CIS), who provided me with readings, explained concepts and helped me get around in tech research in India and across the world. I know what I know because of you. My family, my mother, Dr. Geeta Narayan, who does not understand what I do but supports it nevertheless, my brother, Dr. Abhinav Narain, who is my antithesis, my worst critic and my biggest help because of his LaTeX coding skills; My in-laws Smt Lata Bhagat and Mr. Khima Nand Bhagat, my other set of parents, who are always there to support me, give me the best advice and feed me great food, thank you. The sisters I never had, Pallavi Bhagat and Himani Bhagat, thank you for always making me feel at home. My friends Huzaifa Siddiqi, Harneet Kaur and

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Damni Kain, thank you for reading my drafts incessantly and commenting on them. Thank you Murali Shanmugavelan, for all the words of advice and for always being there to discuss, read, collaborate or just talk. I cannot thank you enough! It takes a village to raise a child and friends and neighbours to entertain them so that you can write a book. If it wasn’t for Sneha, Neha, Akanksha and Shruti for sharing my childcare responsibilities, listening to my frustrations and joining me for a cup of coffee whenever I was down, I would have never been able to finish the manuscript. Thank you Ratna Ma’am for the best daycare ever and being a second mother to my daughter. To my house helps, Omwati Didi and Priyanka who help me not get lost in household chores, Thank you. Last but not least, my husband, who is my partner in the truest sense of the word, Neeraj Bhagat, thank you for believing in me. If only you’d believe in my driving skills in the same way, I would be a formula one racer! And Gayatri, my little girl, my baby daughter, you make me better everyday. Thank you for coming into my life and making it beautiful.

Contents

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An Introduction Crime: Statistical Analysis to Algorithmic Predictions The Institution of the Police The Use of Technology in Policing The Specific Case of the Use of Technology in Policing in India Methods and Methodology Thus, Ethnography Fieldwork Diaries (a Discussion on Methods) Mapping the Book References

1 3 6 7 12 15 18 22 24 24

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Setting Up of the Digital Mapping Division Inception of the Crime Mapping Centre Setting the Direction for the DMD Crime Mapping Beginnings Address and Other Base information for Maps Police Station Boundaries The Dial 100 Call Centre as the Source of Crime Data Dissemination Resources and Training for Mapping and Data Collection Bureaucratic Entanglements of the Digital Mapping Division

29 31 32 34 37 40 41 43 44 50

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CONTENTS

CMAPS Crime Mapping Is Not a Technological but a Human Endeavour References

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Green Diary Introduction The Birthplace of the Green Diary: The Dispatch Command Room The Call Receivers—Dial 100 Call Centre PA 100 Form Training and Resources Location of the Crime Proposals for Better Location Tracking Dispatch Floor PCR Vans at the Crime Scene Written Accountability Categorisation of Crime Implications References

61 61

55 59

66 69 72 77 77 80 83 86 86 88 91 95

Crime Mapping and the Construction of a Criminal Introduction A Recap of Mapping Programme in Delhi Police Mapping Crime Maps and Digital Tech: Geographic Information Systems (GIS) Layers of Crime Mapping Analysis and Predictive System or CMAPS Location, Location, Location! Manual Maps and Their Plotting Inaccuracies The Construction of the Criminal References

97 97 99 100

Epilogue References

125 129

Index

104 105 107 111 113 121

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Abbreviations

ADRIN C4i CAA CAG CATS CCTNS CDAC CFPB CMAPS CPU CRDD DCP DCPC DCR DMD DSSDI ESRI FIR GIS GPS HCL HF IPF ISRO L&O LPA

Advanced Data Research Institute Command Control Coordination Communication Integration Citizenship Amendment Act Comptroller and Auditor General Centralised Accident and Trauma Service Crime and Criminal Network Tracking Systems Centre for Development of Advanced Computing Central Finger Print Bureau Crime Mapping Analytics and Predictive Systems Central Processing Unit Call Record Daily Diary Deputy Commissioner of Police Directorate of Coordination of Police Computers District Control Rooms Digital Mapping Division Delhi State Spatial Data Infrastructure Environmental Systems Research Institute First Information Report Geographical Information System Geographic Positioning Systems Hindustan Computers Limited High Frequency Integrated Police Forms Indian Space Research Systems Law and Order Local Police Action xiii

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ABBREVIATIONS

MeitY MHA MLC NCRB NERS NIC OBC PA 100 PCR PDA PHQ PPP RWA SC SHO SOP ST

Ministry of Electronics and IT Ministry of Home Affairs Medico Legal Case National Crime Records Bureau National Emergency Response System National Informatics Centre Other Backward Classes There was no full form available for this Police Control Room Personal Digital Assistant Police HeadQuarters Public–Private Partnership Resident Welfare Associations Scheduled Castes Station Head Officer Standard Operating Procedure Scheduled Tribes

CHAPTER 1

An Introduction

In the year 2018, during my fieldwork at the Delhi Police HQ, one of the officers in the Digital Mapping Division (DMD) showed me a ‘daily crime map’ of the city. While showing me the map, he said that it was the Muslims in the city who were responsible for most crimes. When I said that on the map, the so-called Muslim areas appeared mostly crime free, he said “What do they even have to steal in their own areas”, following it up with: “They know they will be caught in their own areas so they go out and commit crimes in other areas”. The police’s perception of a ‘criminal’ as poor, belonging to minority religion and residing in marginalised areas is universal. So pernicious, that it was also seen in the popular web series called Patalok (The Underworld) on Amazon Prime Video. Here a Police Officer, Hathi Ram describes the North East Regions of Delhi as the ‘underworld’, whose inhabitants move to upscale places in the National Capital Region (NCR) like Vasant Vihar, Noida or Preet Vihar to commit crimes. Hathi Ram tells his junior officer, Ansari, how no one in the government or the police department cared about what happened inside the underworld. The only reason the underworld was policed was to make sure that its inhabitants do not disturb the peace of the ‘normal’ citizens of the city or as Khanikar (2018) calls them ‘solid’ citizens, as opposed to the quasi citizens, who are residents of the slums and poorer

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2_1

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parts of the city. Solid citizens are always anxious of the quasi citizens, necessitating the policing of the latter. In the current world, technology is readily coming to the aid of the police in its quest for law and order. However, if what is defined as ‘crime’ and thus, who is recognised as a ‘criminal’, is a function of the social (Durkheim 1933), can then technology really help in identifying the objective ‘criminal’? The problem gets more complicated when we realise that crime is not just what offends the collective conscience, but what affronts the dominant collective conscience. In India, the Hindu religious tradition is the dominant tradition of the country. As part of this tradition, different punishments were reserved for various caste members: for example, according to Ambedkar (2014, 52), the Manusmriti, the hindu code book that describes the course of action to be taken in different situations, forbids a king from killing a Brahmin even if the latter is accused of all crimes. For the crime of adultery, Ambedkar throws light on the ordinance 379 from Manusmriti which says that an adulterer from the priestly class may suffer “ignominious tonsure”, while those belonging to other castes would be punished with the loss of life. In keeping with such a skewed view on an individual’s birth determining the legal punishment for crimes they might commit, provisions of the Criminal Tribes Act of 1857 criminalised people by birth and databases like “habitual offenders”, which emerged as a result of the Habitual Offenders Act, 1952, assigned a natural propensity of criminality to certain people. Authors like Nandi (2016, 2019), Kidambi (2004) and Ghertner (2012, 2015) describe the disdain for and implicit criminality ascribed to immigrants and slum dwellers from colonial times till today. Poverty is constructed as their personal failing and a propensity to crime as their natural instinct, as can be seen in the use of words like “addicted”—to crime—in both the Criminal Tribes Act 1871 and Habitual Offenders Act, 1952 (Narayan 2021). The officer in the Digital Mapping Division at the Delhi Police HQ who spoke to me in 2018 believed, even against proof, that people from certain communities are ‘Criminals’. The account in Patalok might be fictional, but as I found during my research, it was not without basis in real life. Residents of North East Delhi were assumed to be ‘criminals’ and it was the police’s job to keep them out of sight of the rest of the city. It became evident to me that it would be vital, from a sociological point of view, to examine how the idea of a criminal is constructed, especially

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with the use of algorithmic systems which have the power to validate social prejudices with the veneer of an assumed neutrality and objectivity of technology.

Crime: Statistical Analysis to Algorithmic Predictions Crime statistics have been a permanent feature of policing for a long time now. The Indian Police Commission of 19021 introduced the standard forms and registers in which crime data could be recorded at the district and police station levels. These forms are used till today and are used to collate crime information from all over the country; we can access this data from the National Crime Records Bureau (NCRB) in the form of crime reports every year. These reports are used by various government organisations and even police organisations themselves for designing policies for crime control and resource allocation of the police. Bayley (1969), however, recognises two sorts of problems in using Indian crime data: the first can be grouped under the technical problems of collection, transmission, computation and presentation of data; while the second is concerned with the adequacy of data collected by police reporting agencies. Prasad (2013) found a significant gap in victim-reported crime and police-recorded crime in India that indicated the reluctance on the part of the police to record all crimes brought to its attention. Authors such as May (2001), Sutherland and Vechten (1934) and Heidensohn (1989) have demonstrated how crime statistics only present a skewed picture of crime and criminals. May has spoken about a definite role of police negotiations and arbitrations in the gathering of input data that is used to compile crime statistics, an argument furthered by Bayley (1969), according to whom it is in the interest of the police to keep the crime numbers down. Therefore, apart from under-reporting crime, they also downplay the severity of it by categorising it into less harmful categories. Bayley says, “a felony becomes a misdemeanour, a murder an unnatural death, a riot an affray and major robbery a petty theft” (p. 99). He also informs us that police exercise discretion in visiting the scene of the crime in a number of cases. A number of times if the investigating

1 From NCRB website regarding its origin story, https://ncrb.gov.in/en/origin-ncrb, accessed October 14, 2020.

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officer (IO) is busy, they could avoid visiting the scene of crime and take the details on the phone, thus missing vital details of the incident. The IOs I spoke to told me that the caseload on an IO can go up to four to five cases a day, some of which could involve a medical examination of the victim, called a medico-legal case or a MLC. This is a bureaucratic affair that could take the whole day, they said, attending to other cases impossible. Other times, it is just an indifference to certain kinds of crimes and victims. May (2001) argues that crime statistics would be very different if white-collar crimes were tried as criminal cases instead of civil ones. By ‘different’ he implies that if this were done, then the statistics would not only reflect poor criminals from ghettos and marginalised communities, which is currently the case. Heidensohn (1989) claims that crime records are socially constructed because of the discretionary ways in which crimes are reported and recorded because most crime reporting and recording heavily favours petty and visible crimes. In the same way, mistrust in the police system in India may lead to improper reporting of crime and many crimes might go unreported completely (Bayley 1969). Today algorithmic analysis of crime data has replaced its statistical analysis, which claims to provide a more accurate and unbiased picture of crime. A specific form of algorithmic analysis claims to analyse large amounts of crime data to provide accurate details of high crime sites (hot spots) for resource allocation. However, scholars such as Introna (2016), O’Neil (2016), Ferguson (2017), Eubanks (2017), Noble (2018) and Egbert and Leese (2020) have argued that algorithms are only as unbiased and neutral as their creators, a cohort which, in today’s world, still belongs to the majority race or upper caste or class (as is known popularly). Connecting traditional policing and algorithmic policing are the old biased datasets (crime records produced on police discretion) that are used to train the algorithms, which in turn result in the latter reproducing the prejudices of the former (Jefferson 2017). O’Neil (2016) has argued, for instance, how algorithms used to understand recidivism in America are hugely biased against Blacks, who, in any case, form the largest group of people in American prisons. Similarly, Rouvroy and Berns (2013) explain that algorithmic analysis now decides the new normal and everything that fails to fit into its patterns is to be policed/disciplined. In the specific case of policing, Coleman and McCahill (2011) argue

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that modern policing systems that work on calculating risks using algorithmic analysis are designed for the reordering of the public realm by the exclusion of the socially and politically marginal. Later in the book, we will learn how biased algorithmic systems are more than just their biased datasets but are also influenced by the social setting they are deployed in. For a complete understanding of algorithmic analysis and how they propagate existing prejudice, merely studying the algorithms (the code or the technical part of an entire smart system) is not enough. Neff et al. (2017) argue that data scientists need to understand how context, technological infrastructures, social processes, human biases, and differences in computational tools influence the process of knowledge creation in a world where data is considered objective and value free. They point to the irrationality of the thought that the world could be known with the help of algorithmic analysis without giving any consideration to the ways in which data is considered independent of the infrastructures of its production or the way truth is something that remains outside the social processes of negotiations in which data is produced, or outside of human understanding and theory. According to Nick Seaver (2019, 418) for a complete understanding of how algorithms work, one needs to study algorithmic systems instead of just algorithms. Seaver defines algorithmic systems as “intricate, dynamic arrangements of people and code” (ibid.). In making algorithmic systems a unit of study rather than only the “code”, Seaver wants to open up the discussion to the “hundreds of hands reaching into them (these systems), tweaking and tuning, swapping out parts and experimenting with new arrangements” (ibid., parenthesis mine). My research thus aims to examine the construction of a ‘criminal’ in predictive policing to find out if, as per its claims, algorithmic analysis of crime produces an objective and unbiased figure of the criminal or reifies the commonsensical understanding of being one. Here, ‘commonsensical’ understanding constitutes being male, poor, residing in slums or being from marginalised communities (we are looking at urban criminals here). Predictive policing, also known as ‘hotspot analysis’ of crime, or crime mapping, analyses data on crime activities using algorithms to predict and control crime in any given area by efficient allocation of police resources. The preferred tool is a computer-mapping system, known as the Geographical Information System or GIS that helps to plot crime data on a map to understand its trends and patterns.

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Innes et al. (2005) and Amoore and Goede (2005) argue that data mining algorithms, especially in the police, turn “questionable data” into “hardened” facts impacting the lives of the people involved. Further, studies of crime statistics claim that these do not represent the true picture of crime in any given society because they are contingent on definition, detection, reporting, recording, negotiations of crime and on police practices (Heidensohn 1989; May 2001; Sutherland and Vechten 1934). With this background, and using the conceptual framework of the sociology of science (or sociology of scientific knowledge as it is also known) according to which “scientific facts” are contingent on their social process of production (Knorr-Cetina 1981), I investigate the claims of “objectivity” and “neutrality” in predicting crime hotspots of predictive policing systems. The social processes, which include organisational processes, direct us to look into the bureaucratic processes of recording, reporting and making data ready for analysis, the same data that would be fed to produce the crime hotspot outputs in the above-mentioned system.

The Institution of the Police Clive Emsley (2017) takes us through a history of the origins and development of the police in Europe, a model, which then spread across the world through colonialism. Many countries such as India still have systems based on this structure of policing. Elsewhere, Emsley (2018) writes extensively about colonial policing and how it was introduced primarily for the preservation of law and order (Neocleous 2000), and not for crime control per se; the police were configured to control the working classes which were becoming increasingly powerful. The police in India were born with the Indian Police Act of 1861, based on the recommendations of the first Police Commission of 1860. The British government in India reorganised the police not on the basis of the independent London Police Force but on the basis of the Royal Irish Constabulary, a semi-military police force in character (Mehra and Levy 2010). Both in its reorganisation and in the rules according to which it was set up, the police in India were required to be accountable to the state, not the people. Mukhopadhyay (1997) notes that policing in India continues to be influenced by its colonial ancestors, which is the reason why maintenance of law and order is central to its functioning. Anderson and Killingray (1991) describe colonial policing as a coercive force maintained primarily to subdue local discontent in the colonies. Radhika Singha (1998, 2015) further explains

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that apart from only maintaining law and order, colonial policing was also about a certain civilising project in India where vast swathes of Indians were to be converted from a nomadic life to pastoral life. Especially in India, thus, policing was a way to police caste (Narayan 2021). The functioning of the police in India should be seen from the lens of their task of maintaining ‘order’ and not from the lens of their perceived task of controlling crime. The complicated nature of the institution of police is demonstrated by Jauregui (2016) who rightly posits that police in a postcolonial nation like India is not just a manifestation of vulgar power of the government but a kind of a social resource which can be used in myriad ways to help realise the desires of the people. Just as Khanikar (2018), in showing how women residing in slums in Delhi use the police’s power to arrest to get back at their abusive husbands, demonstrates how the police force in Delhi is used by the very people it incarcerates as a resource to solve their troubles.

The Use of Technology in Policing Police have been one of the few state institutions to readily accept technology, more often than not because (a) Use of technology in the police provides positive media stories that the state is doing all it can to keep its citizens safe and (b) It removes accountability from the police and gives it to tech, so all the biases can be attributed to the latter. Byrne and Marx (2011) opine that the career of crime prevention has been invariably connected to that of technological innovation. According to them, there are two types of technologies that are used in crime prevention—“hard” and “soft” (p. 17). The former relates to a physical kind of technological innovation, such as cameras or satellites and the latter is concerned with information and its processing/analysis in control of crime. While the automobile, telephone and two-way radio have been hailed as the most important policing innovations in the history of crime control, soft technology includes data collection, analysis and use of information in catching and indicting criminals. Taking their argument forward, I would argue that fingerprinting could be termed as the earliest soft technology in the world. It was invented in colonial India, according to Sengoopta (2003), when the British needed a system to identify criminals accurately. During the colonial period, Sengoopta states that the British in search of a better system of identification of criminals, started looking into the anthropometric systems used in France and wanted to test “the cost of

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their maintenance, their general utility and the propriety of introducing them” (p. 12). Finally, he says, they found the most infallible method of identification in the form of fingerprints: “Fingerprints, the committee (the Troup committee that was set up to find the best method of identifying criminals) declared, ‘are an absolute impression taken from the body itself… For ease of obtaining and for providing proof of identity, fingerprints could not be bettered” (ibid.). Currently, the Central Finger Print Bureau (CFPB) under the NCRB in New Delhi maintains the fingerprint records of criminals who are arrested for national/international crime in the country. Fingerprinting is no longer the complex technology that it once was; with the development of low cost scanners and readers, it is now ubiquitously used for crime prevention and control. Soft technology innovations in India have been scattered and have largely been borrowed from those in the Western Countries, mostly USA, which uses such technologies on a vast scale. For instance, the USA maintains a database of the movement of 800,000 registered sex offenders and uses the data to analyse the chances of recidivism in these offenders. They also use GIS to understand the impact of residence location on offender recidivism. Financial offenders are further monitored in the same way, so as to surveil them effectively and monitor their risk for recidivism (Byrne and Marx 2011). Ericson and Shearing (1986) provide the starting point to understand what we call “intelligence” led or data-led policing today, what we have referred to as soft technology for crime control and prevention above. The collection of data with the help of surveillance, which could be both direct and indirect, represents a crucial aspect of these technologies. Surveillance is conducted through written records, by monitoring offenders’ online activity (indirect) and through such devices as CCTVs (direct). The use of surveillance by the police in India can be traced back to the use of the “Special Branch” by the colonial police, which was continued in independent India. They were instituted to keep an eye on all political parties of India (including the ruling Congress at that time) (Bayley 1969). Bayley informs us that the police in independent India continued the Special Branch because surveillance provided useful information even while impinging on the freedom of political leaders (through direct surveillance and through dossiers maintained on political leaders). The open and visible surveillance action of the Special Branch, something that Bayley calls “inevitable and necessary” (ibid., 134), was considered less harmful than “giving a surreptitious mandate to some agency in the

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bowels of bureaucracy hiding behind an innocuous name” (ibid., 134). Singha (2015) further informs us about the police surveillance of seemingly “dangerous” individuals in colonial India, as preventive policing measures, to maintain law and order. In present times, this surveillance does not merely entail people ‘watching’ certain underlined groups and maintaining dossiers or initiating action on them but as Byrne and Marx (2011) claim, a substantial amount of funding is now being pumped into the development of computer software and tools that help in monitoring people online such as requests to view browsing history, messages, emails as well as documents in the cloud. This kind of blanket surveillance in policing is, as Stenson and Sullivan (2012) explain, how criminal justice works in a risk society where crime is supposed to be policed not on a case-to-case basis, but is a risk that needs to be mitigated for the well-being of the entire society. The new police surveillance that responds to a more risk-determining rather than punishing mandate, is concentrated on targeting aggregate populations rather than individual suspects. Gary Marx (2002, as cited in Coleman and McCahill 2011) describes it as “a new surveillance”, which monitors geographical locations, time periods and categories of people as opposed to individual suspects. Doleac (2017) argues that finding costeffective ways of reducing crime has been the primary focus of economists in recent times: crime to be controlled in such a way that fewer people will commit crime when expectations of punishment increase. Such a model necessitates people self-controlling themselves so that the state does not have to intervene. Surveillance and basic computing technologies have helped police achieve these goals but the flip side of such policing is that public records of crime increase the chance of stigma and increase the possibility of repeat crime by one-time offenders. Manning (2008, as cited in Coleman and McCahill 2011, 76) explains that the use of new surveillance technologies for crime depends on existing crime narratives and how they fit with existing organisational and occupational structures. Therefore, he argues, crime mapping technologies are mediated by similar concerns as those held by frontline officers in police stations. He further states that a few years ago in the Western world, especially the USA, crime mapping systems were operated by retired clerks in the police who were put on the job because of the bureaucratic needs of the station. The police used surveillance systems to track current decisions rather than to plan future actions (ibid.). However, as we have already seen and as Byrne and Marx (2011)

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describe, surveillance is now much more sophisticated in the USA with enhanced abilities to store and analyse information and to purchase expensive devices to monitor and record people. Policing Everything (Shaw 2016) again describes the government’s fixation, especially in the USA, to securitise everything, which is now evident with the advent of drones in policing. In the same vein, Crampton (2014) talks about the data fetish of governments in the race to securitise everything, evidenced in Snowden’s2 case. The use of Big Data and algorithmic analysis of such data tell a story about people, but a story that is incomplete or even sometimes, far-fetched.3 In this regard, Jefferson (2020) and Ratcliffe (2002) have argue about the harmful impact of technological surveillance of criminalised populations, especially the black population in the USA, and their mapping in the form of “crime maps”. This kind of surveillance is both broad-based and in-depth, resulting in detailed databases of those considered criminal, which is invariably the Black population and other racial minorities. Further, Ferguson (2017), Brayne (2021), Ratcliffe (2016), Eubanks (2019), Noble (2018), Moses and Chan (2018), and Harcourt (2010), discuss the problems associated with biased policing algorithms that eventually lead to marginalised communities being criminalised. They argue that predictive policing algorithms are being projected as neutral and objective but are anything but. On the contrary, the inherent bias of the data used to train algorithms or the way the data is analysed leads to prejudiced conclusions that put vulnerable communities in harm’s way. Predictive/algorithmic tech works on the basis of a number of criminological theories, such as the Routine Activity Theory, Rational Choice Theory, Social Disorganisation Theory, Strain Theory and Social Control Theory. The Routine Activity Theory states that the coming together of a motivated offender, suitable target and lack of a capable guardian in the condition of various routine activities serve as ingredients of a crime. Schools, hotels and motels, bars, subways and malls are places where 2 Edward Snowden is a former contractor of the United States of America’s Central Investigation Agency, CIA, leaked a number of transcripts of surveillance to the media unveiling US’s intelligence gathering activities to the world. He was charged for espionage by the USA while the rest of the world hailed him as a hero. He was granted temporary asylum by Russia. A BBC story about the same: https://www.bbc.com/news/world-uscanada-23123964, accessed Wednesday, December 2, 2020. 3 Reclaiming the stories that algorithms tell, https://www.oreilly.com/radar/reclai ming-the-stories-that-algorithms-tell/, accessed Thursday, September 10, 2020.

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victims and criminals come together in large numbers and thus become hotbeds of criminal activities. “Features such as the number of shopping malls and the number of bars are useful independent variables of routine activity” (Chainey and Ratcliffe 2005, 334). These features are called ‘layers’ which are used as filters during analysis in GIS. Information such as socio-economic conditions of the region, and other unique features of areas of the city are also included in these layers. In Delhi Police, data for these layers was collected by the Digital Mapping Division (DMD). You can read more about layers in Chapters 2 and 4 of this book. The Rational Choice Theory, on the other hand, states that offenders make an informed choice to commit a crime based on the opportunities available to them in their current environment. Chainey and Ratcliffe (2005) argue that both Routine Activity and Rational Choice theories are linked because they are not concerned with the residential situation of the offenders but with “the opportunities that are presented to offenders when they interact with potential targets” (ibid.). Another important theory used in mapping analysis is the Social Disorganisation Theory that states that lack of social cohesion and control impacts the rate of crime in any society. Social disorganisation suggests that if there is a high degree of cultural heterogeneity and a high turnover of residents, the community are unlikely to be able to agree to a common standard for behaviour in the street, and that few residents are likely to know the young people on the street, or their families. (Chainey and Ratcliffe 2005, 335)

The three main indicators of social disorganisation are listed as poverty, residential mobility and ethnic heterogeneity. The authors contend that though it has been difficult to find variables that would accurately describe and thus help measure social disorganisation, researchers have relied on variables such as “single parent families with children, percentage of immigrants, percentage of blacks, heterogeneity (measured in various ways), percentage of residents who have moved in the last five years, SocioEconomic Status (SES), population density and family disruption” (ibid., 336). The variables for social disorganisation directly implicate poor and marginalised families as being prone to crime without considering the structural issues that plague them. In other words, these theories, neatly accepted in algorithmic or data-based systems, simply legitimise the claims

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of socially dominant communities who have marked the less fortunate for carrying a natural propensity towards crime. As I argue in Narayan (2021), the very fundamental of the caste system in India is based on the belief that those belonging to castes lowest in the caste hierarchy are criminals by birth. The Social Control Theory (Costello and Laub 2020) further states that the absence of familial bonds can cause criminal behaviour. This theory puts a strong emphasis on the lack of individual self-control, in which good parenting plays an important part as the reason for a person committing crime. Expectedly, good parenting again skews this theory against the marginal, poor classes where parents are unable to provide maximum attention to their children because they have to work long hours and are unable to provide adequate childcare in their absence. Similarly, Strain Theory (Merton 1938) states that anomie results when the desirable goals of a society are in conflict with the opportunities provided to achieve those goals. Though Merton insists that poverty is not the only variable responsible for anomie, which only occurs when a complex mix of related social and cultural conditions come together, the police’s insistence on the get-rich-quick aspirations of the poor as a cause of crime shows how the theory is used and perceived in real life.

The Specific Case of the Use of Technology in Policing in India Bayley’s 1969 work on police in India gives an overview of the miserable communication and crime recording systems in postcolonial India. Bayley explains how crime in India was heavily underreported because the surest way to report a crime was to go physically to a police station. However, an array of reasons, such as bad roads, inclement weather and lack of transportation options kept the people away from the stations. This was also the reason why police could not reach crime scenes on time, and the situation was exacerbated by the fact that there was no communication between stations. Even when radio and telephone systems came up in the country, they were for a long time, largely confined to cities and urban centres, which added to the problem. Indeed, the police force has always been seen as the most worthy recipient of developments in technology by scholars due to the rising needs of security and crime control. According to Raghavan (2003), the data-sharing facility between police stations (which was not available before) has been a boon for the police

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in tracing their suspects. He finds the growth in forensic technology, especially that of DNA sampling, to be another crucial development in police technology. However, forensic technology grew very modestly in pre-independent and even in the early years of post-independent India. A Fingerprint Bureau (FPB) was established in Calcutta in 1897, as we have seen before, but it was only after independence that a Forensic Science Laboratory (FSL) was set up in Calcutta in 1952 and in Bombay in 1958 (ibid.) Wireless communication was set up for the first time in 1946 and the Directorate of Coordination (Police Wireless) started in 1949. The radio operated via High Frequency (HF) channels while Morse Code Signals were used for the transmission of information. Very high frequency channels opened up only in 1964 and were supported by repeater stations, which enabled speech communication leading to the development of mobile wireless fitted patrol vans (ibid.). The Police Modernisation Scheme, set up by the Ministry of Home Affairs in 1969, which provided grants to the police, gave an impetus to the police to extend the use of science and technology in its everyday use. Initially, 25 per cent of the money was given as grant-in-aid and the rest as a loan, but after 1974–1975, the grant money was doubled (ibid.). The report of the National Police Commission, constituted in July 1978 (the report was out in November 1979), lobbied for the police to be allowed to build its own multi-channel microwave trunk-routes in order to provide linkages at the police station level. Another suggestion was to create a Directorate of Coordination at the apex level to ensure greater use of computers in police investigations. It also said that the police should take full advantage of electronic data processing facilities and recommended “the provision of key-to-tape data input devices and optical readers and the expansion of on-line disk storage of capacities of computer centres. It envisaged six high capability regional computer centres which could be connected to the national grid” (ibid., 194). In 1975–1976, the computerisation of crime records was included in the list of programmes to be funded by the government under the police modernisation scheme. The Directorate of Coordination of Police Computers (DCPC) was set up to introduce computerised crime and criminal information in the states. A study group was set up to examine the strategies of setting up such a system followed by a committee on crime records set up in 1978. Another task force was set up in 1985

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constituted by the Ministry of Home Affairs (MHA) of the Government of India that recommended setting up a national repository of crime and criminal information in India. This is how the NCRB came into being (ibid.). An interesting development was the creation of standardised “forms” for recording crime information across India, known as Integrated Police Forms (IPF) (ibid.). Predictive policing is slowly making its presence felt in India. It started with the Jharkhand Police, but the plans did not materialise even after trying in 2012 and then 2016. Delhi Police adopted predictive policing in 2015 under its Crime Mapping and Predictive System (CMAPS) designed by Indian Space Research Organisation (ISRO). As far as scholarly work on predictive policing in India is concerned, Jaishankar et al (2004) have studied crime mapping in Chennai but not from a critical lens of impact on marginalised communities or those communities who have been discriminated against by the police. Parveen (2017) has also outlined the use of technology in policing in India but again has missed any critical engagement with the endeavour. Marda and Narayan (2020) have, however, explained how data produced in police organisations is subject to negotiations by the police and carries along with it ingrained social biases. Such data can only exacerbate existing social discriminations and put marginalised communities further at risk of being incarcerated. Anja Kovacs (2020) reiterates that policing using Big Data can indeed exacerbate already existing biases against marginalised communities and women. She gives the example of Section 36A being included in Karnataka Police Rules in 2011 where transgenders were considered a criminalised community. Such data, she argues, when fed into predictive policing algorithms can reify existing discriminatory practices against the transgender community, making them a criminal just by virtue of their gender. It has to be said that literature via a critical lens on smart/predictive/algorithmic policing in India is almost absent. While we could keep borrowing from studies in the West to understand the phenomenon, our unique social conditions along with the quality of resources and infrastructure requires that we study algorithmic policing systems in the Indian context. This book aims to fill this gap in the literature on India’s tryst with predictive policing, especially with regard to the latter’s impact on marginalised communities.

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Methods and Methodology How I Arrived at Ethnography as Methodology Pointers from Pilot Study I first visited the Central Police Control Room (CPCR) in Delhi Police HQ, Delhi in the months of March and April 2017. Predictive policing is a relatively new concept in India. Jharkhand had ventured into predictive policing in 2012 and signed an MoU with the Indian Institute of Management, Ranchi to use their data analytics capacities for predicting crime which did not work out as desired. In 2016, Jharkhand again tried to reconstitute a predictive policing centre that would conduct crime hotspot analysis. As of 2022, however, there is no news of any such system coming up in Jharkhand. In the meantime, Delhi adopted predictive policing in a more formal way in partnership with ISRO’s Advanced Data Research Institute (ADRIN) and planned to scale this operation in the coming years. Apart from the fact that Delhi Police lead the predictive policing initiative in India, I chose Delhi because it is accessible both in terms of locale and language to me. The Delhi Police is one of the largest police forces in India with a sanctioned strength of 83,762 personnel.4 It came into existence with the Indian Police Act of 1861 (as did all the police forces of all the states under British rule in India) and was a part of the Punjab Police till India won its independence in the year 1947; however, and interestingly, it still works under the specified Punjab Police Rules. The rules for marking a person as serial offender, history-sheeter remain the same as they were in 1933. I went to Delhi Police to find out about a brand new, and for the lack of a better word, ‘swanky’ system of predictive policing in India with a sizable number of analysts working on digital data to predict crime in the city. However, I found the reality to be considerably different. After speaking to the team of analysts at the DMD of the Delhi Police Headquarters, I got to know that Delhi has been analysing crime data and designing hotspot maps since 2008. The team at the Digital Mapping Division comprises5 Incharge Ram Sevak, Deputy Ram 4 The history of Delhi Police, http://www.delhipolice.nic.in/mobile/history.html, accessed Thursday, October 15, 2020. 5 All names have been changed to protect the identity of the officers. The names of police stations have either been anonymised or changed to make sure no identifying

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Manohar, Constable Rohit, Constable Moti Ram and Constable Punit. Ram Manohar explained to me that the team started working on creating hotspot maps when the recent survey maps of Delhi were not even available and since 2008, the geography of the city had changed so much that the old ones had become useless. He said that people had started quoting pillar numbers of metro stations as landmarks for their addresses and there was no way to catch such information on their maps. Ram Manohar explained the long and tedious journey of mapping crime in Delhi since 2009, when they began with a citywide survey to create a usable survey map. Currently, the input data for such mapping comes from the Dial 100 call centre which receives around 5000 to 10,000 calls daily. Every day data is recorded in a “daily diary” or “green diary” and then plotted on the map of Delhi using the ArcGIS software manufactured by the Environmental Systems Research Organisation (ESRI). Since the call centre receives a number of bogus calls per day, the data is only used after being verified (cleaned) at 12:00 AM every day. After being verified, it is recorded in the daily diary, which is given to the mapping team. Constable Rohit is responsible for plotting the crimes on the map. Instituted in 2015, CMAPS, housed in Command, Control, Coordination, Communication Integration (C4i) at the Delhi Police HQ is a latest entrant in the mapping arena in the Headquarters. ISRO has developed the software used by CMAPS. Hotspot analysis, where crime data is plotted on a GIS map to analyse areas of low and high crime density, is done using CMAPS. Resources are then distributed keeping in mind areas where the crime density is high. As Ram Manohar told me, this is a duplication of effort as the same job could have been done via the already existing Digital Mapping Division or DMD; however the latter suffers as a result of lack of funds. When asked, he agreed that CMAPS definitely worked faster.

information regarding cases in Delhi Police Headquarters can be deciphered. For example, if I quote an incident from the HQ records, I have either anonymised the name, such as changed Vasant Kunj Police Station to “a police station in South Delhi” or given it a different name such as Kashmere Gate/Saket. This, so that anyone reading the case description is not able to connect it to the actual police station under jurisdiction of which the incident occurred.

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From Sociological Understanding of crime and Police Work Emile Durkheim, in his study of Division of Labour in Society (1933), describes crime as something that offends the collective consciousness of society. Durkheim developed an extensive theory of social solidarity in the Division of Labour in society where he equated following the rule of law as maintenance of social solidarity, implying that crime was a negation of solidarity and a criminal was someone who sought to live at the expense of society. Durkheim laid the responsibility of defining crime on the shoulders of society. According to Durkheim, crime was not an inherent quality of certain kinds of actions but rather a result of the process of social definition. In Rules of Sociological Method, Durkheim (1982) again claims that what confers the quality of crime upon an action is not intrinsic quality of the action but the definition which the society (collective conscience gives it). Further, Durkheim claims that criminal acts are not merely those acts which offend the collective conscience of the society, but also those acts which the society is able to repress. A society with a stronger collective conscience is also more sensitive to even the smallest of infractions. Therefore, such a society would label even the smallest misgivings as criminal (1933). Hilbert (1989) also argues that Durkheim sees deviance (crime) as an outcome of prevailing social regulations rather than indicators of peculiar individual traits of people. Durkheim saw crime as a normal and essential aspect of how the society is self-regulated. Hilbert (ibid., 243) states, For Durkheim, crime is not only normal but healthy, for it is a primary means the sui generis society has for protecting itself against the withering away of the collective conscience, which is its own withering… It is through the ritualized recruitment of criminal behaviour from the fringe and through its ritualized punishment that the collective conscience is recognized, reaffirmed, and celebrated.

In other words, when we look at Durkheim, crime is seen as something that offends the social consciousness of the people, breaking the solidarity they share. This solidarity is represented in law and breaking the law is seen therefore as one form of an act of crime. Most acts, which are seen as crimes, then refer to only such acts that overtly offend the people’s conscience. Therefore, “normal” crime is considered to be property crimes or murder, assault, arson, burglary, rape and theft. These crimes are in the public domain, are reported and policed and judged.

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However, as Cathy O’Neil (2016) argues, crimes such as finance crimes (such as those in the time of the 2008 meltdown) are never reported and never policed. People who committed these crimes would never be called or considered “criminal”, even though they inflicted equal, if not less or more, pain in society than the above-mentioned crimes. Tim May (2001) also argues that crimes such as sexual harassment at workplace or other white-collar crimes are never reported and perpetrators of such crimes are mostly never called criminals. Concocted evidence too creates criminals according to the whims and fancies of police officers. Bayley (1969), for instance, says that police officers in India concoct evidence in order to fill the gaps in the chain of evidence they have to fulfil the requirements of the law. They also introduce false evidence to strengthen a “weak” case. This is done to increase the number of convictions in cases assigned to the police officers, as a mark of their success. High standards of evidence desired by the court and low resources to actually procure them leads officers to do whatever and whenever they can (Mehra and Levy 2010). Looking at what we now know about data creation, algorithms and how the social influences the technological, my research objectives become clear. The research that fuels the narrative in this book attempts to understand the processes of data collection and analysis in the Delhi Police Headquarters, while examining the functioning of the organisation (Delhi Police) in order to investigate how the social and technological come together to construct a figure of the “criminal” in policing.

Thus, Ethnography I realised that I could achieve this layered understanding using the methodological approach of ethnography as defined by Nader (2011, 211) as a “theory of description”. An immersive approach helped me unravel the various strands that come together to weave the fabric of predictive policing in Delhi. Marda and Narayan (2021) discuss the methodological challenges a researcher would face while researching algorithmic systems in India. One of the major challenges is the lack of sophistication of such projects. India is an example of a developing country that routinely deploys technologies developed in the West (in tune with their conditions), but is otherwise quite dissimilar in terms of its basic infrastructure or even human resources. In the USA, from where India has adopted the predictive policing system, police stations work with

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much advanced levels of data. As a result, location tracking and mapping do not pose a problem. New Delhi, which is the capital city of India, has only approximately 600,000 citizen addresses recorded for a population of 20 million people. Infrastructural readiness, along with resources and training also indicate how India is not ready to work with a predictive policing system but the latter has still been adopted for policing needs in India. The reasons could be manifold, such as a political push to present a strong stance against crime in a city that is commonly known as the crime capital of India. There might be a very genuine fear of missing out considering the technology has been marketed as the panacea for the “crime problem” across the world. The need to appease voters that the government is not shy of doing anything to tackle the crime issue might also be a reason to adopt technologies that have no background of working in India. This kind of situation, where lack of infrastructural readiness meets bureaucratic exigencies, makes it very important for a researcher to study how the institution works and adds its own layer of subjectivity in the working of an algorithm. Another reason for adopting ethnography, which is not at all unique to India, is the process of production of crime data that would be fed to create the predictive policing algorithms. Tim May (2001) suggests that a lot of the production of criminal statistics depends on “detection, definition of crime and police practices” (p. 77). The decision of police officers in recording the incident and taking it seriously (after and if, it is reported), and the institutional practices of the police in tackling certain offences, according to May, affects the compilation of crime statistics. What a police officer decides to do will depend not only on the circumstances of the incident and how sympathetic or otherwise the officer maybe towards the person(s) or act(s) itself, but also the organisational policies that the officer is instructed to follow and the culture of the police organisation itself (ibid.). May thus concludes that rather than taking crime statistics at their face value, one should examine police cultures and their interpretative practices to understand how statistics are compiled. “In focussing upon the organisational culture of the police we are beginning to see that ‘criminal facts’ do not simply speak for themselves, but possibly tell us more about organisational practices and power relations within society” (ibid.). Earlier in the Introduction, I have discussed the scholarship on the lack of authenticity of crime records and their close nexus with police attitudes and power imbalances. The present research received its impetus to

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study the institution ethnographically from all these factors, to be able to understand the prejudices of the system that ultimately gets reflected in the algorithmic output. The process of digital analytics of data is not a straightforward case of procedures turning into rational actions. There are many negotiations involved in the way the analysis centres are set up and the way data is used (or cast aside) for analysis. Thus, the chosen method for this research is ethnography. Studying the police institution as a social system helped me understand how police officers (working as call takers and analysts) negotiate with the bureaucratic rules of their organisation to record and analyse crime data and produce crime analysis. An interpretative understanding approach, or Verstehen, as espoused by Weber (1978) of the human reason, which is beyond being codified and explained in procedures and official documents, is helpful in understanding why data is classified a specific way or why certain data is not included in the analysis Predictive Policing is a system of people and code working together to produce results. As mentioned before, Seaver (2019) and Barabas (2019) insist on studying the institutional system that employs/creates these algorithms rather than just the algorithms per se in order to understand how algorithms work. The institution and social norms are responsible for informing the system (literally), and for encoding parameters that would help it to make decisions. Barabas’s article on “studying up” institutions also encourages one to look at institutions and the seats of power that decide the small details of how algorithmic systems are deployed. Keeping this in mind, it must be said at the outset that the police headquarters in New Delhi largely represents an upper caste but low economic class strata of society (especially in the lower rungs). Barring the Indian Police Service (IPS) officers who make up the ranks of deputy commissioners and above, the vast majority of the police force in New Delhi comes from the neighbouring state of Haryana, many of whom belong to the caste of landed farmers who are looking for a “respectable” job in the police. According to some lower rung officers (of the rank of constables), a respectable job in the police would ensure a higher social standing in their respective caste communities than by following their ancestral professions, which would also ensure better marriage prospects for them. A constable-level officer in the Digital Mapping Division told me that his family has enough property to support themselves but a sarkari (public, in service of the state) job, especially in the police, increases his social standing (Marda and Narayan 2020).

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Delhi Police stands at the 11th rank for diversity among the 21 states studied in India (Status of Policing in India Report 2018). As per the Status of Policing Report 2018, the composition of the police force is largely upper caste at the lower rungs where officers are not hired according to constitutional rules of affirmative action of the depressed classes,6 viz. Scheduled Castes or SC (erstwhile ‘untouchable’ castes and still the most marginalised section of the population in India), Scheduled Tribes or ST (indigenous tribes who have been granted protection by the government) and Other Backward Classes or OBC. The representation of Muslims is also low because, ineffective as it is at this level, there is no affirmative action for Muslims for hiring in government services. Without the most marginalised sections represented in the police, it is evident that social imaginaries of caste remain unchecked among the officers. An ethnographic study of the crime mapping Delhi Police would therefore help us unpack (a) the dominant worldview they represent and (b) their dominant worldview being exclusionary to the many people who would ultimately be the beneficiaries of the maps they would design and work on. As Selbst et al. (2019) argue, sometimes even when designers are not looking to be biased, redundant parameters can encode bias without the designer realising it. This is because some features can act as “proxies” for other openly biased features. For example, in a segregated city like Delhi, the location of residence can easily be used as a proxy for caste and class. Therefore even when an algorithm is not actively using the category of caste, it could still creep in with the help of information on the location of residence of the people. Seaver (2019) says that when the object of interest is an algorithmic system (rather than just the algorithm), “cultural details become technical details”. This is in opposition to the “studying the social as the context” trope7 that has become prominent in the study of technological systems. 6 No rules of affirmative action followed in hiring in police, https://caravanmagazine. in/caste/principles-of-reservation-and-diversity-do-not-matter-while-recruiting-state-pol ice-personnel, accessed Tuesday, September 1, 2020. 7 According to Seaver (2019), when researchers study the social systems as a background/context to an algorithmic system, they do not take into account how the social influences the algorithm at a technical level. Such an understanding merely implies that the social influences the deployment or working of the algorithm but does not say how. When one argues that the social influences the algorithm at a technical level, they mean that these social details are encoded at the data or at the modelling level. For example, in crime mapping algorithms, socio-economic factors are encoded as the cause of criminal

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According to Seaver, this latter viewpoint projects algorithms as inherently “objective” and as has already been demonstrated, that is simply not true. Looking at material infrastructure and its politics was also an important aspect of my study. In other words, I argue an old computer monitor does not just slow down the system; it has profound things to say about what the organisation desires to fund, and therefore the politics of technological advancement in police, as we will see in Chapter 2.

Fieldwork Diaries (a Discussion on Methods) For my research, I visited the Delhi Police HQ on almost a weekly basis for a period of roughly two years, from 2017 till the end of 2018. After late 2018, my interactions continued but mainly via phone calls and text messages. I visited the HQ once in 2019 again when I spent considerable time in the DMD. However, after this my access was severely limited as the senior police officials became increasingly anxious about the impending general elections and the possibilities of harm to the ruling party’s image if a researcher would publish information that would embarrass or incriminate the police, as the Public Relations Officer of the Delhi Police explained to me while denying permission to access the HQ. During my visits over 2017 and 2018, I maintained a researcher’s ethics with regard to all my interactions (the thesis was approved by the ethics committee at JNU). I did my entire note-taking after the visits were over, outside the HQ or during my bus rides home, as I was prohibited to carry notebooks in, especially in the Dial 100 call centre, where the emergency calls were being attended to. If I did end up writing down anything, it was with regard to the standard procedures and working of the DMD which was mostly information available in documents publicly posted on the walls. I did not electronically record any interaction as that was prohibited as well. All the names, designations, case note details and dates have been changed or anonymised so that they cannot incriminate anyone in the HQ who has been kind enough to provide information to me regarding the functioning of the organisation. I also took care to do this because case notes from the HQ are regularly produced as evidence in court and hence cannot be made public. All case details or location details

behaviour. This is done right at the modelling level which means that the algorithm is technically designed to see poor people as criminals.

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are strictly for demonstration purposes; I have changed the identifying details in all of them. All my visits at the HQ were as per the permission provided to me by the then DCP of the Communications Department. The visits were logged in the reception database from where I was given permission slips to visit the department. These slips were only given to me after an officer in the department confirmed that I was indeed allowed to visit. During my interviews with the call takers in the Dial 100 call centre, apart from understanding their method of collecting and storing data for future use, I tried to understand how they manage everyday calls to the emergency number of the police. The interviews gave me an insight into how the call takers are trained to use the tools of data collection (such as the automated electronic form where they store the call data). I conducted another set of interviews with constables in the control centre (who have been technically trained) in charge of cleaning and managing this data and making it ready for analysis. Lastly, I interviewed the data analysts to find out the practices of analysis and data mapping. These interviews helped me understand how the various tools and techniques of the DMD, including algorithms, are used by the Delhi Police in their daily work. During one of these interviews, the head of the DMD also discussed his daily negotiations with the Police bureaucracy in running the centre. I wanted to conduct semi-structured interviews with the team at ADRIN to understand the design of the CMAPS algorithm. However, I could not secure an appointment with them. This is not a new problem for researchers, both technologists and social scientists venturing to study algorithms/algorithmic systems. Algorithms are black boxes where access is given to a few personnel in the closed system only. Even designers, in this case ISRO/ADRIN, do not want to disclose the design secrets of their algorithms for fear of being copied or gamed. As a workaround, I tried to understand the working of CMAPS by looking at the ARCGIS mapping software used in the Digital Mapping Division, as they are pretty similar in design. A considerable volume of literature is available on hotspot mapping which also helped me in understanding how CMAPS functioned.

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Mapping the Book In the chapter that follows, I take the reader to the DMD of the Delhi Police which is where it all began. In 2007, the DMD laid the grounds for crime mapping in Delhi by surveying the city and setting up its basic information infrastructure that would be the foundation for all the future mapping endeavours to come. Chapter 3, which I have titled the Green Diary after the document in which information used to plot crime on the Delhi map is gathered on a daily basis, explains the myriad processes of data collection and processing that are undertaken by the Delhi Police to create the daily crime database of the city. This is the chapter that introduces the reader to the not-so-scientific workarounds that are part of data production in the organisation. I finally bring the concerns that emerge in the two chapters, that is of infrastructure and practice of data collection, together in a final chapter where I explain the making of crime hot spot maps in Delhi, the background processes that bring them into being and their contribution to the construction of the ‘criminal’ in Delhi. This chapter takes the technological artefact of the crime hotspot map, considered to be an objective and neutral presentation of the realities on the ground, and explains how it is a result of human arbitrations and deliberations. The ‘criminal’, thus is a product of our imagination, given a solid backing by technology. Chapter 5 is the Epilogue and ties the loose ends of the books, as there may be, even after all my attempts to carry all the strands of all the arguments with me together. I have titled it as an epilogue because a book on predictive policing can never have a conclusion, only a ‘map’ of the road ahead.

References Ambedkar, B. R. 2014. Dr. Babasaheb Ambedkar: Writings and Speeches Vol. 16. Edited by Vasant Moon. New Delhi: Dr. Ambedkar Foundation. Amoore, Louise, and Marieke De Goede. 2005. ‘Governance, Risk and Dataveillance in the War on Terror’. Crime, Law and Social Change 43 (2–3): 149–73. https://doi.org/10.1007/s10611-005-1717-8. Anderson, David, and David Killingray, eds. 1991. Policing the Empire: Government, Authority, and Control, 1830–1940. Studies in Imperialism. Manchester, UK; New York: New York: Manchester University Press; Distributed exclusively in the USA and Canada by St. Martin’s Press.

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Barabas, Chelsea. 2019. ‘Beyond Bias: Re-Imagining the Terms of “Ethical AI” in Criminal Law’. SSRN Scholarly Paper ID 3377921. Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.3377921. Bayley, David H. 1969. Police and Political Development in India. Princeton University Press. http://www.jstor.org/stable/j.ctt183pj28. Bennett Moses, Lyria, and Janet Chan. 2018. ‘Algorithmic Prediction in Policing: Assumptions, Evaluation, and Accountability’. Policing and Society 28 (7): 806–22. https://doi.org/10.1080/10439463.2016.1253695. Brayne, Sarah. 2021. Predict and Surveil: Data, Discretion, and the Future of Policing. Byrne, James, and Gary Marx. 2011. ‘Technological Innovations in Crime Prevention and Policing. A Review of the Research on Implementation and Impact’. Journal of Police Studies 3: 17. Chainey, S., and J. Ratcliffe. 2005. GIS and Crime Mapping. Chichester: Wiley. Coleman, Roy, and Michael McCahill. 2011. ‘Surveillance & Crime’. In Surveillance & Crime, 111–42. London: SAGE Publications Ltd. https://doi.org/ 10.4135/9781446251379. Costello, Barbara J., and John H. Laub. 2020. ‘Social Control Theory: The Legacy of Travis Hirschi’s Causes of Delinquency’. Annual Review of Criminology 3 (1): 21–41. https://doi.org/10.1146/annurev-criminol-011419041527. Crampton, Jeremy. 2014. ‘Collect It All: National Security, Big Data and Governance’. SSRN Scholarly Paper. Rochester, NY. https://doi.org/10.2139/ ssrn.2500221. Doleac, Jennifer L. 2017. ‘The Effects of DNA Databases on Crime’. American Economic Journal: Applied Economics 9 (1): 165–201. https://doi.org/10. 1257/app.20150043. Durkheim, Émile. 1933. Division of Labour in Society. Translated by George Simpson. New York: Macmillan. ———. 1982. The Rules of Sociological Method. Edited by Steven Lukes. Translated by W. D. Halls. First American edition. New York: The Free Press. Egbert, Simon, and Matthias Leese. 2020. Criminal Futures: Predictive Policing and Everyday Police Work. 1st ed. London: Routledge. Emsley, Clive, ed. 2017. Theories and Origins of the Modern Police. History of Policing. Abingdon, Oxon: Routledge. https://search.ebscohost.com/login. aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1551279. ———. 2018. Crime and Society in Twentieth Century England. 1st ed. Boca Raton, FL: Routledge. https://www.taylorfrancis.com/books/e/978131786 4417. Ericson, Richard V., and Clifford D. Shearing. 1986. ‘The Scientification of Police Work’. In The Knowledge Society: The Growing Impact of Scientific Knowledge on Social Relations, edited by Gernot Böhme and Nico Stehr,

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129–59. Dordrecht: Springer Netherlands. https://doi.org/10.1007/97894-009-4724-5_9. Eubanks, Virginia. 2019. Automating Inequality: How Hight-Tech Tools Profile, Police, and Punish the Poor. Ferguson, Andrew Guthrie. 2017. The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. NYU Press. https://doi.org/10. 2307/j.ctt1pwtb27. Ghertner, D. Asher. 2012. ‘Nuisance Talk and the Propriety of Property: Middle Class Discourses of a Slum-Free Delhi’. Antipode 44 (4): 1161–87. https:// doi.org/10.1111/j.1467-8330.2011.00956.x. ———. 2015. Rule by Aesthetics: World-Class City Making in Delhi. https:// www.vlebooks.com/vleweb/product/openreader?id=none&isbn=978019938 5584. Harcourt, Bernard. 2010. ‘Risk as a Proxy for Race’. University of Chicago Law & Economics Olin Working Paper 535 (September). Heidensohn, Frances. 1989. Crime and Society. London: Macmillan Education UK. https://doi.org/10.1007/978-1-349-19763-7. Hilbert, Richard A. 1989. ‘Durkheim and Merton on Anomie: An Unexplored Contrast and Its Derivatives*’. Social Problems 36 (3): 242–50. https://doi. org/10.2307/800693. Innes, Martin, Nigel Fielding, and Nina Cope. 2005. ‘“THE APPLIANCE OF SCIENCE?”: The Theory and Practice of Crime Intelligence Analysis’. The British Journal of Criminology 45 (1): 39–57. Introna, Lucas D. 2016. ‘Algorithms, Governance, and Governmentality: On Governing Academic Writing’. Science, Technology, & Human Values 41 (1): 17–49. Jauregui, Beatrice. 2016. Provisional Authority: Police, Order, and Security in India. Chicago, IL: University of Chicago Press. https://press.uchicago.edu/ ucp/books/book/chicago/P/bo24835322.html. Jefferson, Brian Jordan. 2020. Digitize and Punish: Racial Criminalization in the Digital Age. Minneapolis: University of Minnesota Press. Karuppannan. Jaishankar, S. Shanmugapriya, and V. Balamurugan. 2004. ‘Crime Mapping in India: A GIS Implementation in Chennai City Policing’. Geographic Information Sciences 10 (June): 20–34. https://doi.org/10. 1080/10824000409480651. Khanikar, Santana. 2018. State, Violence and Legitimacy in India. New Delhi: Oxford University Press. Kidambi, Prashant. 2004. ‘“The Ultimate Masters of the City”: Police, Public Order and the Poor in Colonial Bombay, c. 1893–1914’. Crime, Histoire & Sociétés / Crime, History & Societies 8 (1): 27–47. https://doi.org/10.4000/ chs.513.

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Kovacs, Anja. 2020. ‘When Our Bodies Become Data, Where Does That Leave Us?’ Medium. 8 June 2020. https://deepdives.in/when-our-bodies-becomedata-where-does-that-leave-us-906674f6a969. Knorr-Cetina, Karin D. 1981. The Manufacture of Knowledge: An Essay on the Constructivist and Contextual Nature of Science. https://nbn-resolving.org/ urn:nbn:de:bsz:352-opus-83790. Marda, Vidushi, and Shivangi Narayan. 2020. ‘Data in New Delhi’s Predictive Policing System’. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 317–24. FAT* ’20. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3351095.3372865. ———. 2021. ‘On the Importance of Ethnographic Methods in AI Research’. Nature Machine Intelligence 3 (3): 187–89. https://doi.org/10.1038/s42 256-021-00323-0. Manning, Peter K. 2008. The Technology of Policing: Crime Mapping, Information Technology, and the Rationality of Crime Control. New York and London: New York University Press. May, Tim. 2001. Social Research: Issues, Methods and Process. 2nd ed. Buckingham; Philadelphia: Open University Press. Mehra, Ajay K., and Réne Levy. 2010. The Police, State and Society: Perspectives from India and France. Delhi; Chennai; Chandigarh: Pearson. Merton, Robert K. 1938. ‘Social Structure and Anomie’. American Sociological Review 3 (5): 672–82. https://doi.org/10.2307/2084686. Mukhopadhyay, Surajit Chandra. 1997. Conceptualising Post-Colonial Policing: An Analysis and Application of Policing Public Order in India. UK: University of Leicester (United Kingdom). Nader, Laura. 2011. ‘Ethnography as Theory’. HAU: Journal of Ethnographic Theory 1 (1): 211–19. https://doi.org/10.14318/hau1.1.008. Nandi, Sugata. 2016. ‘Respectable Anxiety, Plebeian Criminality: Politics of the Goondas Act (1923) of Colonial Calcutta’. Crime, Histoire & Sociétés / Crime, History & Societies 20 (2): 77–99. ———. 2019. ‘Goondas of Calcutta: Crimes and Policing in Colonial India’. In Routledge Handbook of South Asian Criminology. Routledge. Narayan, Shivangi. 2021. ‘Guilty Until Proven Guilty: Policing Caste Through Preventive Policing Registers in India’. Journal of Extreme Anthropology 5 (1). https://doi.org/10.5617/jea.8797. Neff, Gina, Anissa Tanweer, Brittany Fiore-Gartland, and Laura Osburn. 2017. ‘Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science’. Big Data 5 (2): 85–97. https://doi.org/10. 1089/big.2016.0050. Neocleous, Mark. 2000. The Fabrication of Social Order: A Critical Theory of Police Power. London; Sterling, VA: Pluto Press.

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Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. Illustrated ed. New York: New York University Press. O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. USA: Crown Publishing Group. Parveen, Razdha. 2017. ‘Policing in India: Technology and Crime Prevention’. Social Science and Humanities Journal July: 132–43. Prasad, Kislaya. 2013. ‘A Comparison of Victim-Reported and Police-Recorded Crime in India’. Economic and Political Weekly 48 (33): 47–53. Raghavan, R. K. 2003. ‘The Indian Police: Problems and Prospects’. Publius 33 (4): 119–33. Ratcliffe, Jerry H. 2016. Intelligence-Led Policing. 2nd ed. London: Routledge. https://doi.org/10.4324/9781315717579. Rouvroy, Antoinette, and Thomas Berns. 2013. ‘Gouvernementalité algorithmique et perspectives d’émancipation. Le disparate comme condition d’individuation par la relation?’ Réseaux 177 (1): 163–96. https://doi.org/10. 3917/res.177.0163. Seaver, Nick. 2019. ‘Knowing Algorithms’. In Digital STS: A Field Guide for Science and Technology Studies, edited by Janet Vertesi and David Ribes, 412– 22. Princeton: Princeton University Press. Selbst, Andrew D., Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. ‘Fairness and Abstraction in Sociotechnical Systems’. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. FAT* ’19. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598. Sengoopta, Chandak. 2003. Imprint of the Raj: How Fingerprinting Was Born in Colonial India. London: Macmillan. Singha, Radhika. 1998. A Despotism of Law: Crime and Justice in Early Colonial India. Oxford University Press. ———. 2015. ‘Punished by Surveillance: Policing “Dangerousness” in Colonial India, 1872–1918’. Modern Asian Studies 49 (2): 241–69. https://doi.org/ 10.1017/S0026749X13000462. ‘Status of Policing in India: A Study of Performance and Perception’. 2018, 2019. Report. New Delhi: Common Cause. Stenson, Kevin, and Robert R. Sullivan. 2012. Crime, Risk, Justice. Hoboken: Taylor and Francis. http://grail.eblib.com.au/patron/FullRecord.aspx?p= 449613. Sutherland, Edwin H., and C. C. Van Vechten. 1934. ‘The Reliability of Criminal Statistics’. Journal of Criminal Law and Criminology (1931–1951) 25 (1): 10. https://doi.org/10.2307/1135675. Weber, Max. 1978. Economy and Society: An Outline of Interpretive Sociology. University of California Press.

CHAPTER 2

Setting Up of the Digital Mapping Division

Talking about the initial days of the Digital Mapping Division (DMD) always elicited a sense of nostalgia among the officers of the division when I spoke to them. They reminisce about the large amounts of funds always available at the time to survey the city and collect information about its geography. “We always had a car and an extra person for those field trips”, Ram Manohar told me with a kind of a happy-sad face you associate with a sense of “things having gone downhill from there”. Today, he said, he has major plans for making DMD maps better but there is no money to execute them. He threw a long notebook on the table before him. “This alone has crime data for almost ten years. For a crime analyst, this is gold. But who cares”, he said. The setting up of a crime mapping system by the Delhi Police might look like a simple case of collecting and ordering information to form the basis of mapping. However, the story is full of details of bureaucratic omissions and commissions. It is also a commentary on technology projects in India, most of which start with much fanfare but are given up for seemingly shinier alternatives, just like the DMD was for the later, more automated system of CMAPS. A crime mapping division in any police organisation needs, most importantly, a department to collect data and another to analyse it; in the Delhi Police though, one often merges with the other. The general policy

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2_2

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across police organisations is that the more data the better, because more information can help in finding offenders more quickly, saving time and resources of the police. Chainey and Ratcliffe (2005) outline five stages in setting up a crime mapping centre: direction (where the structure of the mapping centre is decided); collation (collection of data which could be mapped and on which analysis could be done); analysis (analysing data to investigate crimes); dissemination (providing results of analysed data to other police departments and to the public); and feedback which entails reactions and performance updates from people who use the maps in their daily lives. However, we will concentrate on the first four steps in this chapter because currently, there is no mechanism for feedback in the Delhi Police crime mapping system. Collation is the basis of any crime mapping department. For analysing data on crime, the police need prior data, also called base data or historical data that could include everything from people’s shopping habits to their movie preferences. For law enforcement organisations, all data is required data, which can help identify criminals with more certainty and clarity. When DMD came into being, the emphasis was on getting the geography of Delhi right on the map. Later, the department went on to become a one-stop-shop for all big and small mapping- and data-related activities of the Delhi Police. In this chapter, apart from discussing the DMD’s job of collating, processing and recording data, I will also discuss the very material realities of the technological systems (hardware) it uses; these are often overlooked in order to give importance to the softwarerelated functions of technological systems. The material realities, i.e., the infrastructure is simply presumed to be there, acquired and plugged in to do whatever the human user wants it to do even when it is clear that the acquiring and plugging-in and using have a politics of their own. To elucidate further, Bowker and Star (2000) have shown in their work, for instance, that infrastructure does not work in a vacuum and has social underpinnings of its own, which also informs the final outputs of any classification scheme (in our case, this happens to be crime mapping). I am reminded of Langdon Winner’s essay ‘Do Artifacts Have Politics’ (Winner 1980) when I see the quality of infrastructure that is available to DMD as opposed to what is provided to CMAPS. It helps me understand how seemingly innocuous bureaucratic decisions in the police can indeed be political as they favour one department over the other, thus, in effect, favouring one kind of policing over the other. The neglect of DMD versus the pomp and show of CMAPS with several news articles in

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leading dailies announcing the launch of the latter programme,1 makes this evident. Even though, as will be shown later in this chapter, the maps made by the DMD are more accurate in terms of location and details of crime.2 To me this is a clear indication that policing work is also about managing how the police, and hence the government, is being perceived in the media, and by the general public, and not just about tackling crime (as is assumed). But first, let me take the reader back to what led to the inception of mapping in the Delhi Police.

Inception of the Crime Mapping Centre The push to use maps to “solve” crime came quite arbitrarily in the Delhi Police. In 2004–2005, The Digital Mapping Division was already being set up to use maps to visualise and analyse available crime data. As mentioned earlier, at this time, a number of background processes were in motion, such as the surveying of the city to capture the changes in its geography and marking of police jurisdiction boundaries. With the rise of the “carjacking” or car theft events in the city in the year 2006–2007, senior officers decided that they could use maps for the investigation. This set off a chain of events, as I will elaborate below, which led to the setting up of a manual mapping centre in the Delhi Police Headquarters. After this manual system, called Digital Mapping Division or DMD, was up and running, an automatic system was brought in called the CMAPS. Though the automatic system was supposed to phase out the manual system, both continued to run simultaneously in the Delhi Police HQ. 1 Preventing Crime before it happens, https://www.hindustantimes.com/delhi/delhipolice-is-using-precrime-data-analysis-to-send-its-men-to-likely-trouble-spots/story/hZc CRyWMVoNSsRhnBNgOHI.html, accessed Saturday, October 24, 2020. Also see, https://economictimes.indiatimes.com/news/politics-and-nation/delhi-police-to-usespace-tech-for-crime-control/articleshow/50887300.cms?from=mdr, accessed Saturday, October 24, 2020. 2 As will be explained as the book progresses, mapping in DMD is done with data brought from the ground by the PCR vans. Hence, their location data and crime data are more accurate than CMAPS where mapping is based on just the data collected by call takers of the emergency calls on the number 100. This location information is not accurate as the address database on which the call centre works on is not available for the entire population of Delhi. Also, a number of callers would not know their standard addresses even if they were available as they are used to identifying their homes by their local landmarks. A number of such callers would be women who would not be aware of their addresses because of being confined to their homes for most part of their lives.

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Setting the Direction for the DMD At its inception in 2004, DMD was meant to handle all data/data analysis-related initiatives of the Delhi Police, such as restructuring the district boundaries of the police stations according to the latest population and crime surveys. The work undertaken by the Digital Mapping Division was to provide a form of legibility or state simplification exercises, as James C. Scott (1998), would call it. In other words, it was making the area under study, i.e., the capital city of Delhi “legible” or “simplified” so that it could be used for mathematical calculations, in order to analyse the available crime data and other kinds of data retrieved during policing. Scott explains making an area legible by alluding to a wild forest versus a curated one. In curated forests, gardeners would know exactly where a certain species of tree was growing, its population, fertility and yield. This would allow them to make changes in the forest (as a whole) or in the trees according to requirements of yield or any other variable. This is not possible in a wild forest owing to the lack of information about its flora and fauna. What we see here is another version of the “bio power” which explains how life was made calculable in order to be “governed” (Foucault 2020). Thus DMD was tasked with converting Delhi into a map-able grid by uncovering its geographic and social information. The most important aspect of this exercise was obtaining the address information for “geocoding” crime data, because mapping (and thereafter spatial analysis) is only possible if the data to be spatially studied is put into the correct format of latitude–longitude coordinates (called geocoding). Afterwards, information such as the district boundaries, and other landmarks makes it even easier to plot data on a map (I will discuss these processes in greater detail in the Collation section later in this chapter). As of 2019, the Delhi Police has a new address for its headquarters and I am sure the mapping department has its own place in it. The DMD I remember at its old address at ITO, New Delhi was housed within the Central Police Control Room (CPCR), on the third floor of the nine floor Delhi Police Headquarters, headed by a Deputy Commissioner of Police (DCP) for Communications. Apart from the DMD, on the third floor, one could find offices of the DCP, 12 specialised helpline desks of the Delhi Police, and the Dial 100 call centre with its command centre. The CPCR extended on the fourth floor where the CMAPS was housed in the very swanky, newly constructed and adequately acronymed ‘C4i’ which

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stood for Command, Control, Communication, Computing and Information. CMAPS has been designed specially by ADRIN, part of ISRO for Delhi Police. When I visited the space, it felt so swanky that it was almost deserted, which reminded me of middle-class homes in India where the “drawing room” was always the neatest, cleanest and loneliest spaces in the house. C4i felt like it was made especially for guests. The Dispatch Floor, responsible for sending police staff to the location of the call, its Command Centre,3 a Public Address (PA) system, traffic management systems, and feedback centres (for collecting public opinion on the quality of emergency response in Delhi) were also located on the fourth floor but they were nowhere near as swanky as the C4i. On the contrary, officers on the Dispatch floor told me how they have to make do with broken or missing equipment and a reduced workforce on the floor every day. The DMD itself at that time was housed in a small rectangular room, which appeared to me to resemble more of an alley than a room. There were work stations for 5–6 team members here, of whom three were permanent, Ram Sevak, Ram Manohar and Bhola Ram, and three were “shifting resources”—Moti Ram, Punit and Rohit—as per the norms of transfer of the Police Department. The room also contained a set of four archaic computers and a newly acquired industry-sized colour printer. The printer was a prized possession of the division because they were the only ones who printed documents worthy enough to warrant possessing a colour printer. That did not stop other departments from using the printer, however. The head of the DMD, Ram Sevak, maintained a meticulous log of everyone who used the printer so that he could explain the use of its cartridges when applying for a new one. Though the printer had been acquired because the daily crime maps produced in DMD were supposed to be secret security documents and not open to the public, I was also told that before the printer was acquired in late 2018, they were printed in a local shop in ITO. In effect, these so-called high-security documents were carried in an ordinary flash drive, to be printed in the neighbourhood cyber cafe/printing shop.

3 Command centres are supervisory centres of the Call Centre and of the Dispatch Floor that oversee their working. The Command Centre of the Dispatch floor was also exclusively in charge of updating the comprehensive document of crimes to be mapped in the past 24 hours. This was called the “Green Diary”, which we will discuss in detail in the next chapter.

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There was a permanent stench of urine in the air on this floor from a toilet (for women) at the reception, next to a small changing room meant for women police officers. The changing room was hot and unventilated with a few clothes hurriedly strewn across the room or stuffed into lockers. Small bags and purses hung on nails. Though I found myself gasping for air in this room, the women officers seemed quite comfortable there, some of them even bringing in their cups of tea to enjoy there with a little bit of casual conversation. This was hardly surprising, however, as this was a small private space for women in an otherwise overtly masculine space.

Crime Mapping Beginnings As mentioned in the introductory section to this chapter, in 2006–2007, when the team in DMD was busy in legibility exercises of the city, the DCP of a certain district decided to handle the spiking carjacking menace in the city with the help of maps. A whole new data collection exercise ensued because information on carjacking was not readily available in the Police Headquarters. The Station Head Officers (SHOs) from all the police stations in Delhi were summoned to the HQ to provide detailed reports on carjacking events in their areas. As per vague estimates, carjacking events did see a considerable decline after mapping, but when I spoke to them, no one in the DMD could say for sure if it was because of the mapping. All the same, based on the success of this endeavour, senior officers at the Police HQ decided that snatching (men on motorbikes take away items such as wallets, bags, jewellery, by force from people) and robbery could be mapped too followed by rape and “eve teasing”.4 These crimes were selected for mapping in the initial days in an arbitrary manner, based on what the higher officials in the headquarters thought to be the biggest threat to law and order in the city. Four crimes made it to the final list of heinous crimes to be mapped in Delhi: rape, robbery, snatching and eve teasing. It is important to remember that at this point, 4 Eve teasing is a euphemism for very public sexual harassment faced by women in India. This could entail cat calling, groping, abusive gestures and even assault. However, by clubbing all of these into a harmless sounding phrase indicating mere teasing, and by alluding to Eve, a mythical woman who seduces Adam, this phrases not only trivialises sexual harassment, but blames the woman for it at the same time for being proactive. For more details see Pratiksha Baxi, ‘Sexual Harassment’, https://www.india-seminar.com/ 2001/505/505%20pratiksha%20baxi.htm, accessed June 7, 2022.

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crime mapping was being done mainly to visualise the daily distribution of crime rather than for any specific long-term goal of crime prevention or detection. Over the years, an SOP, or standard operating procedures, was developed for crime hotspot mapping. The SOP, hanging on the wall of the DMD, stated that incidents of crimes for the above four categories were plotted “showing place of incident for prevention, detection, planning and research purposes from various angles by district commissioners of police (DCPs)”. The copy of the map, it said, would also be sent to the Commissioner of Police Delhi, Special CP, Special Units, Special CP for Law and Order (L&O) and Joint CPs of all ranges and all the DCPs. The SOP also stated that the DMD would undertake the manual mapping of the crime data till the induction of the automatic mapping system of CMAPS. On my multiple visits there, I could not find anyone who could comment on the research output of the maps. Mostly, the maps were used for daily bandobast,5 said the officers. On the same wall of the DMD was pinned a poster with the following broad 20 functions of the DMD: (a) Crime Mapping of snatching, robbery, rape and eve teasing on daily basis as per green diary which is received from Command Room. (b) Prepare a daily digital crime map of Delhi Police Ranges and Districts and send these crime maps to all concerned senior officers including CP Delhi. (c) Mapping TSR (auto rickshaw) refusal on a daily basis that is received from the Command Room. (d) Data updation as per challan received from call takers of all shifts. (e) Preparing map up to beat level as jurisdiction marked by concerned Police Stations. (f) Prepared various layers, i.e., major roads, airport, beat of police stations, metro lines, metro stations, Railway Network. (g) Do the survey work of all the PCR bases and Static Pickets in Delhi. (h) Training Call Handlers regarding map handling. 5 Bandobast is a word taken from the Urdu language which loosely means arrangement. The officers here meant to say that the maps were used to assign police forces according to the levels of crime in different parts of the city.

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(i) Data of Delhi State Spatial Data Infrastructure (DSSDI) upgraded as per format of PA 100 Application, i.e., Bank, Clubs, ATM, Colleges, etc. (j) Data Updation of Police Stations in PA100 Application. (k) Net Diagrams (l) Drawing of Mobile Control Room Van (m) Layout Plans of CCTV Camera locations in PHQ boundaries and other such layout plans (n) Prepare Map of jurisdiction boundaries of Police Stations and Police Districts as per requirements of officers. (o) Prepare PCR Zone Maps for the demarcation of PCR Van locations (p) Under DSSDI Project, updating layers for (a) 4 Range Boundary (b) 11 District Boundary (c) 21 PCR Zone Boundary (d) 163 Police Stations Boundaries (e) Police Stations Locations (q) Case Study of Call Traffic Analysis to rearrange police boundaries (in case the call volume to a particular police station is high, it can be put into the jurisdiction of another police district) (r) Crime Mapping (s) Survey Work for Metro lines (t) Other such mapping and survey work that keep coming up according to requirements. As is evident, all the above 20 functions are crucial to any crime mapping exercise: making of boundaries, surveying the city for various landmarks and updating these to keep them relevant. Even the smooth working of the PCR vans is dependent on the maps of their locations designed by the DMD. Any new projects that included mapping or visualising data in any form were done at the DMD. One of the other main tasks of the DMD is to reconfigure the boundaries of police stations and districts of Delhi according to the crime load of the area. Sometimes, due to heavy load on one police station, their areas are mapped to other police stations. Districts are also divided to create new ones, like a new district of Shahadra was recently carved out by taking off a few blocks of East Delhi District. The DMD keeps updating these basic structures of the city as and when new information comes in. This not just helps in adjusting the divisions of the city for governance purposes but is necessary for plotting accurate crime maps.

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The reconfiguration of police station boundaries or whether a police station should be split into two (because the crime rate was too high) was not a simple case of high or low crime numbers. Police officers took great pains in maintaining a “normal” distribution of crime in Delhi: not too low that the police station is scrapped altogether and not too high that it is split. One of the officers in the DMD, who had been a constable for close to 20 years on the field took pains to explain to me that this takes careful alignment of the number of recorded complaints with those that could be sorted out verbally. The decision to record crimes comes from the need to maintain this balance.

Address and Other Base information for Maps The address layer is the starting point of any mapping operation. DMD acquired plain GIS maps of Delhi from the National Informatics Centre (NIC) and a database of 565,000 addresses, i.e., close to 0.6 million; these were, however, already a decade old in 2004. The servers for the operation were initially hosted at NIC but due to connectivity issues, they were moved to Police Headquarters. As is obvious, this database was woefully inadequate considering the fact that the population of Delhi is 16.8 million. All the same, the team had to make do with it because even though in a utopian world, they wanted a full-fledged database of all the mohallas, colonies and resident welfare associations (RWAs) of Delhi, these had never existed. They had no choice, therefore, but to start with this database and update it as they went along. HCL provided the technical expertise to dump6 the existing data on a map which the DMD team keeps updating as and when new addresses become available. Ram Manohar informed me that he has instructed all call takers in the Dial 100 call centre to inform him of the new addresses they encounter in their daily calls, so that they can be added in the address database. For every such address that gets flagged to him, Ram Manohar searches it on Google and then uses the ‘location finder application’ on his mobile device to find its latitude and longitude coordinates and then adds the information to the main database. However, he complained to me that the call takers hardly ever give him any addresses to add in the

6 Data dumping is a technical phrase that means a large amount of data is transferred from one place of storage to the other.

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database; according to him, they take the easy route of marking the location using the police station boundaries database. Continuous reporting could indeed have populated the address database more, making it richer and more informative and gradually it would have become easier to mark locations for all Dial 100 calls. But this has not happened. Along with a textual interface for noting down crime and caller details, a map of Delhi was also added in the PA 100 software, on a separate screen, which the callers are advised to open when they log into their systems. They can click the ‘plot on map’ button on the CRDD form screen to plot the location of the caller directly on the Delhi Map. These steps are crucial in ensuring (even with all the limitations) that the accurate location of the callers is being recorded and eventually crime areas are being accurately plotted. However, as the call takers informed me, the ‘plot on map’ function has not worked in the past few years and has not been repaired. Based on a cursory check at the centre, it emerged that the map worked on only one system, though all the call takers habitually clicked on the plot on map button on the CRDD screen without giving it much thought, almost as if their fingers automatically reached for the button. They did not, however, bother to open the screen with the map because they knew it did not work. I was also informed that very often, it would hang most and take ‘ages to open’, so the call takers did not want to undertake an unnecessary task. They said that regular appeals to maintenance departments to fix this feature had gone unheard and now they did not even bother to inform them. To make plotting accurate, DMD officials, headed by Ram Manohar, surveyed the city in 2004 for new landmarks that had come in over the past 10 years (i.e., from 1994 to 2004), which included new flyovers, parks and of course the metro. The Delhi Metro had become a popular landmark in the city at this time and most people had begun identifying their addresses (while calling the Dial 100 call centre) with Metro stations or the ‘pillars’ under Metro bridges. Ram Manohar was provided with two junior officers and a police car to finish the survey; he told me that this would be termed an indulgence if compared to the resources the department has access to currently for collecting new data and survey work. The DMD team created 58 layers of information for Delhi, including vulnerable routes in Delhi, Yamuna River, Railway Platforms, Railway Stations, Ashram, Banks and ATMs, Bus Routes, Colleges, Community Centres and Courts. These layers continue to play an important role in accurately locating and analysing crime events, both at the DMD as well as

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in CMAPS. However, they will only remain effective if they are updated regularly. Around the year 2006–2007, DMD officials found out that a latest survey map that included all the new landmarks of the city, needed to make the base map for crime mapping, did not exist. Ram Manohar said that he was aware that the Central Government had conducted a large survey of Delhi State around the time of the Commonwealth Games held in Delhi in 2010 in which officials from the DMD also participated. He said that the city was surveyed extensively a year or two before the games, and even though at the time of my conversation with him in 2017, this data was a decade old, it was still the most current data available on Delhi. However, Ram Manohar informed me that it was not accessible to Delhi Police to include in their mapping initiative as it had become the property of a specific government organisation of Delhi that deals with urban planning. It was charging Delhi Police, who had helped in conducting the survey, Rs. 7 million for it. The team at DMD did a fresh round of surveys to collect the same information that was present with another organisation all along. My experiences in the field as a journalist working on stories on UID/ Aadhaar7 and e-governance have similar stories of information becoming locked away in silos and exercises starting afresh; indeed, adequate data is collected in India on a daily basis but is kept aside as soon as the government announces a new project, for which the exercise begins afresh. Data storage and use are not standardised. Because old data is never adequately stored, fresh collection drives are launched whenever information is needed for new projects (which also becomes a way for departments to log expenses and get a piece of the pie of the project budget). Even when data is present, it is never shared with other departments, most of the time due to the lack of standard procedures to do so. According to Ram Manohar, Delhi Police needs to do another series of extensive surveys to collect more information on the residents of the

7 UID/Aadhaar is the universal biometric based identity card for all Indian residents

developed by the Unique Identification Authority of India (UIDAI), see https://uidai. gov.in/. The ID generated a unique identification number that can be used to authenticate and identify people electronically. Though it was much criticised by researchers and poses severe security threats, it is being used across India to provide social security benefits to Indians. It is also used heavily in the financial industry.

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city, for example, names and other details of owners/renters of the houses in the area along with occupation and other such identifying information. He said that such surveys needed to be done for the entire city of Delhi but sadly no funds were being sanctioned for it because the senior officers weren’t interested. He was hopeful that PM Narendra Modi would do an Assam National Resident Register (NRC) kind of exercise in Delhi that would give them the data on each and every household in Delhi. “Meri koi baat maane, to Dilli mein NRC hona chaiye, Assam ki tarah. Modi jee chahe to ho sakta hai. Sabke baare mein ek ek information pata chal jayegi. Aap PhD scholar hai, aap bhi hamari madad kar sakti hai isme. Bahut lamba project hoga, but bahut kaam ka” [If anyone is interested in my opinion, I would suggest an NRC type of exercise in Delhi, just like one done in Assam. If Modi Ji wants, it can happen. It would be a long project, but it would give us every bit of information about all the residents of the city. You are a PhD scholar, you could help us in these surveys.] When I told him I was an ethnographer with no interest or experience in conducting surveys, he said, “There is nothing much to do but visit homes and make enquiries. The ‘bonus’ benefit would be that we would gather all available information about ‘illegal Bangladeshis’. When I asked him how that would help, he looked at me as if I was ignorant, and responded: ‘But they are the root of all crime in Delhi! Get rid of them and our city will be crime-free’”.

Police Station Boundaries The Police Station layer in crime maps is especially important because every crime needs to be pinned to its corresponding police jurisdiction for accurate location mapping and subsequent analysis. Plotting the police station boundaries of Delhi was a mammoth task and the DMD team still proudly claims that they were the only ones who had successfully accomplished the task. This is considered such a vital task because Delhi Police has a confusing layout. In many cases, one side of the road could belong to one police station while the other to a different one. For example, two parts of a posh south Delhi location, South Extension 1 and South Extension 2 lie on either side of the outer ring road in Delhi. While one side of the road falls under the jurisdiction of Hauz Khas Police Station, the other, right across the road, falls under a completely different police Station. Similarly, one half of Jawaharlal Nehru University falls under the

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Vasant Kunj Police Station while the other, under the Vasant Vihar Police Station. A number of people in Delhi can relate to having to run from one police station to another in order to register their complaints because there is no clarity about the jurisdiction limits of police stations here. To clear this confusion, and in order to have a clear demarcation of each and every one of the 163 police stations in 12 districts (the number has increased to 178 in 15 districts by the year 2022), Ram Manohar and his team had decided to undertake a survey of the police stations in Delhi. They sent a proposal to the Deputy Commissioner of Police (incharge of their department) that was passed to the Special CP for approval. On approval, each SHO was summoned to the Headquarters to help the survey team in drawing boundaries on the ArcGIS map. The SHOs would sit with Bhola Ram, who was responsible for plotting these boundaries, and help him in demarcating the exact areas their police station covered. Ram Manohar, along with a few members from the DMD, usually Bhola Ram, would then go and survey the police stations in person to verify the claims of the SHOs. After multiple discussions with the SHOs and surveys by the team, the DMD finalised the police station boundaries layer of the crime GIS map of Delhi around the years 2006–2007. Police station boundaries and district boundaries are updated regularly but survey work for other kinds of information that could help in crime analysis is not undertaken in the same way. I was told that surveys were done with much rigour in the beginning when the centre was being set up. Ram Manohar reminisced wistfully how there were more resources available then in the form of people, money and transport. “No one cares now”, he said, snapping shut another long notebook of crime records on his desk.

The Dial 100 Call Centre as the Source of Crime Data During my weeks at the Headquarters, it became evident to me that the DMD and the Dial 100 call centre are intricately linked. The calls received on the Delhi Police’s emergency response number 100 are the basis for the data used in the mapping system at DMD. Before 2007, when the call centre was automated, the calls on the emergency number were recorded on a printed form: a call taker took down relevant details of the call such as time of call, name of the caller, their location and the description of the event. This form was then sent physically to the Dispatch department

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where someone would manually find which PCR vans were available to send to the location. The maximum technology involved at that time were telephones and radio transmitters. In 2007, HCL Technologies Ltd, a private IT company based in Noida, India, bagged the tender to automate the call centre. The printed forms became electronic forms and the entire operation now began to be run by software on a computer system. A call taker could not just record calls by writing information on a proforma and sending it to Dispatch anymore. Addresses and police jurisdictions had to be filled in using dropdown menus on the form, making them more easily searchable for the call taker. The phone number from which the caller was calling was auto filled in the form with the address on which the number was registered. The call taker had to find out the location of the caller, which as we will learn later, is the most tedious part of the operation because of the lack of a well-maintained address database in Delhi. There is no standardisation in the names and addresses of Delhi, so two people could be talking about the same area in the city, but in completely different terms, making it very difficult to jot it down in a standardised form where the address has to be codified to an area, a street address and house numbers.8 Printed forms back in the day allowed for a certain subjectivity in how addresses were recorded because there were no standardised forms to populate. Something else that came with the standardised form were the 130 prerecorded categories of crime that were not part of the earlier paper forms. In the paper form, the call taker only had to record a description of the event which was later categorised as a particular kind of crime according to investigation/discussion and sections of the laws which would be applied to the final ‘crime’. Now, with the automated system however, the call taker had to categorise the crime when the call came in. The nuances of the events had to make way for what can be recorded, which was restricted to 130 categories. Even though a miscellaneous category exists, call takers told me that its use is discouraged. Understanding the reason for this is vital for our purposes: in algorithmic systems, categories hold considerable power as it is the way that the system learns.

8 We can use this instance to talk about the city and how codified systems such as in this case a codified address system to record calls can change a fluid city into one which is like a mathematical grid. What happens to a city which is as visible and calculable as a grid?

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HCL passed on the mantle of running the call centre on to the government-owned Centre for Development of Advanced Computing (CDAC)9 in 2018–2019 after a long run of 10 years. In mid-2018,10 CDAC had already taken over the working of the helpline numbers and the HIMMAT App11 while the Dial 100 call centre was in the process of being handed over. The DMD team and the officers who supervised the working of the Dial 100 call centre were not very happy with CDAC managing the operations of the call centre. Ironically, they believed that private sector workers were more efficient at completing tasks than their government counterparts.

Dissemination As mentioned in the SOP, DMD produces 23 printed physical maps sent to several heads of police every day (mentioned below). Of the 23, 21 maps are sent to each of the 13 district heads, Six Range12 heads () and Two Law and Order heads. Of the remaining two maps, one is exclusively for auto rickshaw challans and the other is a comprehensive one consisting of all the crimes in all the districts of Delhi.

9 Official website of CDAC, https://www.cdac.in/, accessed June 8, 2022. 10 My fieldwork extended from February 2017 to March 2019 as I have mentioned in

Chapter 1. 11 HIMMAT is a Hindi language word which means strength. HIMMAT app is an in house app developed by the Delhi Police for women’s safety. Women are encouraged to install this app on their phones. This enables them to send emergency SOS signals to the police and their trusted contacts and also record footage of their predicament for future reference. Please see https://digitalindia.gov.in/content/himmat-app, accessed June 8, 2022. 12 13 districts combine to make six ranges. Ranges, 1 and 2 combine to form the New Delhi Range. 3 and 4 combine to form the Southern Range. 5 and 6 combine to form the South Western Range. 7 and 8 combine to form Northern Range. 9 and 10 combine to form Central Range. 11, 12 and 13 combine to form Eastern Range. Six ranges combine to make two Law and Order heads. Law and Order (LO), South comprises the New Delhi Range, Southern Range and South Western Range while LO North comprises Northern Range, Central Range and Eastern Range.

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The digital map also contains detailed descriptions of all crimes, called the attributes of data, such as address of the location of crime, the police station under whose jurisdiction the incident location falls, along with a description of crime and the time when it took place. These details are also maintained as a separate database. The printed maps that are actually sent to the Police Commissioners and Special Commissioners of Police, however, just contain the location of crime printed as coloured dots on a map of Delhi. Printed maps are preferred to digital maps as they may be touched, felt and performed as physical objects. The comfort that officers continue to have with printed documents is also the reason why paper trumps digital even in an organisation desperately trying to be futuristic. On the other hand, CMAPS gives a real-time description of the hotspots of crime. It sources its input from the Dial 100 call centre and the Crime and Criminal Tracking Network System (CCTNS) and calculates hotspots of crime accordingly. The login ID and passwords for CMAPS are given to Commissioners, other senior officers in central and district police headquarters and SHOs of police stations. They can log in to the system at any time and check the levels of crime in their areas and manage their force accordingly.

Resources and Training for Mapping and Data Collection The DMD is assigned different projects according to the requirements of law and order in the city. This is apart from their core work of mapping crime on a daily basis. For example, while my fieldwork was going on, the team was asked to undertake a new project where they had to map crimes related to women starting from January 2018, and another where they had to map fire incidents in the capital, the base data for which would be the Dial 100 calls. Without adequate resources or support to do the job, however, at the time, Ram Manohar looked at the notebook from which he was plotting the incidents related to fire and said, “We are just given the work, do this or that without any thought of how it would be done or if it could be done in a better, more efficient way”. “What would you like the organisation to provide to you?” “We have only four, five people. We work from 9AM to 6PM finishing our daily commitments and then we have this extra work, we need people, better trained people”.

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Ram Manohar put down the notebook in his hand and said, “I got this notebook from the HIMMAT App13 people. It has all the details of women-related crimes for this year (2018) which has made our work easier. Otherwise, can you imagine, we would have to search all the calls14 to find specific instances of women-related calls. Who has the time for that!” Manohar was upset about being treated as disposable labour without any planning for long-term investment in the centre. “They made us survey electric poles in the Rajiv Chowk area without providing for transport or people. Punit and I went in the heat to mark the location of the poles on the map during our workday. But look at this! We do no survey for our own maps but are sent to work for external agencies. How is this fair?” “They put crores in the new system, CMAPS, but cannot spend even a fraction of that money on us”. Base data for operating GIS systems is one of the most under-discussed aspects of these systems; getting the data to be used in GIS systems could be either expensive, fairly inaccessible or just very labour-intensive (Dorling and Fairbairn 2013). Ram Manohar and his team worked round the clock in surveying Delhi to make available the information on police station boundaries and local addresses. He remarked that the task of data collection was ‘easy’ and that he was encouraged when the department was in the process of being set up. “There was ample manpower and money”, Ram Manohar said. But now the maps languish without much analysis because there is no background data that could help the police in any further analysis to effectively curb crime. “There is no incentive to collect more granular data to make the maps more accurate, to help in analysis. I have so much data here but all that we do is send daily maps. Imagine if we could analyse all this, cross reference it with other kinds of data, the kind of insights we could get, the kind of work we could do?” “You are a PhD student, you should help us in surveys, maybe you can talk to the senior officers regarding how important they are”, he added. The material infrastructure makes its own statement in the DMD. The Pentium 2 processor computers which hang more than they work and 13 HIMMAT App is an in-house APP from Delhi Police for women to report crimes against them. See https://digitalindia.gov.in/content/himmat-app and http://himmat. delhipolice.nic.in/. 14 All the calls for a period of approximately three months where the daily caseload could be anywhere from 5000 to 20,000 calls.

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the monitors that shut down abruptly and have to be slapped gently to wake them again don’t make the work go any faster. Archaic computers and processors, low resolution monitors, outdated even in 2007 when the division started, line the benches of the DMD. The keyboards often get stuck, so much so that there is an acquired physical sense in all the team members on how much pressure one must apply to get them to work. Not very long ago, when one of the monitors absolutely stopped working, they were given what looked like an old TV screen, where the maps were barely visible, from the Headquarters storeroom. When I asked Punit about it, he smiled and shrugged. “This is how much they think we are worth, no?”. The human resources provided to the DMD, according to Ram Sevak (incharge) are ones who have lost their ability to work in the highpressure environment of the thanas . Ram Sevak, just like Ram Manohar, was disgruntled by the lack of attention paid to his department. He was happy to show me around and explain the working of his department because, according to him, finally someone was interested in the work that they did. In a hyper-masculine environment of the Delhi Police HQ, his department was said to be ‘fit for women’ or those who were unfit for the violent, very ‘masculine’ job of policing on the field. He was angry with this characterisation; it was the reason why the only personnel he got were the rejects of policing. As Ram Manohar explained to me, “Most people take the call taker job as a ‘comfort job’ to avoid the hardships endured in policing on the ground”. Sevak was also unhappy with the transfer policy of the Police, where people were transferred just as they began to get the hang of things. Ram Sevak reiterated what Ram Manohar had said about the call centre (which was the basis of data collection for mapping in Delhi Police), that it needed people who were young, agile and could think on their feet. He was against people above 40 working in the call centre. “After 40 you have many responsibilities, especially aging parents and kids. You are no longer fit for working in a high-pressure environment like the call centre”, he said. Sevak also said that the education and background of the call takers meant that they were not polite or empathetic to the callers. The training budget was dismal. He reasoned this was why they did not put much effort into learning the work. He said that for a better call centre, where call takers would record crime details accurately and map locations precisely, call takers’ jobs would have to become permanent.

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Sevak attempted to create a profile of his team members for me. Some of them came from poor economic backgrounds, and had gone to school without basic infrastructure or computer-based teaching whatsoever, or they were so old that any current technical knowledge was too formidable for them. The new hires in the form of executive-level officers (constables) had a basic knowledge of computers which gives them an edge over others. But as Sevak mentioned before, they would be transferred before they got the hang of things. An elderly team member, Moti Ram, was a beat constable for 28 years before he was transferred to the mapping division. Before coming to the division, according to him, he had never even held a computer ‘mouse’ in his hands. He was sent to the DMD without any training or any provision for training. It is interesting to note that there is no facility for training of any personnel in mapping and they all learn on the job, with the old members teaching the new and Ram Manohar and Bhola Ram guiding the training. Bhola Ram has a diploma in cartography but still struggles with new technologies of mapping such as GIS. Moti Ram received informal training from the team members at the DMD who, he acknowledges, were patient enough to make him learn. Even after four and half years in the division (in 2018), however, he typed using only one hand. Moti Ram has been stuck for promotion for the last 15 years because of a complaint against him; he said to me that otherwise, he would at least be a sub-inspector now. “I was asked to beat up an accused by my senior officer while I was stationed at a thana in Delhi. I did as I was told, wouldn’t you? The accused complained about me and the enquiry is still not over after 15 years. The senior officer has got promotions but I am stuck, and now I am here learning how to use the computer”, he said. However, because of Moti Ram’s extensive experience as a beat constable, he is invaluable when it comes to his knowledge of Delhi and its streets while mapping. The address database of Delhi is insufficient— with approximately 0.6 million entries—and it is not enough to map the various addresses of the city where crime takes place. Mapping is done according to the mappers’ own knowledge of the city and Moti Ram’s help. He is aptly nicknamed “hamara Google” (our Google) by his teammates because he knows Delhi like the back of his hand. No address is unknown to Moti Ram, who could pinpoint with startling accuracy where a certain alley or mohalla would be located. Even with his slow speed on computers, he would plot the maps faster than others simply because he had to only look at the address once to know where it was.

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The other team member, Punit, was also a beat-level constable before being transferred to the DMD. He too is not very well-versed with mapping or computers but has substantial policing knowledge to help in surveys and other outside activities of the department. Punit was also very chatty and knowledgeable about the functioning of the police. He taught me certain Urdu words used in the daily functioning of the police such as Roznamcha (Daily Diary) and Tehrir ki Mausulgi (Complaint Received). He told me how the older generation of police officers is more well-versed with these Urdu terminologies rather than Hindi or English ones, especially when making records such as Daily Diaries or Complaint/First Information Report (FIR) registers. However, he said that the young officers are more comfortable with Hindi/English. “This is the last bit of tradition that is going away from the police”, he said. Punit was a traditional Hindu man, who believed in the caste system as a non discriminatory, functional system for the good of the society. He thought of himself as a pious man. He promptly gave me Rs. 10115 when I went to the HQ with my four-month-old daughter for the first time. The Status of Policing in India report (SPIR 2019) reported serious gaps in human, technological and physical infrastructure in policing in India (p. 64) such as lack of clean drinking water and even toilets in most police stations. In the PHQ, the toilets for the use of common people and police personnel were extremely dirty and unfit for human use (the IPS level officers had their own toilets attached to their cabins). I had to resort to using the one passably clean bathroom on the second floor of the HQ (which meant a journey from the fourth or the third floor, wherever I would be currently, all the way to the second floor. Going to the toilet was a mission). Even during my field visits to the police stations, it was difficult not to see the inhuman conditions that some police officers worked under, especially in police stations in North East Delhi where the compound was dirty and the anterooms spoke of neglect. The apathy to fix core infrastructure but invest in expensive, seldom-used technological products shows how police work is more about presenting an image of a futuristic, modern organisation to the people without putting in much thought into bettering its own labour force. The SIPR 2019 found out that only 6 per cent of police personnel, concentrated in the higher levels of the hierarchy, received in-service 15 As is tradition, Most Hindus offer an odd sum of money, Rs. 21,101,501 to guests who visit their residence for the first time.

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training during their career, which it said could be correlated with the common-sense assumption of criminality about people from slums and immigrants. I would argue that prevalence of these common-sense notions of crime and criminality are not a mere training issue but originate in the caste system in India (Narayan 2021). Lack of diversity in the police force, as cited from the same report’s 2018 edition, helps these notions thrive as there is no one, or very few people, to challenge them (Narayan 2020). However, there are still many advantages in introducing training for officers; especially as the report states, it would help them to use technology better and to understand its non-neutral nature in order to work with caution with the many technological tools provided to the police currently. Technically, the switch from the private technology firm HCL to the government-owned CDAC as the technical support arm of the HQ meant a shift in the pace of crime mapping. Officers overlooking the ‘HIMMAT App’ (Himmat is a hindi word which means courage. This is an App designed by Delhi Police for women’s safety. Women can report sexual harassment incidents including location and photos) said to me that unlike HCL which provided a dedicated resource at the PHQ to resolve the technical difficulties in the app, CDAC offered an impersonal call centre. The call centre gives them an automated ‘ticket number’ for their complaints where the query is put in a queue and resolved accordingly, without any personal attention. Another technical officer in the HIMMAT app department told me that it had been 24 hours since he had complained of a VPN connection fault of the app with a CDAC enquiry but was still waiting for a call back. Despite being government employees themselves, people in the DMD and other technicians were not very sure of the service delivery of another government department. They said that they had always known that HCL would exit sooner or later because government departments are famous for not paying their partners in Public Private Partnership (PPP) Projects. In turn, most private players, even after bagging the tender, do not provide their best resources on government projects, resulting in shoddy service and another reason for governments to delay their payments. And the loop goes on. They said the same thing happened with HCL, with them waiting for pending payments almost always, and it was a matter of time before they left. However, even the sub-par levels of service provided by HCL were much better than what any government department could muster, they told me.

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Bureaucratic Entanglements of the Digital Mapping Division What became clear to me during my fieldwork was that the DMD barely gets any credit for all its work in setting up the crime mapping infrastructure in the Delhi Police. What this seemed to indicate was a lack of ‘actual’ need for crime mapping but rather the infrastructure being set up more as (a) a technological intervention acquired because others have it; and (b) a symbol of serious action by the police against crime to gain the trust of the citizens of Delhi. Within policing circles, the officers in the DMD complain that they are not considered serious police officers, as their work does not entail ‘real’ policing, i.e., which happens on the streets and in the thanas. They lament the lack of recognition of their department in the annals of policing in the city. “We are called the bindu lagane wale (People who merely put dots). They think we just put these coloured dots on the map, that’s how we are introduced to people from outside”, says Ram Manohar with a rueful smile. The DMD acquired four licences of mapping software(s) ArcView, one each of ArcInfo, ArcEditor, and of Internet Mapping Server from ESRI when they started the mapping work in 2007; now the licences have expired and it would take a few tens of millions rupees to acquire new ones. This is no longer a priority since in the meantime CMAPS has been set up. Though CMAPS was set up, as stated in the standard operating procedure manual of DMD, to replace manual mapping with automatic mapping, its use does not make it clear if the new investment has been worth the effort. As mentioned earlier, the computer system with CMAPS was mostly in sleep mode in the Delhi Police HQ the whole time when I was conducting my fieldwork. The CMAPS mapping platform resembles the ArcGIS software that runs in the DMD on the floor below. Ram Manohar said that ISRO had not made a ‘new’ software or heralded a revolution in the way a hotspot mapping software is designed and that the programme resembled all other hotspot mapping software available in the market. Ram Manohar’s disdain was evident when he said that CDs of such software could be bought cheap in any market such as Nehru Place or Palika Bazaar.16 He was 16 Nehru Place is known for providing affordable IT services and equipment to their customers. However, piracy is commonplace and pirated videos, software are available to those who are looking for a cheap fix for their computers. Palika Bazaar is an underground

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disheartened that the government would not spend more money to make the DMD more effective but was ready to waste money on half-hearted efforts in CMAPS.17 The DMD team wondered if they couldn’t have done a better job than ISRO/CMAPS when they were the ones who had collated and provided all the base data (all the layers) that CMAPS was using for its mapping. Both Ram Manohar and Ram Sevak also told me that neither their consent or permission was taken when the base data was made available to CMAPS. Lack of working infrastructure along with the Delhi Police administration’s indifference towards the DMD’s contributions seems to be an indication of an administration more influenced by political exigencies than the actual work of policing. A recent news report in the Washington Post 18 suggested that police departments often acquire new technology not on the basis of a reasoned cost-benefit analysis but simply because another department has it. The lack of understanding of technology in local police stations and the departmental neglect of those involved in crime mapping (especially by officers in police stations, who believe that ‘real’ policing is done by their officers who are physically present on the field), indicates a break between the everyday realities of policing (less technology, more manpower-based) and what needs to be projected to the public via the media (savvy, technology-oriented smart policing that can tackle any crime). The hue and cry in the media regarding the status of Delhi as the ‘crime capital’ of the country19 might also have had a role to play in Delhi Police’s decision to invest in Predictive Policing technologies. However, as we have seen, over a period of time, even these

market in Delhi’s famous Connaught Place which is known for its bargains. As a novice to Palika, I was told to pay only 25% of the quoted price by the shopkeeper. I did and it worked! 17 Ram Manohar’s concern was not at all misplaced. A CAG report confirmed that CMAPS was used “sub optimally”. The report said that the planned objectives of the project were abandoned and its utility was questionable. See the report here: https://cag.gov.in/uploads/PressRelease/PR-Press-Release-Rep-15-2020-Civil-05f 79f136429bd3-22501879.pdf, accessed April 1, 2022. 18 We spend $100 billion on policing, we don’t know how it works, https://www.was hingtonpost.com/posteverything/wp/2017/03/10/we-spend-100-billion-on-policingwe-have-no-idea-what-works/, accessed Friday, September 18, 2020. 19 Delhi has been routinely called the crime capital of India because of the high rates of crime reported from the city every year, https://www.newslaundry.com/2020/01/27/ why-is-delhi-indias-crime-capital, accessed April 3, 2023.

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centres suffer neglect, when new and more expensive technologies come up, such as the automated CMAPS that was set up to replace the more manual DMD. Ram Manohar was sad that the base data that they had so accurately curated was given to external agencies without consulting them. Along with CMAPS, HIMMAT, an SOS application for women, was also designed with data from the DMD. The team said that there were no confidentiality agreements signed between them and the other party using their data. Neither was there any agreement on how the data could be used. They felt useless, as if their work did not matter. At one point, they were to be given separate funds to expand the DMD and revamp the Dial 100 call centre but the proposal was merged with the National Emergency Response System (NERS, started in August 201520 ), which had since become embroiled in bureaucratic issues. NERS is to be the new emergency number of the city, number 112, on the lines of 911 in the USA, which would enable better distribution of emergency calls in the city. Currently all calls related to fire, traffic, accidents, other medical emergencies, even potholes, landed at 100, increasing the call load of the centre and decreasing its efficiency. 112 would smartly route calls to their right destination and only allow crime, and law and order-related calls to reach the police. In effect, 100 for police, 101 for fire and 102 for ambulance would be merged in one number 112 and routed accordingly. The call centre of NERS would be set up in a different building in another part of the city. The project has been stuck for a few years now (it had been stuck for a few years while I was doing the fieldwork for this study during 2017–2019. I have checked again and though the new building for Police HQ is up in central Delhi, there is no information about the other project building where the call centre for 112 was supposed to be established) and no one knew when it would reach completion, though regular file movements and presentations have been taking place over some time. According to Sarita in CMAPS who was part of the meetings, regular presentations were being done for NERS and the project had already been handed over to CDAC.

20 According to this document by Ministry of Home Affairs, Government of India, https://vifdatabase.com/wp-content/uploads/2018/04/nersguideline_2100815. pdf, accessed October 12, 2020.

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CMAPS CMAPS started in 2015 but was announced to the world with much fanfare in January 2017, with an accompanying news report dated 31 January 2017 in the Hindustan Times showing off its capabilities and prowess in tackling crime in the city. Delhi Police was only too excited to partner with ISRO, which is considered to be the best in India when it comes to space technology. Call data from the Dial 100 call centre (as recorded by the call takers) and FIR data from the Crime and Criminal Tracking System (CCTNS), set up by the UPA II government as a part of its e-governance initiative to sync crime information across all district police stations in India is mapped on CMAPS every three minutes to plot the latest picture of crime in Delhi. CMAPS is housed in the C4i wing of the CPCR, on the fourth floor of the Delhi Police Headquarters. In the centre of the hall, a computer system is set up with the CMAPS software loaded and working in it. On the sidewall are pasted the 13 kinds of crimes, including rape, robbery, snatching and eve teasing , that were already part of the DMD repertoire, that it maps and produces hotspots for. A female supervisor, Sarita, who is an executive-level officer or a constable in the Delhi Police, in her mid-twenties when I met her, looks after the running of the software. She was trained to log in and browse CMAPS and check crime status in different parts of the city at any time of the day. Sarita was always nervous discussing CMAPS with me because she was forbidden to do so by her supervisor, who sits in a cabin in the gallery leading to the hall and is suspicious of anyone checking the software, or talking to Sarita too long. Sarita stayed away from journalists and refrained from talking about the initiatives of Delhi Police. She only explained the working of CMAPS to me because Ram Sevak explained to her supervisor that I have permission for research in the PHQ. However, it was a closely monitored explanatory session where the supervisor monitored her every word and did not let her go into much detail. I attempted to go to C4i (where CMAPS is housed) and speak to her several times but every time I tried, the supervisor appeared out of nowhere and cut our conversation short. I could not persist because I was not allowed to be in the C4i wing for long periods of time due to security reasons. The only time Sarita spoke without care was when she came to the DMD to get something printed. However, she was still hesitant about talking about CMAPS.

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Sarita had limited information about the design and working of CMAPS or how crime is actually analysed in the system. It was obvious that while she knew how to use the application, she had no idea about its background work. I saw that the computer where CMAPS ran was shut most of the time (screen off mode) and was only switched on when someone logged in. It was obvious that CMAPS was not being used for active analysis or investigation of crime at the PHQ. According to the HQ officers overseeing the project, the District Superintendent of Police (DSP) along with SHOs of police stations have access to the maps for their routine police activities. However, in all the police stations that I visited during my fieldwork (2017 and 2018), I could not find any officers using it for their policing activities. One of the officers at a South Delhi police station even told me that their traditional modes of policing were much more effective than new forms of technological policing. Though there have been news reports21 about CMAPS helping Delhi Police in nabbing criminals, the Comptroller and Auditor General (CAG) Audit report22 of logistics and manpower management in Delhi Police released in September 2020 states that CMAPS is not being used to its optimum level. It further states that the planned objectives of the project have been abandoned and therefore its utility is questionable. CMAPS is used more like a visualisation programme rather than an analysis software in Delhi. The map of Delhi is visible on the home screen with different coloured dots indicating crime spots marked at specific points. The legend pertaining to the colours is given at the bottom righthand corner. One can zoom into a specific part of the city or zoom out to see the crime situation in the entire city and get an idea about areas that are more prone to crime. Crime data from the Dial 100 call centre and FIR data from CCTNS is fed into it every three minutes. Historical data collected by the DMD is entered as layers in CMAPS but analysis is still limited because the DMD has data on only four kinds of crimes while

21 Delhi’s tryst with predictive policing, https://timesofindia.indiatimes.com/ city/delhi/delhis-tryst-with-predictive-policing/articleshow/64598386.cms, accessed Wednesday, November 4, 2020. 22 CAG’s performance audit report on manpower and logistics management in Delhi Police dated September 23, 2020, https://cag.gov.in/uploads/PressRelease/PRPress-Release-Rep-15-2020-Civil-05f79f136429bd3-22501879.pdf, accessed Wednesday, November 4, 2020.

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CMAPS works with 13 kinds. There is no effort to expand the data collection in the DMD, which implies that new datasets are also not added in the CMAPS system. The cost of data collection and updating could be the reason for the half-hearted operation of the DMD and CMAPS. Dorling and Fairbairn (2013) in their study on crime mapping using GIS systems state that in time, the cost of updating and maintaining data used to run the GIS system could far exceed the cost of setting it up in the first place.

Crime Mapping Is Not a Technological but a Human Endeavour In documenting the labour that has gone into setting up a crime mapping division, my aim has been to humanise the process of crime mapping that is otherwise considered to be a mechanical process, devoid of human arbitrariness and prejudice. While Chainey and Ratcliffe (2005) have described five clear steps of such an enterprise, this chapter makes it clear that processes do not exist in clear silos but merge into one another. The DMD was not set up as a mapping division but to work on data-related work of the police; for example, to restructure police station boundaries according to their crime load. As we have seen, the task of crime mapping developed in an organic manner when a certain DCP wanted to use mapping for curbing carjacking crimes in the city. From the selection of which crimes should be mapped, to the actual mapping process—from mapping at the police station location to an approximate location to a somewhat accurate location—we have seen the processes at work which run counter to an assumed rational way of working of a bureaucratic organisation (Hull 2016; Mathur 2016). More than just showcasing the fluid and organic character of bureaucratic organisations, in this chapter what I have attempted to show in detail is the politics of bureaucratic decision-making. The working of the DMD, along with the dissatisfaction of its officers for being underfunded and for not being considered serious police officers, in relation to the launch of CMAPS, tells the story of technological interventions in policing that are as much decisions of logic as they are of people trying to put something together to see if it works. One of the things I really want to emphasise in this entire story is the point about infrastructure. Not the symbolic infrastructure that is the whole assemblage of crime mapping but infrastructure that is the material aspect of how technology runs. It is the invisible wires and tubes,

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working in the background, that make any work possible, only becoming visible when the system breaks down (Bowker and Star 2000). However, a comment by Ravi Sundaram, Professor at the Centre for the Study of Developing Societies (CSDS), has resonated with me since I first heard it in 201723 Prof. Sundaram asks if in a country like India people even have an idea of unbroken, invisible infrastructure or an idea of seamlessness? He argued that things always worked in a kind of patchwork way in India so essentially how does one know if/when things are actually broken? I have earlier mentioned the slow P2 processors in DMD, the computers that have to be nudged for them to work along with the TV monitor that was sent to replace a broken computer monitor. While these are obvious examples of breakdown of infrastructure, somehow, in the Delhi Police Headquarters, it does not signify the breakdown of the system. There is no break in the work because there are always workarounds around the break. This opens up a wider issue of standardisation and the (lack of) seamless operation in a technological system designed and developed for the West but used in South Asian countries. Infrastructure use for predictive policing systems are being adapted in non-Western contexts such as in countries like India and such adaptations become standardised with use (Star and Ruhleder 1994). This can be seen even in the case of mapping using ad hoc location details or better still, using a human being’s (Moti Ram’s) experiential knowledge to translate it into digital location data for mapping which is in stark contrast from the USA, where these systems have been designed and exported from, with its robust system of base databases, especially that of addresses/locations. As Latour’s (2005, 72) Actor Network Theory states “In addition to ‘determining’ and serving as a ‘back-drop for human action’, things might authorize, allow, afford, encourage, permit, suggest, influence, block, render possible, forbid, and so on”. In this sense, less than optimum infrastructure such as archaic computers or TV monitors in place of computer monitors does not just sit around in an organisation but also actively participates in its outputs. The lack of money for renewing ArcGIS licences and for surveys to collect new data also disallows the team in the DMD from making new breakthroughs in crime mapping. The latter is in turn used as a justification or validation to make way 23 The recording of the Presentation and Prof Ravi Sundaram’s comments can be heard here https://sarai.net/events/lives-of-data-workshop-report-recordings/ under ‘Preeti Mudliar, IIIT Bangalore Broken Data”, accessed Sunday, October 25, 2020.

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for more ‘sophisticated’ and automated systems such as CMAPS. That CMAPS itself needs the DMD to collect and update historical and base data for its optimum working is lost on the decision-makers. In the dispatch floor upstairs, as I will discuss in the next chapter on the Green Diary, the lack of foot-pedalled microphones may just be the difference between PCR vans reaching a crime site on time or never reaching at all, impacting further the kind of data collected for predictive policing. Moti Ram’s statement when the DMD was provided with an old, feeble TV monitor from the PHQ storeroom in place of a faulty computer monitor, “this is how much they think we are worth” made evident that Delhi Police favours CMAPS over the DMD because the former provides better press. Indeed, technological interventions in policing can often be more about a bureaucratic/political exercise than responses to actual policing needs. Studying the design of a system provides us with a lens to understand the political rationality of a government that defines the ideation and deployment of technological projects (Larkin 2013). In that sense, a look at how a system is designed, right down to its structural spaces, plumbing and tubing or in this case, to the training of human resources, gives us an insight into the intended nature of the project which, in Delhi Police HQ terms, we see in the intended design of ‘layers’ of crime mapping software. The understanding of crime as a ‘natural problem’ of the marginalised, which necessitates their surveillance and control, rather than a structural problem requiring governmental assistance, is laid bare in the requirements of data related to their socio-economic conditions or education backgrounds. My time at the DMD, its adjoining Dial 100 Call Centre and CMAPS (along with the swanky C4i floor where it is housed) taught me about the material, indeed, very physical, existence of the production of data along with its fine print of the social and political. As Bowker et al. (2010) suggest, this kind of perspective is not new and has been explored by Latour and Woolgar in Laboratory Life (1979) and many others. However, it is important to reiterate that (a) data is a result of culmination of very physical processes, the click and clang of machines, just as may be imagined of a piece of cloth or a car or any other physical object, but (b) at the same time it works on the principles of the dominant cultural and social values. The people living on the edges of society have been deemed as criminals from time immemorial (the Criminal Tribes

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Act of 1857 was not so long ago) and those thought processes seep into so-called ‘objective’ ‘data-based’ systems as well. In the absence of social details of an area on the map in the form of its base data (or even layers), comparing crime numbers is of no use; this is because, while it would provide details of a high crime area numerically, it would not provide any reasons for why one area is more crime-prone than the other. It will also result in making poverty a personal problem rather than, as mentioned earlier, a structural one. The myths surrounding the ‘lazy poor people living off state benefits’ are a product of this theorisation. Crime analysis done with such an approach does nothing to ameliorate reasons for crime occurrence. As we will see in more detail in further chapters, it only provides reasons for more and more policing of certain areas. One of the final issues that this chapter raises is that of “techno hubris” which asks if mere building of certain infrastructures guarantees their usage (Bowker et al. 2010). CMAPS was set up with much fanfare and at a high cost; though its actual budget was not disclosed to me, from conversations with other offices in the communications wing, I could assess that it was an expensive gadget for the police. C4i, the centre where CMAPS was stationed, was itself a spanking new state-of-the-art wing. Still, it was hardly used for crime investigation/analysis either in the PHQ or in the other police stations in Delhi and merely remained on show most of the time. Furthermore, there was not much attention paid to the procurement of data that was the bedrock of the entire system. No funds were available for surveys and data collection or to strengthen the DMD, where data collection surveys were actually done. An expensive software which does not even meet its basic utility is not just a manifestation of techno hubris but also a preference for a certain perception in the public domain. For people to think that the government in power is doing well, a lot depends on the law and order situation. Hence, arming the police with the latest gadgets seemed like a good election investment. Indeed, it speaks volumes that my access to the Delhi Police HQ was cut off just before the general elections in 2019. The DCP, rejecting my permission to conduct further research at the HQ said, “what if you write something against us and it hurts the prospects of the government in the elections?”

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References Bowker, Geoffrey C., and Susan Leigh Star. 2000. Sorting Things Out: Classification and Its Consequences. Inside Technology. Cambridge, MA: MIT Press. Bowker, Geoffrey C., Karen Baker, Florence Millerand, and David Ribes. 2010. ‘Toward Information Infrastructure Studies: Ways of Knowing in a Networked Environment’. In International Handbook of Internet Research, edited by Jeremy Hunsinger, Lisbeth Klastrup, and Matthew Allen, 97–117. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-1-4020-9789-8_5. Chainey, S., and J. Ratcliffe. 2005. GIS and Crime Mapping. Chichester: Wiley. Dorling, Daniel, and David Fairbairn. 2013. Mapping: Ways of Representing the World. London: Routledge. https://doi.org/10.4324/9781315842264. Foucault, Michel. 2020. The History of Sexuality. Volume 1, The Will to Knowledge. Penguin Modern Classics. London: Penguin Classics. Hull, Matthew S. 2016. Government of Paper: The Materiality of Bureaucracy in Urban Pakistan. https://doi.org/10.1525/california/9780520272149.001. 0001. Larkin, Brian. 2013. ‘The Politics and Poetics of Infrastructure’. Annual Review of Anthropology 42 (1): 327–43. https://doi.org/10.1146/annurev-anthro092412-155522. Latour, Bruno. 2005. Reassembling the Social: An Introduction to Actor-NetworkTheory. Clarendon Lectures in Management Studies. Oxford; New York: Oxford University Press. https://hdl.handle.net/2027/heb32135. Latour, Bruno, and Steve Woolgar. 1979. Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills: Sage Publications. https://archive.org/det ails/laboratorylifeso0000lato. Mathur, Nayanika. 2016. Paper Tiger: Law, Bureaucracy and the Developmental State in Himalayan India. Cambridge Studies in Law and Society. Delhi, India: Cambridge University Press. https://doi.org/10.1017/CBO978131 6227367. Narayan, Shivangi. 2020. ‘Past, Present, and Past as Present in India’s Predictive Policing’. XRDS: Crossroads, The ACM Magazine for Students 27 (2): 36–41. https://doi.org/10.1145/3433144. ———. 2021. ‘Guilty Until Proven Guilty: Policing Caste Through Preventive Policing Registers in India’. Journal of Extreme Anthropology 5 (1). https:// doi.org/10.5617/jea.8797. Scott, James C. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale Agrarian Studies. New Haven: Yale University Press. Star, Susan Leigh, and Karen Ruhleder. 1994. ‘Steps Towards an Ecology of Infrastructure: Complex Problems in Design and Access for Large-Scale Collaborative Systems’. In Proceedings of the 1994 ACM Conference on

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Computer Supported Cooperative Work, 253–64. CSCW ’94. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/192844. 193021. Winner, Langdon. 1980. ‘Do Artifacts Have Politics?’ Daedalus 109 (1): 121– 36.

CHAPTER 3

Green Diary

Introduction The Diary is simultaneously a classification system and an archive. Every day, precisely at 6 AM, DMD receives a green coloured sheaf of paper containing the record of the four selected categories of crimes of the previous day. This document, meticulously prepared by the Dispatch Floor Command Room, contains the details of crimes received under the categories of rape, robbery, snatching and ‘eve teasing’ (all marked as ‘heinous’ crime categories in the Delhi Police Headquarters) from 00:00 hours 23:59 hours which have been verified by PHQ officers. By 9 AM, the information in the document is plotted on 23 maps and sent to various department heads and range officers in the city to help them decide the arrangement of ground police force in the city. Everything about the Green Diary, called so because it is literally made from green paper, screams unalterable truth. It is a record of crime that took place in the past 24 hours in the city of Delhi, recorded with the help of police officers who attended to the citizens who called the emergency response number 100 to report the crime. Yet, it is a result of human beings coming to an agreement on what the truth must entail. For instance, there are arguments about whether a certain crime may be categorised as a robbery or theft. Or if the women of a certain region within Delhi even tell the truth while reporting sexual harassment.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2_3

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A classification system is where “standards, categories, technologies and phenomenology” converge (Bowker and Star 1999, 47) and where are present invisibly the “bureaucratic struggles, differences in world-view, and systematic erasures” (ibid., 55). In this chapter we plan to look at all these—the standards, categories, technologies and phenomenology that converges in the making of the Green Diary and the bureaucratic struggles, differences in worldview and systematic erasures that do not show up in the final document but are present throughout. According to Lisa Gitelman (2013) and Gitelman and Jackson (2013, 3), data needs to be imagined before it can function as data and therefore, it entails an interpretative process. A call, asking for help from the police for a crime committed against a person needs to be processed before it can be used to understand crime patterns in the city. Mathew Stanley (2013, 84) also argues that biblical data on astronomy needed to pass through textual, historical or psychological filters before it could be used for current calculations on astronomy. “Each filter could be used positively or negatively, to either exclude a record from reliability or to detect reliable records”. This chapter aims to uncover these filters on each aspect of data prepared for crime mapping. As an archive, the Green Diary is a record of the daily crimes of snatching, eve teasing and rape and robbery that pass the scrutiny of the police and end up being the official representation of crime in the capital. However, as is true with any archive, the people who create the document have a role to play and a perspective that inserts itself in its construction. Schwartz and Cook (2002) bust the myth of the archive as a value free place of storage of records and archivists as the neutral record keeper and position power, that enables one to include one record in the archive over the other, as the most important aspect to understand an archive. “Yet power – power to make records of certain events and ideas and not others, power to name, label, order records to meet business, government or personal needs, power to preserve the record, power to mediate the record, power over access, power over individual rights and freedoms over collective memory and national identity – is a concept generally absent…(in the understanding of the archive)” (p. 5). This ‘power’ can be observed in the verification process of the emergency calls that land in the call centre at the PHQ requiring immediate assistance. Not all calls are found to be ‘true’ after investigation. On 14 March 2018, for instance, Ram Manohar checked his logs, and found calls had been made reporting 200 cases of eve teasing, which, he said,

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were reduced to eight or nine after investigations. Similarly, the call centre received nine calls for rape that became zero upon investigation. For snatching, there were 800 calls out of which only 64 were found true upon investigation. Ram Manohar did not have a clear reply when I enquired about this reduction in the numbers upon investigation. His response suggested to me that police were summoned because it was easy to do so and people could do so even when there was no real problem at hand. “A woman called the police a couple of days back claiming that someone snatched her chain on the bus stop when she went to drop her kids to school. When the police reached there, she said the chain had broken and fell in her blouse. Not finding it on her neck, she suspected snatching and called the police,” he said. “These things happen very frequently, but we do not know until we go to the site and we have to attend to every call that comes in,” he added. Being ‘true’ is an interesting concept and one that we will investigate in depth in this chapter. Police records have the reputation of carrying the weight of the record makers, being influenced by how much the police want to tell and how they perceive the crime/criminal (Sutherland and Van Vechten 1934; May 2001). Is it right to assume that the rest of the calls were termed bogus upon investigation because they were bogus and not because (a) perhaps the police either wasn’t interested in investigating them; or (b) it intimidated the petitioners into withdrawing their complaints; or (c) the police does not entertain calls from certain areas of Delhi as it is assumed that the residents of the area ‘always lie’? For example, call takers in the Delhi Police considered ‘eve teasing’ calls from poorer areas of the city not as authentic calls but as a way of women teaching their men a lesson. Rohit, one of the team members of the DMD and the one in the HQ responsible for plotting the maps based on the data in the Green Diary, was of the firm conviction that very few people actually call the number 100 in a real emergency. He believed, just like Ram Manohar, that the high volume of fake or bogus calls is because of easy access, because the number 100 is toll free. If the ‘100 number’ is chargeable, he said, ‘we would reduce the call numbers to half’. The call takers were of the opinion that people call 100 because the police are their first point of contact with the Sarkar (government). They dial the number even when the matter is not related to policing at all. “People call for traffic jams and potholes and petty quarrels between them. It is not our job to fix them but when a call is received, we have to send someone,” said Rakhee from the dispatch

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team on the fourth floor whose job it is to assign Police Control Room (PCR) vehicles to crime locations. Rakhee was angry with the police for not providing her and other members of the dispatch team with better infrastructure and human resources to handle the daily barrage of calls they had to handle. However, she was angrier at the people who called for considering the police unimportant enough to come fix their potholes. It was they who were responsible for her unending busy day. Rohit also believes that while the calls might be real, the incident descriptions are inflated. According to him, no one calls for just a missing wallet, it is always a missing wallet with a large amount of cash in it. “You ask me why, it is because these people believe that the police would never be interested in small cases. They want to inflate the problem to attract our attention.” Rohit is a young man, around 25 years of age, who had been recently recruited as a constable in the Delhi Police. He comes from an agricultural family in Haryana (a neighbouring state of Delhi) and took a job in the police, like many others, for the high social status that such a job promised. “Police jobs are powerful. Now I will get better rishta (proposals) in marriage and can even demand a greater dowry. Everyone in my village wants to be in Delhi Police,” he said. As a matter of fact, most officers in Delhi Police belong to Haryana, with a majority of them belonging to the dominant agricultural castes of the state; they all want a leg up the social mobility ladder via a job in the police force. They hold similar prejudices against the marginalised castes and religions as other upper castes of the city (Narayan 2020). According to the Dispatchers and even Call Takers I spoke to during my visits to HQ, most of the rape/‘eve-teasing’ calls are a way for women to teach men a lesson. “Whenever a couple fights, the woman calls the police accusing the guy of rape or eve-teasing to scare him but when we actually reach the spot, it is nothing. Most of the time, the woman would ask for forgiveness or ask to rafa-dafa (a Hindi phrase to let the matter die) the case,” Rakhee told me as she worked non-stop with a broken microphone and an absent team-mate assigning cars to call locations. She added that most calls during the day are from ‘slum type areas’, especially in the aftermath of quarrels and brawls that constantly erupt in these areas (as opposed to ‘posh’ areas; she gave the example of ‘South Delhi’, the part of Delhi that housed its rich and influential, saying that people from here seldom called the number 100 and that brawls never occurred there). Most of the time, she said to me, the slum residents resolve their quarrels on their own, which meant a waste of time for the police. “These people

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just want to scare the other party by calling the police, there is really no police case there,” she said. This dichotomy in how victims are perceived by the Delhi Police reminded one of Khanikar (2018)’s concept of “quasi citizens” in her work ‘State, violence and legitimacy in India’. Here she describes the “indurated divisions based on caste, class, religion or sex, most often several of these signifiers gelling together to give the (un)desired identity of a given person (like a Dalit Muslim, or a Muslim slum dweller, or a Dalit slum dweller)” (p. 87). Quasi citizens are also often referred to as “‘the marginal’, ‘slum dweller’ or ‘Bangladeshi 1 ’” (ibid.). Even for the police, who ‘sides’ with the “solid citizen” (another term borrowed by Khanikar (ibid.) which refers to someone on the other end of the class, caste, religion or sex spectrum, the actions, troubles and the complaints of the quasi citizens are always suspect. Whether the ‘suspect intentions’ of quasi citizens influence how the police attend to their calls is something we will examine later. However, it must be noted at this point, as Khanikar explains, that the marginalised sections use the police capacity for violence and its perceived authority to restore order in their day-to-day living. Affirming Rakhee’s observations to me, Khanikar (2018) narrates how Ruby uses the police to discipline her husband—not by filing a case with the police or by going through any of the formal procedures for registering a case of domestic violence. “What she wanted was the police to informally keep her husband in custody for a few days, which would have the effect of intimidating him for a while. While anyone else doing this would be an abductor, she believed that the police could do it without trouble” (p. 113). The police in cases like that of Ruby is accepted more as a “‘strongman’ and this form of the state becomes a close ally of the people” (p. 115). According to Khanikar, it is common knowledge that the police is not a neutral arbiter; divisions of caste, class, gender, communal and religion influence the way it works. In such a situation, the marginalised find spaces to reverse this power equation. She postulates that this not only makes the “coercive state more bearable” (ibid., 110), but also makes its most deplorable qualities, violence and corruption, a resource for the people at the margins.

1 Literally a citizen of Bangladesh but used pejoratively to denote an illegal immigrant in India.

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The Birthplace of the Green Diary: The Dispatch Command Room Stacks of green paper line the DMD everywhere. They are sometimes used to clean the samosa chutney that did not stay on its plate off the table top. You can use them as scrap to note down something important or use them as plates to keep the fritters. They are omnipresent and ready for any task. These are the Green Diaries, useless after they have been plotted on maps and sent across Delhi Police officers and superintendents. Their life ends in DMD but they are born a floor above. The Dispatch Floor’s supervisory centre, also called the Dispatch Command Room, is located on the fourth floor; this is where the Green Diaries are made every day. All of them are a compilation of the day’s crime events, this room could be seen as the original crime analysis centre of the PHQ, much before mapping and algorithmic analysis took over. Interestingly enough, the analysis goes on still. Two officers in the room divide particular instances of crime according to the time of day. This is how, they told me, they could conclude that snatching incidents take place mostly in the mornings, from 7 to 10AM, when mothers are out and about dropping their kids at school. Murders, they say, take place in the wee hours of the morning, from 2 to 5AM. This insight has helped the police to arrange their officers accordingly. This time-wise analysis is done even today and recorded in a green diary which is sent to the commissioner of police on a daily basis. One more diary, containing the break-up of crime timings and their details, is also prepared along with another that contains the monitoring data of all the PCR cars in the capital. For our present purposes, however, we will focus on the green diary that contains details on the heinous crimes of snatching, rape, eve teasing and robbery prepared for the DMD on a daily basis. Halaat is an Urdu word which broadly means ‘condition’ or ‘status’ (condition of a person, status of a situation or event). The diary itself is a Microsoft Office Word document created on a computer dedicated to recording the Halaat . The diary carries details of case number, caller’s phone number, time of call, time when the PCR van reached the incident location, time when it left, location of the crime, time the police van spent there, and the incident description or the Halaat report. The Halaat report describes the details of the incident as it happened at the crime scene, reported by the officers in the PCR van. They relay information such as the names of the victims, action taken, including the name of the investigating officer (IO) who is

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sent from the local police station. The Dial 100 call centre receives around 20,000–30,000 every day. Add to it the calls received on the 21 helplines that are in the headquarters, and the call count goes up to 60,000 calls a day. The Diaries are recorded for these calls for a timeline of 00:00 to 23:59 hours of that particular day. The Dispatch Command room, a large room with computer screens lining its walls and a huge table in the centre, has a call monitoring system on the right where an officer monitors all the high priority calls that land in the Dial 100 call centre. The head(s) of the Dispatch floor sit(s) on the table and manage the administrative duty of the floor. The computer screens on the sides are manned by various officers tasked for compiling the various Green Diaries of the day. The console for the mapping Green Diary is on the right side from the entrance, set up between the ‘server’ computer and the call monitoring computer. The server computer is the only one with the PA 100 software installed and the only one where officers can follow the journey of an emergency call to the number 100. Typically, two officers are assigned for every green diary. As soon as a call taker records a call as high priority (presses the H button on their screen), it appears on the call monitoring system, their serial numbers forming a queue on the right-hand side of the screen; calls that have been read are coded as blue while ones that have not are coded as black. The officer manning this desk checks the call descriptions known as ‘halaat ’ reports, sourced from the server computer, of every call to make sure they match with the categorisation provided by the call takers. The desk officer copies the numbers of the heinous or ‘H’ cases on a small sheaf of paper, like a journalist’s notebook made out of waste paper lying in the room, and sends them to the server computer. If the halaat report and the call categorisation are the same, the case number is circled and transferred to the computer where the Green Diary is made. A Word Doc with the Green Diary is kept open on this computer at all times, continuously recording heinous call case details as and when they come in. Case numbers that don’t match with their halaat reports are struck off. Case numbers that are confusing are checked with the officers in the room and if need be, with Dispatch and even the call takers. Not every halaat report is generated after visiting the scene of the crime. For instance, while at the PHQ, I found out that a number of PCR vans were found not visiting several cases they were assigned to when the DMD conducted a random investigation. A police constable at a station in South

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Delhi also told me that halaat reports could also be written by calling the complainant because there is not always time to visit every case assigned to them. The computer system next to the server computer is where the consolidated diary is made for the Commissioner of Police (CP) to give him the bird’s eye view picture of crime every day in the capital. This diary carries brief descriptions, since the CP does not have time to read everything, of all heinous crimes along with calls from 21 other helplines situated at the Delhi Police Headquarters. Right next to it is the computer through which PCR vans are monitored via GPS monitors in the vehicles. At this system is also made another diary containing the categories of snatching, robbery, murder, and attempt to murder. It contains the essential details of the cases, most importantly of the time of the crimes so that they could be slotted into four time zones—T1, T2, T3, T4 or from 6 AM–12PM, 12PM–6PM, 6PM–12AM and 12AM to 6AM—in order to understand the distribution of crime at different time intervals and the locations that are prone to specific kinds of crime. It also provides information such as most frequent items taken away in snatching or the kind of property most commonly stolen in robbery-related crimes in the capital. As mentioned earlier in this section, even after the introduction of CMAPS, which allows such kind of analysis to be done faster, this manual analysis and documentation continues to happen. The shift working on the Green Diary has to write the complete description of the calls in the diary and most importantly record the final action taken in the case, using acronyms such as FIR or first information report; LPA or local police action, signifying that the local police has taken over the case; or simply Pending or Filed. Filed cases could mean anything from a bogus case to a ‘compromise’ where the police officers make the conflicting parties come to some sort of an agreement that is beneficial to all. If the shift ends but the final action on a case is not known, it is passed over to the next shift, i.e., pending. A list of such pending cases is made with the details of the incident, CRDD number or simply the case number and the halaat report and given to the next shift that takes over and completes the work. This is called shift handover. Handover of pending cases is done at the end of every shift from the outgoing to incoming shift. It is good practice to relieve the outgoing shift of the caseload but this courtesy is not extended for complicated cases. For such cases, few members of the incoming shift wait and complete the case details before leaving for the day.

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From 8:00 PM to 12:00 AM all the cases entered in the green diary are kept in the pending state because a final decision on them is difficult to be taken during the night. The day (of recording the diary) ends at 23:59, that is when the shift on duty calls the District Police Control Room and Police Stations to verify pending cases. Though this task goes on during the day, it is only at the end of the day that the green diary is finally checked and revised and made into its final form. If the final decision on pending cases does not come before the diary is sent for mapping, they are noted in the Green Diary with their status as ‘pending’ and sent for mapping. While these calls are followed and their status is noted in the CRDD form, it is not updated in the Green Diary. So, in DMD, they are mapped exactly like that, without verification of the outcome or the crime. One would never know, just by looking at DMD maps, if the calls coming in from 8:00 PM to 12:00 AM were actually what they claimed they were. The diary is submitted to the senior officers at 6:00 AM so that they can have a look at the cases that happened the previous day. Although the senior officers are informed about important cases throughout the day, the green diary gives them a comprehensive picture of crime in the city. It also acts as a record of the most important crimes of the previous day. The diary is also sent to the DMD at 6 AM so that the details can be mapped and the maps are then sent to the senior officers of the Police.

The Call Receivers---Dial 100 Call Centre The Dial 100 call centre is situated on the third floor of the Delhi Police Headquarters in a large hall right next to the Digital Mapping Division. The right-hand side of the hall (called the floor) is the call centre, while on the left side are all the various helplines run by the Delhi Police. A command room that supervises the working of the call centre and helplines separates these two halves. 40 call takers in any shift attend to distress calls from inhabitants of the capital. The shifts run from 8AM to 2PM, from 2 to 8 PM and from 8PM to 8AM. Every call taker takes anywhere from 400 to 500 calls every day. The last shift runs through the night to avoid logistical difficulties related to commuting, i.e., so that the call takers do not have to commute in the middle of the night when the city roads are not safe in the least. Indeed, the odd hours at night when this centre continues to

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work would make it imperative for Delhi Police HQ to arrange transportation for call takers, especially the women. I couldn’t help thinking that it speaks volumes about the situation of the safety of women in the city when the Police HQ officers themselves consider it a tall order to ensure the safety of the women officers during late night shifts. It also indicates their belief that crimes against women can be controlled by eradicating women from those spaces and times where/when crimes could be committed, i.e., public spaces after dark. It was difficult for me to obtain permission for interviewing call takers because the Dial 100 call centre is one of the most critical operations in the PHQ. Officers feared that I might disrupt the everyday routine of the centre resulting in an unnecessary pending case queue. They were also concerned that I would be privy to calls that might turn out to be crucial evidence later—the officers reminded me that the 16 December rape case investigation started with a phone call to the emergency response number—in which case I could not be writing or talking about it with the outside world. After much back and forth I was allowed three days in the Dial 100 call centre; I was told I had to go in without a notebook or even a pen and ask questions only during breaks. I was supervised incessantly and told to keep my questions limited to the study I was undertaking. The call takers, mostly women,2 on the other hand, were ready to chat. They welcomed my presence as a distraction from the monotony of their work. Though they understood that they had to hide their enthusiasm from their supervisor, it did not stop them from familiarising me with minute details of the first point of contact of Delhi’s emergency centre. They were interested in knowing my marital status, how much money I made (and were amazed that the government gave money for conducting research) and if I had kids. They explained that the ‘relaxed’ job profile of the call takers as compared to the field police officers is what attracted women officers here. Management also preferred women because they were easier to train. A ‘relaxed’ job profile meant a weekly off, strictly eight hours on a shift and an ‘air conditioned office’, which was a boon in the Delhi heat. They got a weekly off, something unheard of in the police, when their shift changed from night to day. In other words, they 2 The Dial 100 call centre was mostly populated by women call takers and I ended up interviewing only women. The men were few and were reluctant to speak although I could observe that they were courteous towards their women colleagues and offered to help them by covering their shifts and navigating tricky calls for them.

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got done in the morning with the night shift and had to only report the next morning for the day shift. ‘You understand that as women we need to look after our house as well, and this is like a regular job, with structured timings,’ one of the women call takers explained to me. Saroj, another call taker I chatted with at length, told me that a day in the life of a field officer could stretch to 18 hours a day without any weekly offs. They could be posted in the heat or the rain and had to fend for themselves in those punishing circumstances. ‘My mother in law would go ballistic if I had to work like that,’ she laughed. The primary task of a call taker is to attend to the incoming calls on the Dial 100 call centre, note down the details on a standardised form and pass the information to dispatch so that an emergency vehicle can be sent to the location of the call. Before the system was automated, the details were noted down on paper forms, where the call takers recorded such details as the name of the caller, address, time of call, location of incident and details of the incident. The form was then passed to the dispatch team who sent a PCR vehicle to the incident location. From the location, the officers relayed information about the incident, and put together the halaat report as discussed earlier. Till the year 2007, this was done without the use of computers, software or much technology. In 2006–2007, however, as mentioned before, HCL technologies got the tender for operating the call centre and for managing its software and data. They designed the ‘PA 100’ software that automated the working of the call centre. The paper-based form that the centre used to record details of the crimes was thus turned into a digital one, called the CRDD form, short for Caller Recognition Daily Diary, that came pre-filled with the 130 categories in which an incident could be slotted, the address database of Delhi (however inadequate), and the police station database of the city, in the form of drop-down menus. The CRDD form was also connected with various other helplines, such as the ‘missing children’, ‘senior citizen’, women’s helpline, Hotline, Traffic, CATS ambulance, Indian Railways and anti-stalking helplines run by the police headquarters. The form also has space to note down details of the incident that cannot be accommodated in the drop-down crime categorisation menu. Once the call taker is done noting down the details in the form, it is instantly transferred to dispatch for them to send a vehicle to the location. Call takers have a maximum of three minutes, in which they have to locate the issue with the callers, find out their location and pass the details

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to the dispatch team that would send a PCR Van to their location. If they take more time than that, calls could queue up, increasing the time taken to send help to distressed callers. This can only be done ideally if the caller is able to report his/her address adequately and the crime is selfexplanatory. However, more often than not, this is not the case, as we will see later.

PA 100 Form As soon as the phone rings on the desk of a call taker, they greet the callers with “Namaskar, channel no xxx, main aapki kya sahayata kar sakta/ sakti hu” (Greetings, channel no xxx, how can I help you?). The calls are distributed into various lines called channels. There are 60 channels for the Dial 100 call centre and additionally there are 30 + 10 channels on standby. Every channel may be understood as a separate phone line to which a call is routed. The calls are simultaneously also directed to District Control Rooms (DCRs) to transfer the emergency notification to the appropriate police station. There are 14 DCRs in Delhi. An SMS with call details is also sent to the phablets of the PCR van officers, but it is of no use because these phablets are never charged and most officers cite training issues in operating them on the field. As soon as they attend the call, the automated form, or the PA 100 form as it is called, opens on the computer screen in front of the call taker. The form comes with an auto-generated unique identification number for the complaint called the CLI CPCR DD number or just the ‘CR DD number’—a mix of a serial number with the date of the call. Repeat calls from the same phone number are tagged with the same number. The phone numbers of people who call the Dial 100 call centre are saved for a period of three months in the police database, unless their calls are entered in the Green Diaries, where they remain forever. The phone number of the caller and the address on which the number is registered is also recorded automatically in the CRDD form. On its left-hand side is the searchable database of addresses—the same address database of 6 lakh people that the DMD scored from the NIC—and the police station database which comes with a sub database of areas that belong to a particular police station jurisdiction. For example, if one clicks on Vasant Kunj Police Station, another drop-down box would be populated containing the names of all the areas that fall under the jurisdiction of Vasant Kunj Police Station. The left-hand side also contains check

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boxes for various helplines located in the Police Headquarters such as the Senior Citizen Helpline, Traffic, Foreign National helpline, Women, State Ambulance Service and Fire. Some of the helplines are auto-checked according to the category of crime selected; for example, for an accident case, the helpline box of Centralised Accident and Trauma Service ambulance or ‘CATS’ (an ambulance service started by the Delhi Government since 1991) would be auto-checked. Call takers can also select other helplines according to the situation. Lampland and Star (2009) argue that standardised forms are the very essence of bureaucratic action. Citing Laurent Thévenot (1984), they claim that investment in forms was a cultural-historical project of deletion of content and residual categories. The CRDD form in the PA 100 software is also a standardised form that allows only 130 categories of crime, along with one miscellaneous section to be entered in the police records. No matter what happens, the incident must be accommodated in one of these categories. In actuality however, a crime might not fit in a single category because of multiple events; even in such cases, the call takers need to record it under one neat category. If the call taker is not entirely sure, they resort to sorting it under the ‘highest’ category crime while the rest of the information is noted in the case notes section of the form. The inclusion of standardised categories thus works as a barrier in recording the actual details of the crime because of the need for sorting only according to the required categories. Lampland and Star further argue that the choices given in the standardised forms are moral choices and reflect those adopted by the society at large. For example, they argue that partnership choices in most forms are either ‘married’ or ‘single’, which excludes so many other ways in which human beings come together. Between ‘Male’ and ‘Female’ as gender choices, they ask, ‘Where do transsexuals go?’ (ibid., 8). In the CRDD form as well, the category of crime when the husband beats the wife is clearly noted as ‘domestic violence’; however, where does one sort a crime when the wife and/or her family member beats the husband (which, rued one female call taker, was happening quite frequently these days). Call takers said that they put such a call description in the category of ‘quarrel’, with an event description in the case notes section. They did not feel that a wife beating the husband should be categorised as domestic violence because the category did not reflect the nature of the crime adequately. However, they also added, ‘Women shouldn’t do such a thing.

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They should respect their husbands, this is not right,’ indicating to me the umbrella of patriarchy that they eventually functioned under. I wondered if this was the reason why they did not raise their voices against the harassment they received on the calls where men asked them for sexual favours or passed lewd comments. Did they believe in the adage that “men would be men”? Is this why the police blamed the women for instances of sexual harassment? There is no cell for the women officers to report sexual harassment to; mostly they are just advised to ignore such callers. Women call takers save the numbers of such harassers on their machines and just stop answering calls from them. For Saroj, one caller repeatedly landed on her channel and just stopped speaking when she picked up the call, only breathing heavily into the receiver. She showed me a list of such callers’ numbers, numbers she had saved on the answering instrument, which she did not answer. She also agreed that the best way to deal with such callers was to ignore them. Supervisors routinely walk the floor assisting the call takers when they get stuck in finding an address or making a decision about how to categorise the incident. However, they are not the most helpful lot and might even penalise call takers who routinely ask questions. I found the supervisors on the call centre floor to be suspicious of my presence among the call takers. They only allowed me inside because of the DCP’s permission, however, they were not going to allow me to go around freely in the call centre. They wanted the call takers to concentrate on their calls rather than chit-chat with me. I concluded that the supervisors are not very trusting of their workers either; they were more like taskmasters than helpful seniors. The call takers were more comfortable asking their senior counterparts for advice rather than approaching supervisors. As a matter of fact, a work shift in the call centre felt like a close-knit family session. They shared each other’s workload during leaves of absence, covered for each other when they took a break and helped each other through a tricky call. Though call takers had to fill a pre-formatted form if they took a break of more than 10 minutes, they usually skipped doing this and assigned their desks to their friends while they were gone. During my time on the floor, I learnt that call takers do not undergo any special training to be able to do their job; they go through just a seven-day orientation at a training centre in the northern part of the city, where they are taught the working of the ‘PA 100’ software on which the Call Centre runs. They are expected to be sympathetic, and to listen to

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the callers carefully, and understand their problem with as much courtesy as possible. They work in rotating shifts, and eight hours a day, seven days a week in the call centre, which according to other senior police officers, is a ‘plum posting’ in the police because hours in the police stations could go up to 15–18 a day, seven days a week, most of which are spent outside in the heat and dust of Delhi. One day, as we were sitting and talking in the DMD, we could hear an elderly call taker shouting at a caller for not being clear with his problem. Ram Sevak from the DMD immediately went to the centre and checked him for inappropriate behaviour but later told me how it was not possible to be patient on the phone because of bad infrastructure, even with all the incentives of prize money being given to them. Both Ram Manohar and Ram Sevak also claimed that the call takers are not the best of the lot in the CPCR. As mentioned before in chapter on the setting up of the DMD, they felt that people above the age of 40 should not be allowed to become call takers as it is a very important job and demands mental agility and concentration from the call taker. Both Sevak and Manohar felt that over the age of 40, a person is more involved with family issues, particularly ageing parents and growing children. Therefore, while their abilities could be used for supervisory functions, they could not be expected to be as patient and focused as a call taking job demanded. Even in the particular case, where Ram Sevak ticked off the call taker in my presence, the call taker was aware that shouting on the phone would be marked on his performance evaluation and he would likely lose the prize for that month. However, long hours, faulty listening instruments and lack of any training on handling bewildered callers take a toll on how call takers ultimately handle calls coming in on the emergency number 100. During the festival of Holi in the year 2017, the call queue took three days to be cleared. Holi is a festival of colours in the northern part of India. Though it is a festival of fun and frolic, hooliganism and drunken misbehaviour have become regular features of the celebrations. Though the police are always busy on Holi attending to calls regarding brawls, quarrels and harassment, the ones on Holi 2017 kept them particularly busy. “There was no way we could have attended to all the calls that came in that day. We could only attend to some of the calls after a two to three days delay. Of course, it was futile but we had to close the call,” a dispatcher told me. Even on such high-pressure days, the call takers are expected to maintain their composure and help the callers with patience and courtesy. I

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remember visiting the Dial 100 call centre one evening only to see the call takers smiling and exchanging looks among each other. When I went to one of them and mouthed “what happened,” she simply put her machine on speaker mode and signalled to me to listen to what was going on. There was a man going on and on about his kids and how he was unable to take care of them now that he had divorced his wife. “I did it by mistake. I should not have let her go. Now there is no one to look after my kids. You don’t understand, I cannot do it by myself. Please help me in getting her back.” The call taker kept telling the man that this was an emergency call centre in the Police headquarters in Delhi and his problem did not fall under the purview of emergency response. But he insisted that she help him. “Arrey Sir, please aap samajhiye, hum aapki koi madad nahi kar sakte. Aapko apni wife se khud baat karni hogi,” said the call taker (Sir, please understand that we cannot help you. You will have to talk to your wife yourself). This went on for some time. The call taker kept trying to pacify the caller to no avail. In the middle of his monologue, when she was at her wits’ end about the situation, she put the call on mute and commented, “Yeh dekhiye, aisa ek call bhi aa jaaye to hamara saara performance bigad jaata hai. Inki problem badi hai, but hum kaise solve karein?” (Just look at this. Even one call like this can jeopardise an entire day’s performance. I know this guy has a huge problem but how can I solve it?”). Throughout the entire episode, the call taker was worried that if she disconnected the call, she would get negative feedback for it, which would in turn impact her performance. There is a separate cell on the fourth floor of the building which records feedback from randomly selected callers to assess the quality of the calls and the solutions provided to them. Good feedback plays a role in assessing the top caller of the month who gets a reward of Rs. 1000 with their photo displayed on the common notice board in the lobby of the CPCR. Queued calls, which would mean a longer connection time, or rude behaviour, failing to listen to the problem of the caller or less than exemplary service in any way, even if it is an infrastructural or connection problem, has a bearing on the performance of the call taker. For example, it is difficult to hear the voice of the caller on the telephone instruments provided in the Dial 100 because the software is outdated, but the call takers cannot let that become a hindrance to the quality of their work.

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Training and Resources As the Dial 100 call centre is the source of data for all kinds of crime mapping at the HQ, both Ram Manohar and Ram Sevak want better training, better people and more facilities for the call takers; but the proposal for such improvement has been pending for a long time. As a result, the performance of most call takers is not up to the mark, even with incentives. It is also not a permanent posting so call takers who have become good at their job, merely by practice, are routinely transferred, but new incoming officers are not provided any facilities for training, except on the job. Call takers often falter even in taking notes during calls. For example, once a call taker noted “caller ki death ho gayi hai” (the caller has died) and categorised it as a case of robbery. When the case was opened in dispatch, the dispatcher was in a fix—if the caller has died, who is calling? The apathy towards the call centre could be seen as a part of the general apathy towards any activity of the police that takes place outside a police station. As mentioned in the previous chapter, the DMD team laments being called “Bindu lagane wale” or the people who merely put dots (on a map of Delhi). This indicates that technology-led policing is not considered the most effective way of policing in Delhi.

Location of the Crime One of the most important questions a call taker needs answered is the location of the caller, which in turn is the location of the crime. This is where help would be sent; the more accurate the location, the faster a distress call can be addressed by the police. However, this becomes a complicated task for the call takers because of lack of any concrete database of locations in Delhi and no access to GPS locations of callers. To begin with, callers in distress are not always coherent and in their hurry to ask for help, are sometimes not in the state to answer particular questions posed by the call takers. A harried, overworked call taker is no help either. Many callers cry and shout for help on the phone, unable to comprehend that they have to describe their problem and their current location clearly and carefully in order for the police to come to their aid. The automated form of the PA 100 software on which the Dial 100 call centre runs gives the location of the place where the caller’s phone is registered but not the place from where they are calling; unless both these locations are the

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same, which is not the case in most situations, technology is no help in locating the caller. Most callers assume that their call lands at the nearest police station, where the police officers would know the surrounding areas well. This leads to added confusion. Callers sometimes ask call takers to send help to the “banyan tree” nearby, or “by the side of the bridge”. Saroj told me that very often callers have no idea that the calls go to a central call centre in ITO where the call takers do not know the local landmarks of the caller’s locality. It takes a while to make callers understand that they have not called the local police station but someone far away. Saroj mentioned how some people do not know their addresses even if they have been living at that place all their lives and an overwhelming majority of such people are women. She said women mostly stay inside the house and are not that aware of public space around their home or even the exact address of their location. In most cases, they would not even know the nearest police station by which the call taker could identify the caller’s address. In such cases the call takers have no choice but to ask the callers to call again once they know their address. They encourage them to ask passersby to tell them about the landmarks of their location, a police station or a well-known landmark to get a sense of their location. While call takers are almost on autopilot to ask for the location of the caller, because it is assumed that the caller is calling from the scene of the crime, it is worth making a distinction between the location of the caller and the location of the scene of the crime. The caller might be calling to report something they saw at some other location. Thus, it would be prudent to say that in many cases, and we certainly do not know how many such calls come in at the Dial 100 call centre, the location of the caller is not always the scene of the crime and that distinction has been lost in the designing of the PA 100 software. For example, in one of the cases, a woman’s family called 100 to report the death of their daughter due to medical negligence which had occurred in a hospital although the mother was calling from her home. But because they called from their home and not the hospital, the location of crime was taken as their home address, indicating that the death happened at the girl’s home and not at the hospital. For a familiar location recognised by the call takers, they select the district/area/mohalla of the caller and map the call to that location. In the absence of a concrete address database, familiarity with the city, not necessarily programmed in the Dial 100 software, helps a lot when

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identifying the caller’s location. In case they are not familiar with the locality from where the call was made, they ask the caller for the nearest police station. The police station boundary layer and the (limited) address database offer the maximum help in identifying a caller’s location in the city. All the police stations in Delhi are listed in a drop-down menu in the right-hand corner of the CRDD form screen. The areas that fall under each police station are also listed in another drop-down menu under the police station menu. On selecting a specific police station, for example, Vasant Kunj, its encompassing areas, such as Mahipalpur and Kishangarh, which are not as upscale as Vasant Kunj, would also become populated in the drop-down menu under the police station bar. Call takers can then select the nearest place that fits the location of the callers according to the description they give them. In order to close the calls quickly, in case callers are not sure of their locations, the call takers simply enquire about the nearest police station and record this as the caller’s location. Though the DMD still plots its maps only after the locations are verified by the assigned PCR vans and Investigating Officers which considerably reduces the margin of error, this does often end up causing glaring errors in the CMAPS plotting (more on this later in the section on CMAPS). Call Takers can dial the callers back through the command room. They can also go to hear the calls from the call maintenance section where each call is recorded for future reference. However, this doesn’t work all the time, like in one of the cases where a female caller called shouting for help but disconnected the call without giving much information. The call taker went to the command room to call her back but she didn’t answer her phone and her call was lost, unable to be acted upon even when the police knew there was someone in trouble looking for their help. Ram Manohar says that getting access to callers’ real-time location would allow the HQ to help in such cases, but for the time being, this is not possible. Classification is material and symbolic (Bowker and Star 1999, 40), which therefore compels us to look into the physical aspect of all that makes classification possible. Faulty telephone instruments because of which call takers are not able to hear callers’ voices properly could lead to incorrect details being noted down for the case. The complicated nature of the process to remedy a mistake further ensures that those mistakes are never corrected. For example, in one of the cases mentioned earlier where the call taker noted wrongly (perhaps because he could not hear

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the caller clearly) that the ‘caller’ had died, this inaccurate piece of information might never be corrected at all. Reverting to the caller to fix the error would involve a tedious bureaucratic process in which the call taker would have to file paperwork to request that the caller be called back from the command room. By this time, the log would already have been made, which would now have to be changed. Further, the presiding inspector of the command room would be the one to call the caller back and record the details of the case, that is if the caller took the call at all. Such an elaborate option is mostly not exercised by the call takers because (a) it involves informing the authorities that an error had been made and (b) absence from their seat to go through the entire process would mean a long queue waiting for the call taker when they would return.

Proposals for Better Location Tracking The officials at DMD told me that a file by the Ministry of Home Affairs (MHA) proposing to make it mandatory for service providers to provide latitude and longitude coordinates for calls to 100 is pending at the Ministry of Electronics and IT (MeitY). DMD officials said that though the reason given for the file being stuck is the concern for privacy of citizens, in reality, it is the privacy of the politicians that is the cause of concern here. “If the permission for location tracking would be given to call takers, then it is a free for all, including ministers’ phones, they explained to me.” The complicated nature of Indian bureaucracy3 and its opacity means that requests could remain unattended for months without any explanation, a feature used by governments to delay projects that they have no intention of completing. In addition, telecom operators also don’t want to give such sensitive data to the police, said Ram Manohar. DMD officers said that they could ask the service providers to use the

3 Indian bureaucracy is a complicated system where each task involves the movement of the file through various departments without much accountability of the time taken at each step. A file could be stuck at a certain step because of various factors such as the non-availability of required information or due to some ethical/organisational problem with the proposal. Such stuck files are kept in limbo forever, with the officers working on such cases simply moving on to other matters without even bothering to inform the proposer to correct or add the requirements in the file. Bribery or influence from a higher up officer or a politician is a routine affair when trying to “move” the files from one department to the other. Refer to Matt Hull (2012) Government of Paper for more insights into Bureaucracy in the Indian subcontinent.

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‘Triangulation method’ for providing location details for basic (on smart) phones but it is a time-intensive exercise. It involves data being taken from three nearest phone towers to provide a location of the person’s mobile phone. Ram Manohar said that it would take five minutes or more for service providers to get hold of location data via triangulation for each call. Such an amount of time cannot be accommodated during calls in the Dial 100 call centre, however, as it would result in a large number of queued up calls. The only way therefore that they could get an accurate location is if the phones are fitted with a GPS connection and that data is sent live to the Dial 100 Call Centre. According to Ram Manohar, this is the only way for accurate plotting of the crime information. He said that if the Police want the CMAPS to function then the government needs to make the use of smartphones mandatory, even distribute them for free if needed. Also, it would need to make it mandatory for service providers to provide location information for emergency numbers. The wishes of the police officers at the DMD often sounded to me like something out of an episode of the hit Netflix show Black Mirror called “Hated in the Nation”, where hackers were able to kill people with the most hate tweets against them with the help of government surveillance data which was available for all to the last detail. While it could be argued that location information sourced via Global Positioning System (GPS) could help those stuck in problematic situations by allowing the police to reach them quickly, it would not be wrong to surmise that the same data may be misused by the state to overtly surveil its citizens. Without adequate laws and social mores to safeguard citizens in such matters, the latter would almost certainly be true. An example of function creep could be reports on how facial recognition systems,4 allowed by a Delhi High Court Order to be only used to look for missing children,

4 In 2017, Delhi High Court allowed Delhi Police to use facial recognition to find missing children. The accuracy and efficiency of this tech was dubious, so were its claims of finding 3000 missing kids. However, this tech was used in 2019–2020 to surveil protestors against the Citizenship Amendment Act (CAA) and the National Register of Citizens (NRC). For function creep of facial recognition read, “From Protests to Chai, Facial Recognition is creeping up on us” https://timesofindia.indiatimes.com/blogs/voi ces/from-protests-to-chai-facial-recognition-is-creeping-up-on-us/ accessed September 12, 2022. For CAA and NRC read What NRC + CAA mean for you https://indianexpress.com/ article/explained/explained-citizenship-amendment-act-nrc-caa-means-6180033/ accessed June 27, 2022.

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were used to curb protests during the anti-Citizenship Amendment Act (CAA) protests in Delhi in 2019–2020.5 Back in 2018 during my visits to the DMD, I remember Sarita, the officer working on the CMAPS software in C4i, telling me that the government was going to pass a proposal to make phone manufacturers compulsorily manufacture all phones with GPS. This would mandate even basic phones to have GPS functionality so that location data could be available for crime mapping. Another solution for accurate location mapping would be to provide an app on the personal digital assistant (PDA) devices to all investigating officers (IOs—who reach the crime site from the police station) and officers manning the PCR vans. Using these they could punch in the exact latitude–longitude coordinates of the crime scene on a Delhi map removing any possibility of ambiguity. This might be the most viable solution of all because, unlike the GPS route, it does not have the potential to infringe on anyone’s privacy. However, this proposal is stuck with the policemen who do not want to use the application in their personal digital assistant (PDA) devices citing training issues. However, the real issue, I was told, was the surveillance that came with using such an app. As we discussed earlier in the chapter, PCR van officers do not attend all the calls that land in their kitty. If the use of the location tracking app were to be made mandatory, whoever uses the app would have to actually visit the scene of the crime in order to punch in the location. Casual conversations with policemen and researchers who work on beat policing in Delhi revealed how some IOs update crime details on the phone without visiting the crime scene. This, according to them, is due to the heavy workload they have. Lower rung policemen work 14–15 hours a day seven days a week and more often than not have a full day investigating crimes in the city. They are also mostly not compensated for travel, increasing their already burdened financial systems. Therefore, for petty crimes, they mostly do not visit the crime scene but merely call the person who reported the crime to get further details. Even the PCR vans cut a deal with their dispatch teams so that they are assigned fewer cases during the day to decrease their workload. A random investigation 5 Delhi, UP Police use facial recognition tech at anti CAA protests, others may soon catch up https://www.indiatoday.in/india/story/delhi-up-police-use-facial-recognitiontech-at-anti-caa-protests-others-may-soon-catch-up-1647470-2020-02-18 accessed March 5, 2020.

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around July 2018 by the DMD revealed that many PCR vans did not get as many cases as others. On looking into this further, the DMD found out that some PCR van operators consistently received fewer cases than others, making it clear that their dispatch officer made sure their workload did not cross a certain threshold.

Dispatch Floor The dispatch floor is a cacophony of noises with all the operators simultaneously talking to their respective PCR vans on the microphone. The voices are grainy and the operators have to shout into the microphones to be heard. They have their own shorthand language so that they are able to communicate the most in the least amount of time. The calls that land in the Dial 100 call centre eventually have to be passed over to the dispatch floor on the fourth floor from where the PCR vans (emergency response vehicles) are sent to the scene of crime. Because the calls are distributed according to districts, the floor is designed such that different consoles, also called ‘NET’, represent the different districts of Delhi. When I visited the DMD, there were 11 cubicles, though Delhi recently added two more districts taking the count up to 13. These consoles are given code names for quick recognition such as ‘Charlie’ or ‘Eagle’ where each code name signifies a district. The dispatch floor has the same PA 100 software open on their systems so they can also access the CRDD forms that the call takers fill on the floor below. Apart from the access to the CRDD form with information filled by call takers, the screen on the dispatch floor also shows the pending cases (those that still need to be assigned PCR vans), as per priority, on the side of the screen. They are colour-coded to signify a range between the most urgent and low in priority. The highest priority is given to calls related to heinous crimes and usually coded a dark pink for easy recognition. The dispatch floor also sends the details of the calls related to heinous crimes to their Command Room (there is a separate one on the fourth floor for supervising the dispatch floor and preparing the day’s Green Diary). On the dispatch floor, first, the calls that are received at the Dial 100 call centre are diverted to their respective district consoles also called the ‘NET’. The districts selected by the call takers decide which ‘NET’ the calls are diverted to. There, an operator opens the CRDD form and reads out the description of the call along with the location. The operator can

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check a map with the real-time locations of the available cars in the area and assign them accordingly. However, the map is a heavy programme to load and run in the limited processing space that the computers on the Dispatch floor have. Rakhee informed me that while two operators are assigned to each dispatch console, more often than not, there is only one. One person has to update the halaat report and copy the details of the case (including a unique number that the dispatch team assigns to the case along with the case details in brief) in a physical log book while the other has to dispatch the cars to cases as soon as they can be sent. Most of the time, however, the same person does both jobs. Being perennially understaffed, the Dispatch Floor cannot wait for heavy programmes to load and delay their work. On the contrary, the operators become so well-versed with the locations of the cars and the general geography of the areas that they deal with on a daily basis that they hardly ever need to use any maps to check the location of the cars. Rakhee said that they have a mental map of the cars when they begin their shifts, according to the places they have already been sent. The PCR vehicles are, if not moving, parked at a specific location in each district every day, which the operators quickly learn with constant practice. Accordingly, they keep assigning the cars not on duty to new cases. The operators are in constant communication with the PCR car drivers to assess their location and decide on the cases they need to be assigned to. The incident is assigned to an available PCR car (each car is assigned a code name which is related to the code name of the district it is operating in), by manually reading out the call report to the officers in the PCR van on a handheld transmitter microphone. Rakhee said that initially they had a pedal-operated microphone but now they only have a heavy handoperated one. She said that while using that, one hand is always engaged in communicating with the PCR van operators, slowing down other work, especially operating the system keyboard and writing in the log book. Even though each officer in the PCR car has been given a phablet (a phone-cum-tablet device) on which an SMS of the incident could be sent, this is not done because most of the Phablets do not work or officers simply don’t check their messages soon enough. If this were done on a regular basis it would help because an SMS could be sent directly from the Dial 100 call centre containing the description of the call along with the name of the caller and their address and the location of crime. Phablets also discharge fairly quickly so charging them is another chore, especially on the field, indicating another link in the classification and infrastructure

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chain as mentioned before. It is also a surveillance tool according to the officers, because it can track their location at all times with the inbuilt GPS technology, therefore deliberately not used. So the call is communicated through the microphone—a typical announcement would consist of information about the location of the crime according to the description given by the caller. Its accuracy in helping the assigned vehicle reach the crime scene depends considerably on the dispatch and PCR van officers’ own knowledge of the geography of the city. Along with the location, the announcement also contains information about the kind of crime that has taken place, for example, ‘jhagda’ (quarrel) or rape or snatching or robbery, or if there is an unidentified body. No distinction is made in the announcement between an accident with injury and those without injury though an additional ambulance team is indeed sent to the incident location if injuries are reported. The PCR police officers along with the investigating officers from the police stations then have an additional role of admitting the injured to a hospital. Sometimes, the ambulance is unable to reach the location of crime, then the PCR car doubles up as the ambulance to ferry the injured to nearby hospitals. The language in the case notes of the PA 100 software (where the call takers take notes and a similar one where the dispatchers write down the halaat report) is as colloquial as it can be, full of acronyms that are commonly used among the officers. Hindi (in the English script) and English words (with tons of spelling mistakes) come together to make a mishmash of words that become comprehensible only to the people involved in the process. Looking at this vocabulary reminded me of the SMS lingo of young people, where letters are often replaced with numbers, where ‘People’ becomes ‘ppl’ and ‘forever’ becomes ‘4va’. Despite the fact that call takers, dispatchers and police officers are handy with the peculiar lingo that has been developed over time, at times, things can go awry if one set of dispatchers does not understand an expression or a particular way a word has been spelt by another set of call takers. In such a case, dispatchers have intra-office phone connections through which they call the call takers on the 3rd floor and ask for the meaning of a particular word or phrase. In contrast, the language in the Green Diary is formal and without any spelling mistakes. Matthew Hull (2003) describes such a presentation as bureaucratic writing, full of passive verbs and formal words that takes away any agentic discourse from the written material. The purpose is to present the document as a collective work of the entire organisation and

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not of any one person, making sure that no one person could be held responsible for its making. The purpose of the case notes is different, the agentic discourse being already present due to the software that logs the identity of the call taker/dispatcher. The notes are more for internal conversation, among peers, whereas the Green Diary is the final, official document. It is interesting to imagine how the call takers and dispatchers and even those who make the Green Diary—individuals who are not the most fluent in English—deal with software that is designed entirely in English and produce the Green Diary on a daily basis.

PCR Vans at the Crime Scene After the PCR van reaches the scene of crime it has to report the description of the incident and the action taken to the dispatch team back at the control room. This, as mentioned before, is called the ‘Halaat report’, a comprehensive report including details such as the name of the victim, and in the case of a woman, her husband or father’s name (every woman is suffixed by her father or husband’s name), the actual incident (for instance, was it really a rape or chedh chaad (teasing)?), the action taken by the police and the police officer from the local police station who took over the case. If the case is simply handed over to the local police, then the case is closed as ‘LPA’ or ‘local police action’. The halaat report includes a short description of the case which has only the prominent details. When there is a deluge of calls, it is important to prioritise them as early and as efficiently as possible. The first priority for dispatch goes to the calls related to heinous crimes. The dispatch officers are responsible for clearing the queues as early as possible or they get a warning from the supervisors in the command room to work faster. The queues could get massively long during festivals or other major events in the capital, as mentioned before, such as during festivals like Diwali or Holi.

Written Accountability A physical logbook of each call is maintained even though the logs are automatically kept in the PA 100 software that runs both in the Dial 100 call centre and Dispatch Floor. One of the dispatch operators explained to me how the maintenance of the logbook is no longer as cumbersome as it was before when there were no computers and they had to write each and every detail in the logbook. Now they just write the case number along

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with the caller’s phone number. They also assign a serial number to all the calls in order of their dispatch that is then written in the comments section of the PA 100 software for handy reference. This helps them identify the case especially when it is in the queue of dispatches. The books are maintained for 1–2 years as official records before being destroyed. The officers refer to the log book as their ‘backup’ because in case of a controversy or if the systems break down, they can always refer to the physical records to vindicate themselves. One officer said to me that physical records such as the log book are and will remain a permanent fixture of the police bureaucracy. “Yeh to yahi rahega, chahe yeh (referring to the computer) kitne aa jaaye” (Paper will remain no matter how many computers arrive) indicating the importance of matters recorded on paper in any organisational setting. Even with PCRs, it is more important that their case notes reflect that they indeed reached the crime scene than for them to actually do so. Once I noticed an officer chatting with a constable on a feedback email thread he had received regarding his action on one of the calls. The caller had asked for a local DJ at a wedding nearby to be shut down because the music was being played after 10 PM, the official deadline for loud music in the city to be shut down. The constable had received this call from the dispatcher in the PCR van he was operating in the night and had gone and got the DJ to shut down. However, the arrival of the police or the action taken was not notified to the caller. At this point, the inspector, who was the head of the command room and the head of the shift when the call had been received, was coaching the constable on how to save himself from enquiry from senior officers. The Inspector asked the constable if he had written down every detail of the day’s events in the logbook and if the same were conveyed in the ‘Halaat ’ report of the call. He emphasised on every detail of the case being written down. The constable looked a little perplexed; either he had not confirmed the halaat to the dispatch team or he wasn’t sure they had noted it down. The officer explained in response that if the constable had a proper record of the events then he should attach this to his reply to the senior officer; that it would act as evidence in the constable’s favour. He advised the constable to word his reply in such a way as to explain that there had been a number of weddings in the area, that the PCR car had gone to a number of them to get the DJs to shut down the music, and by the time they reached the caller’s venue, it was 10–15 mins into the call. They had got the music

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successfully shut down but had not been able to inform the caller about it. The proof was in the written reports. The officers in the PCR vans too maintain a logbook where they note down the description of the call as told to them by the dispatcher. Throughout my time at the DMD, I also noticed that GPS logs were checked only randomly, that too if there was a particular complaint, and that written records were trusted more. Mathew Hull (2012, 7) explains that the practice of recording everything that took place in an organisation on paper is an extension of the colonial government. The directors of the East India Company had little trust in their agents in India and therefore, made sure that every little act was recorded in writing so that it could be checked and audited in retrospect, Hull (2003, 294). When actions are not performed through writing, they are supposed to be autographically documented. A person who is an agent but not an author, who causes things to happen without writing (or being written about), is a kind of witch from the bureaucratic point of view. Official procedures of file production are designed to determine agency (and therefore responsibility) absolutely by comprehensive documentation of authorship. Through autographic writing the actions of individuals within an organisation are made visible.

Categorisation of Crime As mentioned earlier, every call that lands in the Dial 100 call centre is categorised according to the categories pre-loaded in the CRDD form. According to Gillespie et al., data needs to be “readied” before an algorithm processes them, and categorisation is a powerful tool for making data “algorithm-ready” (Gillespie 2014). The 130 categories which were created for the CRDD form are the result of a combination of factors such as the Punjab Police Act of 1861, past calls, police registers, IPC and CrPC, as well as the officers’ own knowledge of the city and the way it works. While preparing the software, senior officers of the Delhi Police Headquarters discussed the most important crimes related to which calls were made and then came to a decision about which of these should be added in the list of categories of crime. As Bowker and Star (1999) would argue, each classification system is tied to a particular set of coding practices. The coding of all that happens in the city of Delhi (with regard to crime) was coded into 130 categories. A miscellaneous category enables the call takers to record a crime-related call

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without disturbing the existing coding system. The authors describe two kinds of classificatory systems, Aristotelian and Prototypical, the former being a binary classification system while the latter constitutes the fuzziness that people experience while slotting things into neat categories. They say that in standardised forms (such as the PA 100 software), people refer to certain assigned codes (such as the crime categories the call takers have) and try to provide descriptions to them. This slotting is expected to be fuss-free (Aristotelian) but in practice is more Prototypical than ever. There are “cognitive and perceptual uncertainties” that the people in charge of categorising feel. Call takers are the first point of contact in case of Dial 100 calls and it is they who decide the categorisation of the calls, which could be again changed, according to PCR investigations, by the Dispatch team. Both these teams discuss and decide among themselves the best category a call should be assigned. These practices do not show in the final record (Green Diary), where there is no reference to the actual process. “What the categories are, what belongs in a category, and who decides how to implement these categories in practice, are all powerful assertions on how things are and are supposed to be” (Bowker and Star 1999). When calls are being categorised, snatching becomes robbery if there are more than two people involved and/or if there is a weapon involved. In one such incident of confused categorisation, a shopkeeper called and said that two people came to his shop on a bike and took away two cans of Patanjali ghee. The call taker categorised his call as ‘snatching’. However, on further investigation by the PCR officers, it was found that the two men came to the shop, asked for two 5 kg cans of Patanjali ghee. When the shopkeeper gave them the cans, they asked for a can of Chyawanprash. When the shopkeeper turned to get the Chyawanprash, the men ran away with the ghee on their bikes. Was this robbery or snatching then? After all, the men had robbed the shopkeeper in broad daylight! However, it was categorised as ‘snatching’ finally because after much discussion in the Dispatch Floor, it was decided that the merchandise was taken away (even though force was not used) from the hands of the shopkeeper. If two or more kinds of crimes occur in the same event, its category is decided according to the ‘higher’ priority crime committed. For example, murder takes precedence over every other kind of crime, so if there is a robbery and murder in the same call, it gets categorised as murder. The call takers categorise the calls as the nearest crime or what is most probable according to the details given by the caller. For instance, most

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conflicts that arise between neighbours, or people in the same locality, or among friends, are categorised as quarrels. A number of hit-and-run cases are categorised as accidents, though accidents are differentiated on the basis of those resulting in injuries and those not doing so. On more than one occasion, I noticed call takers put their calls on hold to discuss how they could be classified. These discussions are furtive because supervisors are watching and also because they need to close the call as early as possible and no detailed discussion is possible. There is always an experienced person in the shift on whose wisdom everyone relies. If there is no answer from the wisdom tree then either the calls go into the miscellaneous category or to the closest category that the call taker can discern based on the description of the caller. As soon as the call is categorised as one of the four heinous crimes, it automatically gets the code ‘H’ signifying High Priority. High Priority calls are attended to first by the dispatch team on the fourth floor of the Police Headquarters that handles the job of assigning PCR vans to incidents; they are also closely monitored by the Command Room on both the third and fourth floors. The dispatch operators send the details of the H calls immediately to their command room so that they may be added to the Green Diary at the earliest. The categorisation of the crimes evidently does not follow any standard rules; I observed this as I sat in while some calls were categorised as snatching by the call takers in the Dial 100 call centre on a particular day in March. Out of the 22 calls that I checked, only 10 were found to indeed be cases of snatching while the other 12 were found to be either fake or later categorised as a different crime, such as robbery. Out of the 12 which were finally categorised as cases of snatching, three calls were from the same number for the same crime. Even though repeated calls from the same number are tagged together in the same CRDD number (there is a provision for tagging in the software), these calls were assigned separate numbers. Even later, in the halaat report, the same call was not categorised as snatching because the investigating officers found out that ‘known people’ had taken the money from the victim. I learnt, on enquiring, that the ‘unsaid’ rules about the category of ‘snatching’ claim that if people known/familiar to the victims take something from them, it cannot be termed as ‘snatching’. For any crime to be called a ‘snatching’ crime, the material possessions of the victims needed to be taken away by people unknown to them. Interestingly, when I checked the same case in the actual police station in North East Delhi, the records register claimed

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that the case was of a mere kahasuni (argument) and therefore it wasn’t a matter of ‘snatching’. I must add that I could only access the record of this particular incident after much persuasion in the police station. Another call for snatching was converted to robbery and still not included in the Green Diary for mapping. Once again according to the agreed upon ‘unsaid’ rules followed in the HQ, robbery is when more than two people take away the victim’s material possessions and/or there is a weapon involved. The case met neither of these conditions in the case notes nor in the halaat report but still this case was converted into a case of robbery. Also, robbery cases are included in the Green Diary but this one wasn’t. No one in the DMD could answer when I asked them why. A call from a police station in South Delhi was reported as snatching and included in the Green Diary for mapping. The case notes said that the call taker had categorised it as snatching because a woman’s gold chain had been snatched in the morning when she went to drop her kid at school. The Halaat report also corroborated the call. However, when I checked the IO report at the police station, I found out that no ‘snatching’ had in fact occurred. It was clear to me after my time observing the way things worked at the HQ that not only are crime numbers provided by the Delhi Police not a reflection of reality, but the categorisation of crimes could be the polar opposite of the actual incident.

Implications As I had mentioned in Chapter 2, one of the team members, Punit, informed me about the ‘right’ amount of crime needed to ensure that a police station remains viable. Too high, and it might be split into two to handle the workload. Too less, and it might be scrapped entirely or merged with another district, ending its life as an independent station. The police officers in every than a therefore work very carefully to keep themselves in the middle lane. That crime records are discretionary and cannot be more emphasised enough. Police officers need to believe you to make sure you are not lying. The quantum of crime needs to adhere to their standards. They need to like you as well to believe you. Ask this of a woman who went to a police station to report a sexual harassment crime dressed in what are considered ‘skimpy’ clothes by people. Everyone believed she had it coming. Kislaya Prasad (2013) in his research on Indian reported crime data (reported by the victim in independent surveys) and recorded crime

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(recorded by the police at the police station) noted that there is a vast difference of crime numbers in both these instances. A number of victims do not report their crimes to the police and if they do, the police may not record it for various reasons. Police’s discretion in reaching the scene of the crime revealed by the PCR records also tells us of the subjective nature of the Green Diary. Not all calls are attended to and not all halaat reports are collected by actually visiting the crime scene. Police officers at the PHQ often complained about the people living in slums for using the police for fake cases as a covert justification for not attending to their calls (though they claimed that a PCR van was sent whenever there was a call). Satyogi (2019)’s argument offers evidence as well as a counter narrative to this theory about police discretion where she argues that the police filter victim complaints in cases of domestic violence through the lens of what would hold in the court of law. Thus, she demonstrates that the police does change the victim’s version of a complaint, but on the other hand, she argues that this is not done only to harass a victim, but also to make sure that narratives hold up in a legal setting. As I have mentioned in the first chapter, all the calls received at the Dial 100 call centre (and ultimately those recorded in the Green Diary) can be used as legal evidence, making it necessary for the officers to change the language to one that is conducive to the legal system. When marking an event, any event, as crime, algorithmic policing should take note of such human endeavours in the police and their impact on categorisation. Categorisation is not absolute but, as in this case, is dependent on what would hold in a court of law. Such discretionary powers of the police along with their social conditioning often lead to bias creeping into categorisations and records. Marda and Narayan (2020) explain the three kinds of bias that creep into crime data in the Delhi Police as being historical, representational and measurement bias. Historical bias, as we have learnt from the previous chapter, is about the police’s bias towards the marginalised when it came to pinning accountability of crime. This is a tradition that has continued from the times of the colonial police and has its roots in the caste system in India (Piliavsky, 2015). As far as representational bias is concerned, the over-representation of the marginalised classes in the crime records across police stations in India is a fallout of historical bias. Finally, measurement bias is because of the faulty database of addresses. Shanty settlements cannot be correctly encoded as location data because they are not part

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of the planned parts of the city, as compared to the so-called posh localities (Marda and Narayan 2020). The historical bias of police officers is evident when the ones engaged in mapping regularly portrayed Muslims and Bangladeshi immigrants as criminals while talking to me. Prison data6 across the country is proof of representational bias that is a direct fallout of historical bias. Shanty settlements suffer most from measurement bias because of the non- standard nature of their addresses, which often get wrongly geocoded by the call takers or map plotters. In conclusion, it is important to remember that throughout this chapter I was not looking at the standard procedures laid down to make the Green Diary, but the pragmatic workarounds of those procedures. This is in line with Bowker and Star (1999)’s methodology of looking at the everyday practices that help in applying any classification system. Bowker and Stay in turn drew their methodology from Latour’s Laboratory Life (and in general, science studies) where Latour claimed that one can either look at what the scientists say they were doing or what they were actually doing (as cited in Bowker and Star 1999, 48). When I look at the practices in the Delhi Police HQ, it is easy to see that standard categorisation of crime is frankly not so standard. Because crimes do not follow a set pattern and do not follow the neat crime categories available in standardised police forms, a number of them have fuzzy boundaries and sometimes fit into more than one category. It was up to the discretion of the police officer noting down the case details to fit into the category they thought was appropriate. It was possible that there were times when it did not reflect at all what really happened as already demonstrated via various examples earlier. The standard form, a vision of bureaucratic management of the Dial 100 and crime recording (Lampland and Star 2009), gave only so much leeway to include everything that happened in Delhi in 24 hours to fit into 130 categories. The ‘miscellaneous’ category was a breather people often took recourse to, but I noticed a strong tendency to try and always categorise the crime in some way other than leaving it in the miscellaneous category. The importance of the category assigned cannot be overemphasised because while case

6 This figure on page 27 in the Status of Policing in India Report 2018 shows the number of Muslims in Prisons vis-à-vis their population in the states. In all states the former figure is higher (Status of Policing in India 2018). Table on page 196 in the same report shows the index of SC, ST and Muslim prisoners with respect to their populations in various states. In almost all cases, it is close to one (hundred per cent).

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notes carried the exact details of the crime, it was the crime category that made it to the Green Diary and it was the category that was eventually mapped. I again allude to Latour’s Actor Network Theory, as mentioned in the previous chapter as well to explain infrastructure in DMD, to explain the role of material objects like telephone instruments, pedal mics and address databases in recording crime information correctly in the Green Diary which would be used for mapping DMD and as base data CMAPS. The shoddy state of the tools and instruments used in various stages of recording crime data conveyed to me the apathy of the higher police bureaucracy and the government who are only interested in grand spectacles of technology-led policing (in CMAPS). Wrong geocoding emanating from inaccurate location information of the crime event can impact, to varying degrees, the calculation of hotspots in crime mapping algorithms. Lack of address database and planning in the city meant that call takers and dispatchers plotted the best possible address for a crime event; sometimes to stick to their time constraints, they also marked the crime event location as the police stations in the area. I will talk more about the impact of this phenomenon in the next chapter. In keeping with Bowker and Star (1999, 319) in this chapter, I try to look at the classification of emergency calls on the number 100 into different crime categories as a “site of political and ethical work”. Their warning reproduced below is important for this era of artificial intelligence, especially when combined with crime classification: The importance lies in the fundamental rethinking of the nature of information systems. We need to recognise that all information systems are necessarily suffused with ethical and political values, modulated by local administrative procedures. These systems are active creators of categories in the world as well as simulators of existing categories (ibid., 319). The bureaucratic rules and processes involved in getting data ready for mapping give us an understanding of the assembled nature of data rather than it being an ‘objective’ or ‘neutral’ entity devoid of any moorings. I have explained the kind of biases that these processes can introduce in the data. The word ‘bias’ is inadequate because it has a mathematical undertone that indicates a gap that can be fixed by mathematical formulations (for example, engineers argue that representational bias can be overcome by putting in more and more data). As I will show in the next chapter, it

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is not just bias in the data but the cumulative design of the system that cannot be separated from the social realities that inform the construction of a crime and the criminal.

References Bowker, Geoffrey C., and Susan Leigh Star. 1999. Sorting Things Out: Classification and Its Consequences. Inside Technology. Cambridge, MA, USA: MIT Press. Gillespie, Tarleton. 2014. ‘The Relevance of Algorithms’. In Media Technologies: Essays on Communication, Materiality, and Society. Edited by Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot. The MIT Press. Gitelman, Lisa, ed. 2013. ‘Raw Data’ Is an Oxymoron. Infrastructures Series. Cambridge, Massachusetts: The MIT Press. https://doi.org/10.7551/mit press/9780262525374.003.0009. Hull, Matthew S. 2003. ‘The File: Agency, Authority, and Autography in an Islamabad Bureaucracy’. Language & Communication 23 (3–4): 287–314. https://doi.org/10.1016/S0271-5309(03)00019-3. ———. 2012. Government of Paper: The Materiality of Bureaucracy in Urban Pakistan. https://doi.org/10.1525/california/9780520272149.001.0001. Jackson, Virginia and Gitelman, Lisa. 2013. Introduction. In ‘Raw Data’ Is an Oxymoron. Edited by Gitelman, Lisa. Infrastructures Series. Cambridge, Massachusetts: The MIT Press. Khanikar, Santana. 2018. State, Violence and Legitimacy in India. New Delhi: Oxford University Press. Lampland, Martha, and Susan Leigh Star. 2009. Standards and Their Stories: How Quantifying, Classifying, and Formalizing Practices Shape Everyday Life. Cornell Paperbacks. Ithaca: Cornell University Press. Marda, Vidushi, and Shivangi Narayan. 2020. ‘Data in New Delhi’s Predictive Policing System’. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 317–24. FAT* ’20. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3351095. 3372865. May, Tim. 2001. Social Research: Issues, Methods and Process. 2nd ed. Buckingham; Philadelphia: Open University Press. Narayan, Shivangi. 2020. ‘Past, Present, and Past as Present in India’s Predictive Policing’. XRDS: Crossroads, The ACM Magazine for Students 27 (2): 36–41. https://doi.org/10.1145/3433144. Piliavsky, Anastasia. 2015. The “Criminal Tribe” in India before the British. Comparative Studies in Society and History 57 (2): 323–354. Prasad, Kislaya. 2013. ‘A Comparison of Victim-Reported and Police-Recorded Crime in India’. Economic and Political Weekly 48 (33): 47–53.

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Satyogi, Pooja. 2019. Law, Police and “Domestic Cruelty”: Assembling Written Complaints from Oral Narratives. Contributions to Indian Sociology 53 (1): 46–71. https://doi.org/10.1177/0069966718812522. Schwartz, Joan M., and Terry Cook. 2002. Archives, Records, and Power: The Making of Modern Memory. Archival Science 2 (1): 1–19. https://doi.org/ 10.1007/BF02435628. ‘Status of Policing in India: A Study of Performance and Perception’. 2018. Report. New Delhi: Common Cause. Stanley. Mathew. 2013. Where Is That Moon, Anyway? The Problem of Interpreting Historical Solar Eclipse Observations. In ‘Raw Data’ Is an Oxymoron. Infrastructures Series. Edited by Gitelman, Lisa. Cambridge, Massachusetts: The MIT Press. Sutherland, Edwin H., and C. C. Van Vechten. 1934. ‘The Reliability of Criminal Statistics’. Journal of Criminal Law and Criminology (1931–1951) 25 (1): 10. https://doi.org/10.2307/1135675. Thévenot, Laurent. 1984. Rules and Implements: Investment in Forms. Social Science Information 23 (1): 1–45. https://doi.org/10.1177/053901884023 001001.

CHAPTER 4

Crime Mapping and the Construction of a Criminal

Introduction “Viewing maps as embodiments of power relations directly challenges the traditional scientific, technical notions of objectivity that permeate cartographic practice. But if the key to the internal power of maps is the “cartographic process”, then the sociology of that process must be analysed in order to understand how power flows through it” (McHaffie 2011). At this juncture of the book, the traditional, objective, technical notions of the cartographic practice are looking a little blurry. Through Chapters two and three, I have established that the need for mapping is political and is dependent on the definition of crime as a property crime. Also, that mapping is dependent on bureaucratic exigencies of funding, of proposals being accepted by senior officers and their perceptions of crime mapping versus traditional policing on the field. The lack of an address database and the tussle between different departments to share data, which led to a number of challenges and workarounds, is the reality of crime mapping, not the smooth processes one might imagine. Chapter three demonstrates the very situated, human nature of data used in mapping, data that is otherwise considered to be unattached to the social or cultural environs from where it comes from.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2_4

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Now, let’s come back to our original question: Does predictive policing provide an objective figure of a ‘criminal’ or does it reify existing common-sense notions of crime and criminality in India? I use the word ‘reify’ because the outputs of technological systems carry with themselves a veneer of authenticity that makes it impossible to question them. What they say becomes true in an indisputable sort of way. With regard to this, it is my proposition that the outputs of digital crime mapping do not fall very far from its common-sense perceptions, with dire consequences for impacted parties. In this chapter, I continue to understand the power flows in the cartographic process of crime mapping in Delhi Police by drawing on CMAPS’s choice of layers, and plotting accuracy of both CMAPS and manual mapping. In the final section, I ask, what role does the Predictive Policing System, its input data, its layers, along with the way the data is plotted, all ensconced in the bureaucratic setup of the police institution and the caste society of India, have in constructing these dangerous residents of dangerous spaces? Poverty has been seen as one of the biggest factors for causing crime with poor spaces considered as hotbeds of crime. However governments do not put their energies in reducing poverty but in policing it (Bhardwaj 2014). In a neoliberal conception, poverty is seen as the fault of the poor hence, a life of crime, their own choosing (Wacquant 2012). Also, since, poverty routinely intersects with those who are socially marginalised, people from oppressed castes and minorities are seen to suffer from ‘criminality’, a phenomenon considered an intrinsic feature of their natural selves, sanctioned by the caste system (Nandi 2016; Narayan 2021). This conceptualisation is extended to everyone who becomes the “other”, especially lower caste members of other religious communities such as Sikhs and/or Muslims.1 Again, I posit in this chapter that these communities continue to be criminalised even with the so-called objective and scientific world of crime mapping, now with an added section of tech. I will take a quick recap of the mapping process right now to continue to

1 Although religions like Sikhism and Islam claim no division on the basis of caste, in reality they almost mimic the caste division of the Hindus, from which most of their followers originate from. Lowest caste Sikhs are the ‘Ravidasi Sikh’ or the ‘Majhabi Sikh’ while Ajlaf Muslims like the weavers, butchers, etc., form the basis of the lowest castes in Muslims. See Imtiaz Ahmad (2015) and Puri (2003).

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explain the reification of commonsensical perceptions of ‘criminals’ and their ‘criminal spaces’.

A Recap of Mapping Programme in Delhi Police As has been systematically explained in the previous chapters, mapping in Delhi Police follows two paths—automatic and manual. Manual mapping has been going on since 2007 in a department within the Communication Department of the Delhi Police Headquarters called the ‘Digital Mapping Division’ (DMD). Here four kinds of crimes: snatching, rape, robbery and eve teasing, are plotted on a GIS map of Delhi every day and sent to 23 heads of the Police for resource planning. The crime data plotted in this way is based on emergency calls received in the Dial 100 call centre for the above-mentioned four crimes (discussed in detail in Chapter 3). Maps are plotted using an approximate location because of lack of a robust address database. This crime data from the past years (13 years as of 2017) is being used as historical data in the newly established automatic mapping system called the Crime Mapping Analysis and Prediction System (CMAPS) in 2017. The system automatically maps calls received on 13 kinds of crimes at the Dial 100 call centre on a GIS map of Delhi. An algorithm calculates the hotspots while several layers help in a deep analysis of the data. The maps in DMD show a daily picture of crime but are static. Once they are printed they are mere visual props and not analytical tools. However, CMAPS is capable of showing a different picture of crime depending upon the vantage point one chooses to see it from, through a combination of layers. The data from the Dial 100 call centre goes through only one loop of interpretation in CMAPS, and does not get verified by investigating officers (although as I have explained in Chapter 3, such a verification exercise does not always attempt to find out what really happened at the scene of the crime, and thus false calls also get recorded and mapped). For example, in January 2018, a snatching case in a South Delhi neighbourhood was recorded as a true case in the Delhi Police HQ and hence mapped in CMAPS. However, when I went to enquire about the case at the police station, the records showed that there was “no matter of snatching” (police speak for it being a false case). The case notes said that although the woman had felt that her chain was being snatched, it was a false alarm.

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These types of discrepancies are not rare. Police data is largely dependent on police officers’ own interpretation of the crime event and also on their understanding of the victim (for example, women victims of sexual harassment rarely get their problems recorded by police officers) (Sutherland and Van Vechten 1934). DMD data acts as historical data for CMAPS hence these discrepancies become a part of CMAPS too. CMAPS uses data on 13 kinds of crime, from the Dial 100 Call Centre and the Crime and Criminal Tracking Network Systems (CCTNS), a mission mode project under the UPA government, which records First Information Reports (FIRs) from all police stations in Delhi. Currently, all the FIRs recorded in any police station in Delhi are recorded by CCTNS. However, a large part of police station data includes informal complaints which do not form a part of CCTNS. Due to the high workload, police actively discourage people from registering FIRs and use the complaint route to solve their problems. Some complaints do convert to FIRs but not all. This implies that FIRs also give only a partial picture of ‘crime’ in the city. There is also an Online FIR system, called the e-FIR in Delhi, where people can log their FIRs without visiting the police station or calling on the number 100. These FIRs are instantly logged without following the procedural route of filing FIRs in Delhi. However, e-FIRs, miniscule as they are as a percentage of all FIRs in Delhi, are again not part of CCTNS therefore not mapped in CMAPS. Crime mapping thus should be seen with a caveat that it does not represent all that goes on in the name of crime in any region, here Delhi. It covers only a limited arena of deviance categorised as ‘crime’ by the politico-legal system of India—for example, even severe cases of sexual harassment are downplayed as ‘eve teasing’ and one would have to die and go to heaven to see crime hotspots that show finance crime. Still crime mapping is a myth of its own and continues to be the go-to choice for police.

Mapping Crime The information society has provided a new impetus to mapping with a renewed interest in visual image (Pickles 1995). Visualisation of crime was done with paper maps and coloured pins and now done technologically with the help of digital crime maps. With GIS led crime mapping, it is possible to explore different crime types and identify the typical profile of the offender who commits this type of crime, explore their personal

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networks and associations with other offenders and even their associations with the victims of their crimes. It is also possible to map where offenders live to explore if certain areas have high densities of offenders…. An ‘offending’ profile identifies the general characteristics of those that are likely to commit these crimes, and even helps pick out particular individuals who are known to the police and who could become subject to further targeting (Chainey and Ratcliffe 2005, 291). This kind of investigation, using an offending profile, is where mapping works best, because it is able to connect associations on the basis of locations. On the other hand, an offender’s profile, which would need the police to know about the offending person’s passions and motivations, might not be best suited for mapping. Therefore, generally mapping is used to connect ‘crime’ to ‘criminals’ and not the other way around. Crime mapping exercises evolve from dealing with crime in the city on an individual basis to becoming a governance activity that is intended to keep away those who were considered a risk to ‘ordered’ society. Members of the ordered society are the ones who are intrinsically considered to be the rightful owners of the city. They are the rightful citizens as opposed to the “quasi citizens” (Khanikar 2018), as discussed in Chapter 3. Aurora Wallace (2009) argues that the categories selected for crime mapping are those which are most central to the private property ownership of today; therefore property crimes or nuisance crimes are at the top of the list of crimes that are mapped and its perpetrators policed the maximum. According to Hull (2012), as opposed to just being positivist or interpretive, maps are in a complex and dense relationship with the things they represent. Maps exist in the space between being positivist and interpretative but are entangled with all the complexities of the reality they are shown to be representing. Hull espouses that when we look at a map, we should also look at the “ideologies and political processes” that are embedded in the practices that create a map (p. 213). In the case of crime mapping in Delhi Police case, one needs to specifically look at everything from the input data to the actual mapping practices, the actual making of the layers to understand the ideologies that the resultant crime map embodies. However, the unit of crime mapping, crime, is not an ‘objective fact’ but an interpretative category based on the subjectivity of the observer. Wallace (2009), Wood et al. (2010), Crampton (2007), and Harley (1988) argue that maps present a simplified representation of the actual reality, thus making it difficult to understand the criminality of an event.

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“By erasing context, motivation, befores and afters, the static crime map provides little or no interpretative information” (Wallace 2009, 16). Wallace (ibid.) explains further that the cultural dynamics of a place, its street-level micro processes are stripped away during crime mapping because of the imperative of the map for uniform representation of the area being mapped. “As objects, these maps are abstracted from the process of their own construction, from the ideological debates and viewpoints that inform them, and from their public uses. They are made to inform and to confirm, functioning as both question and answer” (p. 19). Kindynis (2014) states that maps are socio-political constructs that represent a certain negotiated reality. He adds that they are also “instruments of domination and government” and “expressions of power and ideology” (p. 229). Kindynis further argues that maps reduce the “spatial dynamics of crime” because they discount the complexities of the space and its role in the making of the crime. By making hyper local spaces more relevant than whole sections of the city (large sections of the city become just swathes of spaces marked by differently coloured dots; it is only when one zooms in at the street level that the dots make any sense), Kindynis claims that crime maps justify the preference for streetlevel punitive action rather than looking at macro social, economic and political reasons for rising crime events (ibid.). Finally, he claims that maps might be propagating a scientific objectivity that does not exist but is assumed to be present; Kindynis even alludes to the use of crime maps as Latour and Woolgar’s (1979) inscriptive devices (a concept which I will deal with in detail later in this chapter). Finally, Innes et al. (2005) argue that crime maps are an attempt to exert rational knowledge that is sourced by imperfect sources, which is then legitimised by the map, which we have seen happening in DMD as well as CMAPS. As Wallace (2009, 19) puts it, “The Map is the phenomenon objectified, and once objectified it is its own proof”, much like the criminality of Delhi’s marginalised spaces. On 15 February 2020, a popular English daily in India published a news item stating that on Google Maps, Kashmir is shown as part of India, if seen from India, but is shown as a disputed territory if seen from anywhere else in the world. The report stated that Google, with its majority control of information on the world, bends information according to its own will. With 80% share in mobile maps and over a billion users, Google has a disproportionate impact on the viewers’ perception of the world. This story is a clear indication to us that all maps

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have a hidden agenda but because of the popular belief that they represent some objective reality, these agendas are rarely questioned by their users (Kindynis 2014). Brian Harley was one of the first researchers on mapping to argue about maps as a vehicle of the Foucauldian discipline of spaces. He postulates that the “activities of compilation, generalisation, classification, formation into hierarchies and standardisation of geographical data” are not neutral technical activities of mapping but involve powerknowledge relations at work (Harley 1988). These activities construct a place and territory according to the standards of the world that the society desires. Transformation of a space, any space, into territory is a political process where mapping plays a pivotal role in recognising a certain space as a specific kind of territory, as explained in the example of Google mapping Kashmir as part of India when seen from India but as a disputed territory when seen from anywhere else. A territory becomes a territory only after it is rendered so on a map. Similarly, a geographical area in a city becomes a crime-prone region due to the coming together of processes of surveillance and mapping. Mapping involves processes of “gathering, working, reworking, assembling, relating, sifting, … speculating and so on…[that] allow certain sets of possibility to become actual” (Conner 1999 as cited in Dodge et al. 2011, 94). Maps embody a certain “synoptic perspective” which makes their clear lines and intersections feel divorced from the reality they represent, a reality that can be messy (Hull 2012, 212). This act of making clear or legible alludes to the arguments of thinkers like James Scott (1998) and Foucault (1979): to make something legible and clear is an act of surveillance and control. Seeing clearly increases the power of monitoring just as we have seen earlier in the case of Bentham’s Panopticon. Presentday societies offer the premise of ‘risk’ to calculate the ‘dangerousness’ of an individual/community. Prominent feature of risk governance is the division of people according to their various features in comparison with a basic ‘normal’ set (Ericson 1998). Risk calculations have also now moved from individuals to communities, where they are divided according to varying levels of potential harm. According to Crampton (2007), geosurveillance, therefore, becomes the tool of choice to map entire populations and govern them according to the risk they present. The many locative technologies, he argues, mark a person in space in order to calculate the risk presented by the location of the act. Location

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data when mapped also provides a comparative picture of risk from one place to another. To map a territory is to make it visible for governance, as James Scott (1998) would argue, but mapping does not just make visible but also lends an area to surveillance and ordering activities of the state that include enumeration, classification, categorisation and ranking according to risk. While risk-based profiling could enable the government to help the marginalised, it is only used to push ‘risky’ populations further from the ambit of governance. While seeing clearly would make it easy to govern a forest, it might be too simplistic to govern people this way because the totality of people’s life variables—their aspirations, likes, dislikes, or their emotions—are not quantifiable. Also, while objects may be classified without such classifications being used to define their identities, it is not the same case with human beings. Classification of people into castes, classes, criminal proclivities has a feedback effect on their identities (Hacking 1990).

Maps and Digital Tech: Geographic Information Systems (GIS) The Geographical Information Systems or GIS is a computerised system for storing, retrieving, analysing and displaying geographic data (Monmonier 2002). GIS provides immense opportunities for the analyst to play with large amounts of social, economic, historical and urban data (present as layers) to analyse data in order to retrieve hidden patterns in it. GIS is not just a technological system of mapping but upholds a certain value system of techno-positivist solutions to social problems. Robert Lake (1993) notes that GIS has been seen as a sign of the presence of the positivist method in the social sciences even though the positivist method has been heavily criticised especially by feminist scholars. According to him, the GIS objectifies the human subjects whose data is used in the mapping process, making them “indistinguishable, fungible and interchangeable” (p. 409). Lake borrows the term, “God’s eye view” (ibid.) from Donna Haraway (1988) to explain how the GIS process fixes categories such as gender, race and class from a universal vantage point. This vantage point is described as one that is external to the “situatedness, context and viewpoint” of such categories. In other words, for a GIS programme, every person in a specific category has the same experience in life. Lake argues that for a GIS programme, all that matters is

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how the “categories interact in a space–time grid with other objectively defined categories” (1993, 410). Goodchild (1995) informs us that most of the GIS formats of representation are approximations to best represent the reality to be mapped. According to him, ideally, the representation should be made by people who best understand the field; he gives the example of a soil scientist who would be best equipped to create a soil map. However, he says, most of the time such an ideal situation does not exist. The representations are constrained by either limitations of the software or by the lack of understanding of the GIS user. Chainey and Ratcliffe describe a hotspot as a “geographical area of higher than average crime. It is an area of crime concentration, relative to the distribution of crime across the whole region of interest (e.g., a city centre, census ward or tract, municipal district, county or state). Hotspots are often clusters of crime that can exist at different scales of interest” (2005, 145–46). A hotspot thus, is not an absolute measure of high or low crime numbers but just a relative indication of its increase in a given area. However, for all practical purposes, it is considered an absolute measure of crime, without any enquiry into how it came into being or how it works. In the next section, I will unpack the last bit of the cartographic process of crime mapping in Delhi to understand how technological processes can provide a stamp of approval to common prejudices and that it has real ramifications for people from the most vulnerable sections of society.

Layers of Crime Mapping Analysis and Predictive System or CMAPS A digital map, on which crime is plotted for hotspot mapping, is a composite map consisting of many ‘layers’. Each layer is a storehouse for specific information, and different layers can be made to interact with each other to help in a multi-factor analysis of crime. For example, analysts can cross-reference crime data with the number of hospitals, wine shops, dark areas, local landmarks or any other information (in the form of layers) they deem as influencing the rate of crime in the area. Crime mapping is about visualising every aspect of a crime event—location, nearby areas, the social and economic profiles of people who live in the area, their interests and habits—and linking them together to find the offender, the criminal.

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Any and all data is relevant in such an exercise, because the possibilities to make patterns and connections are endless here. Nearly any and every piece of information regarding a person can be used to create their profiles, especially data on their location. The layers of GIS are different information sections, just like the layers of a cake, that can be made to work in different permutations and combinations to give analysts a better understanding of the data they are working with. They work in an additive fashion where the layers could be combined to study their effect on the base data, herein this case, crime. For example, the location and time layers can be mixed with crime data to see the distribution of crime by time or location: the data on local landmarks mixed with crime data could show the distribution of crime according to local landmarks. Layers can also act as historical data for the GIS algorithm, teaching the programme what features to recognise in a crime. The GIS cake tastes different with different combinations of layers. Layers in GIS maps are another example of the techno-positivist imagination of social life where designers aim to divide it according to individual features. Robert Lake would argue that layers of identity such as gender, race or class are embedded in each other and cannot work in an additive environment (1993, 410). In his view, no number of layers can possibly represent the complexity of the human condition. Coming to CMAPS, in chapter two, I have discussed how the DMD team worked on creating the layers for police station boundaries which was essential to place crime in their respective police station jurisdictions. The list of 20 odd functions of the DMD also includes making layers of ranges, PCR zones and police station boundaries under the Delhi State Spatial Data Infrastructure (DSSDI)2 project. The other layers that the DMD also created, as have been mentioned in Chapter 2 as well, are the metro station and flyover layers that have cropped up in Delhi in the last 10–15 years. The data mapped in DMD for four heinous crimes, rape, robbery, snatching and eve teasing also acts as a layer in CMAPS against which crime analysis could be performed. Just as Lake said above that any number of layers cannot represent the complexity of the human condition, I would argue that the choice of what layers could allegedly effectively represent the human condition is a political affair. Ram Manohar, along with the ESRI executive, said that

2 Delhi State Spatial Data Infrastructure Project, https://gsdl.org.in/DSSDI.html.

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the mapping done at the DMD could be more detailed if more information, especially about socio-economic conditions, were provided about nearby areas, which could then be converted into layers in the GIS maps. The ESRI executive said that layers such as socio-economic variations of the city, educational variations and immigrant localities are some of the most important layers that are considered while designing crime mapping software worldwide and that the Delhi Police should incorporate them in CMAPS for more accurate analysis of crime. He said that such software was available with them but that the officers were not ready to pay for it. Lack of existent data, as Ram Manohar said, was also an impediment against buying additional software for CMAPS. However, while the Delhi Police HQ did not possess detailed social or economic data on neighbourhoods in the city and neither did anyone seem keen on acquiring them during my fieldwork for this book from 2017–2019, after 2019, they have shown a strong inclination towards using facial recognition and hence is working to substantially expand its databases.3 It is currently in the midst of creating the Interoperable Criminal Justice System (ICJS) which will merge all extant databases in order to get a 360-degree profile of a person.4 Thus one can see that layers are not innocent and the choice of the kind of information to be added as layers is an indicator of the politics of the GIS users, here the police department. Willingness to add socioeconomic layers shows that police officers still believe in the correlation of poverty with crime and would use GIS not to alleviate poverty but to police it.

Location, Location, Location! Earlier, while discussing the definition of a hotspot, we learnt that hotspots are relative areas of high crime. Therefore, finding the location of crime and plotting the event at its right location are imperative for calculating the correct hotspot. I dealt with the former in chapter three, let us

3 NCRB to use Facial Recognition on mask wearing faces, https://www.medianama. com/2020/09/223-indian-automated-facial-recognition-system-face-mask-detection/, accessed Tuesday, October 27, 2020. 4 Interoperable criminal justice system, https://thc.nic.in/user%20manual/ICJS-man ual.pdf, accessed October 27, 2020.

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look at plotting the map correctly and its importance in crime mapping in this section. In this chapter I want to discuss how wrong location mapping could and does lead to wrong mapping and wrong calculation of hotspots which could have cascading impact on the kinds of bias created in the maps. To understand crime plotting in CMAPS, I recorded two crimes each from the categories of snatching, robbery, and one from that of the category of ‘eve teasing’ to examine whether they had been mapped correctly in CMAPS. To do this, I first noted the call number (CRDD) of the random crime events from the above categories, from the PA100 form maintained by the Dial 100 call takers, along with their location and looked for them in CMAPS. I found that one of the calls of snatching was mapped at the police station of the area of the crime event. The address in the PA100 form was also of the police station, however, there was no description in the case notes that the crime indeed happened at the police station. The call taker could not ascertain the location because of bad audio of the call. They said that they tried to recheck the call to find the exact address by listening to a recording of the call but were unable to find the correct address. Such recordings are maintained by the AMC centre on the fourth floor of the Delhi Police HQ for exactly such situations. l. The police station was thus mapped as a placeholder in place of the exact address. The second crime occurred in ‘Baba Mohalla’ in Aya Nagar. It was plotted in Aya Nagar but not in ‘Baba Mohalla’ The third crime of robbery occurred in Dwarka but was plotted in Gurgaon, in the neighbouring state of Haryana, which is not in the jurisdiction of Delhi Police. The other crime of robbery took place in a bank and because that was a fixed and known address, it was plotted correctly at the bank. A crime of eve teasing was plotted in East Delhi when it actually occurred in Central Delhi. Apart from that, there was another case where the case notes said that the in-laws had killed their daughter-in-law. The girl lived with the inlaws in Khanpur but she died in the Virat Kapoor Hospital in Shadipur. I noted that CMAPS had mapped the incident (murder) at the hospital in Shadipur instead of Khanpur where the real incident occurred. This is not a case of the call taker recording an inaccurate address, but of the definition of the term “location of crime” itself. The software does not take into account that the caller might be calling from a location different

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from the actual scene of crime. They are trained to locate the caller’s address and mark it as the location where the crime occurred. The executive from ESRI informed me that call takers were trained to ask for the location of the caller and not that of the crime. While it could be the same most of the time, a number of times, it was not. For instance, in the case mentioned earlier of the girl whose parents alleged that her in-laws had murdered her. Because the parents called from the hospital, where the daughter finally succumbed to her injuries, and not from the in-laws’ home, where she was actually assaulted, the scene of crime was recorded as the hospital instead of the in-laws’ place. The ESRI executive said that his company had the software that could be loaded into the personal digital assistant (PDA) devices, such as phablets, of the PCR officers enabling them to capture the correct location when they visited the scene of crime, he lamented the fact that the senior officers of the Delhi Police HQ were not interested in pursuing this. It was not just the senior officers, as we learnt in Chapter on Green Diary, the officers on PCR duty were not ready to carry the PDA devices for fear that they themselves will be surveilled in the field, as the app can provide real-time tracking of their location while on move. Furthermore, crime data (or any data that is being mapped) should ideally be mapped with reference to some standard reference data, generally provided by the government. For example, US mapping is done with reference to the Topologically Integrated Geographic Encoding and Referencing (TIGER) database from the US Census Bureau that is freely available to download; no such standard reference database is available in India for crime mapping. In the examples above, the first mapping serves no purpose in hotspot identification as it marks the location of crime as the nearest police station instead of the actual place where the incident occurred. The other snatching case included in the table was mapped as located in the random area of the Aya Nagar region which may or may not have been Baba Mohalla, creating a case of missing data for hotspot calculation. Due to the dependence on the local concentration of crime, even minor changes in location can cause major variations in hotspot calculation, such as in the Aya Nagar case. The third crime event had been mapped in Gurgaon, which is not even in Delhi. The fourth one was indeed marked correctly as it was a

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unique address with readily available geocoding5 Finally, the last incident in the table was mapped in East Delhi when it actually occurred in Central Delhi. Inaccurate mapping of locations results in inaccurate sites being marked as ‘hotspots’; this could further lead to marking non-crime-prone regions as crime-prone ones or even increased police presence in places that do not require it. Inaccuracies in mapping locations can also introduce bias in spatial analysis. For instance, a study in Texas, USA, revealed a selection bias in terms of ethnicity because of wrong geocoded addresses (Hart and Zandbergen 2012), address matching depends on the quality of the recorded address (noted by the call taker) and the reference address (the base address database of the citizens of the region) along with on the GIS software design and its parameters. The data that cannot be matched is called missing data. Bichler and Balchak (2007) note that most police record management systems were not designed keeping in mind the need of accurate data recording, leading to inaccuracies of names of streets, block numbers and directional identifiers. A number of addresses, they say, are approximated to the nearest location. This we have seen happen quite regularly in the Dial 100 call centre where a number of crime locations were approximated to the nearest police station. Cases of wrongly reported data more than once can introduce a series of missing data, in turn creating a geographic bias where certain areas are over-represented and some are not represented at all. Bichler and Balchak (2007) argue that another way missing data can be created is when open areas are routinely part of crime data that could be included more than once while plotting a crime map. To understand this better, we can go back to our example of Najabgarh wali road where, because of the absence of specific landmarks, the location of crime events was plotted according to the discretion of the plotter. If crime repeatedly occurred in this area, there was a probability that it would be geocoded wrongly by the plotter leading to the case of missing data for the actual location of crime. This can lead to bias in the final crime analysis. Addresses of shanties and slums that are not part of the planned geography of Delhi are more likely to be wrongly geocoded in this manner and could suffer from over inclusion or exclusion in crime hotspots. 5 The act of determining the geographic coordinates of a place and then assigning them to an address (where the crime occurred), is known as Geocoding (Chainey and Ratcliffe 2005).

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No process is fool proof and Bichler and Balchak contend that there will be some missing data in every scenario (2007, 44). While up to 15% of missing data, i.e., the address data that could not be matched with one in the address database of the city, can be estimated by standard estimation techniques, more than that can lead to selection bias, again compromising the analysis (Bichler and Balchak 2007). Estimating the locations or leaving them out, in any case would introduce bias in the analysis referred to as a ‘measurement bias’ (Marda and Narayan 2020). Along with historical and representation bias, this can skew the direction of crime analysis against specific locations and due to the segregated nature of Delhi, against specific communities residing in them. As far as Delhi Police HQ is concerned, I have described how location problems abound in CMAPS primarily because it maps the locations recorded by call takers without any second-level verification, unlike in the DMD where the PCR van officers visit the site to verify both the crime event and the location. Call takers are in a hurry to record the nearest location that they are able to decipher from the callers’ description. Many a time they map the call on the police station in the area reported by the caller, leading CMAPS to map the crimes at the police station. When confronted with a hard to decipher address, most call takers take the easy way out and just map the crime to the police station of the area (because police station boundaries, as we have seen, are the most complete and accurate database they have). This is also the only thing callers can report with certainty most of the time, unless they are not visitors in the area.

Manual Maps and Their Plotting Inaccuracies The manual map plotters in DMD don’t even aspire to plot the accurate locations of the crime incidents in the DMD because it is not possible for them to do so unless the exact X–Y coordinates are available. They do not also refer to the address database because it is a cumbersome process to access the Delhi address database. When referencing the database, the plotter has to search for the address in the database, copy its latitude– longitude (X–Y) coordinates and then look for the corresponding location on the map before plotting it. It takes a considerable amount of time to shift between two different applications on two different screens because of the slow speed of the computer processors. If done too frequently, the computers hang and the entire exercise becomes a waste of time. Therefore, it is an unsaid practice among the plotters in the DMD to plot the

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map based on their own knowledge of the localities of Delhi and use the address database only when there is no other recourse left. As we have seen before, the maps are divided into districts and police station zones. Once the plotter reaches the district/zone, they look for the closest match to the address in the Green Diary. Once reaching this position, they put the dots (plots the crime) on the location they consider nearest to the one mentioned. For example, if the description of the location includes some landmark, they will look for it and plot the crime in the broad area closest to it. Rohit, whom we met earlier in Chapter 1, and who usually plots the crime maps daily (others pitch in when he is not available), said that he was quite knowledgeable about the geography of Delhi and was able to identify most locations given in the Green Diary. He felt he did not have to refer to the address database to plot the crime data, but sometimes when he got confused about a location address, he would ask the local human database, Moti Ram. Moti Ram, as mentioned in Chapter 2, is a beat-level constable who was transferred to DMD after 28 years of working on the streets of Delhi. He is well-versed with all the locations of Delhi and guides the plotters to the right address when they get confused, as mentioned in chapter three. However, sometimes the addresses are so vague or incomplete that even Moti Ram faces problems. For example, Moti Ram was stumped on one occasion when the location mentioned by the caller was ‘Najabgarh wali Road’ (loosely translates to a road in an area named Najafgarh); the road ran for several kilometres so the crime was plotted on (what was understood to be) the centre of the road, since a more precise location was not available. Inaccurate geocoding can create wrong and false hotspots. According to Chainey and Ratcliffe (2005), algorithms are generally corrected for errors in plotting, especially in the case of long streets. In manual mapping, however, it would be the responsibility of the crime mapper to correct such inaccuracies. In an instance where there is no further information provided, the crime mapper has no option but to map the address to a convenient location, generally the centre of the street, if it is a long street and there are no other identifying landmarks available. Map making has always been an exercise in estimation but this is not something that the users of the maps are informed of. As John K. Wright (1942) describes, some cartographers took recourse to their imagination while drawing their maps to mask the use of inadequate source materials or, more problematically, careless use of adequate source materials. Wright

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(1942) argues that often there are processes through which information on the map is simplified or amplified. When there is too much information available about certain attributes plotted on the map, cartographers need to simplify these to make the maps legible. On the contrary, if it is a case of too little information, they need to be amplified to fill in the blanks. In both these cases, users of maps should be cautioned against using the references blindly. Now that we have looked at the two most prominent features of both manual and automated mapping in Delhi Police and understood, as I would emphasise again, the very situated, arbitrary and socio-technical nature of crime mapping, let us look at the question we posed at the beginning of this chapter. Who is a ‘criminal’ and how does crime hotspot mapping in Delhi reify commonsensical notions of one?

The Construction of the Criminal Till now I have talked in detail about layers and plotting troubles in CMAPS and manual mapping, as essential details to confirm mapping as a human endeavour as opposed to how it is understood as being devoid of any social or cultural bearings. Now I want to talk about how the crime map, with its bureaucratic exigencies, infrastructure troubles and data arbitrariness comes together to form a map and construct the figure of the ‘criminal’. Latour and Woolgar (1979) describe ‘inscriptions’ as the final diagrams or curves about a substance in a laboratory, which then become the focus of its discussion. The activities, including the time and cost, that made the diagram/curve possible are hidden away, probably in manuals that no one cares to read. A key point to the whole distillation is that any further writing or discussion about the substance takes that diagram/ curve as a starting point. Comparisons are made within diagrams and curves, effectively denying the processes that brought them into existence. If we equate crime maps to inscriptions, two parts of this argument become relevant. First, crime maps hide all the background processes that brought them into existence. The biases of data used in mapping in DMD also indicate the biases of data in CMAPS, as CMAPS uses data from DMD as its historical data. Thus, the propensity of DMD officials to consider slums and immigrant colonies as crime hotbeds, as shown in Chapter 3, can lead to their over-representation on the crime map in case the addresses are not clear. This could be because the addresses of shanty

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settlements are not part of the planned architecture of the city and are most likely missed or wrongly plotted. Shanty settlements developed in the crevices of the planned architecture of Delhi; as Khanikar (2018) has argued, Delhi’s master plans of both the years 1962 and 2001 do not provide any spaces to accommodate its mobile population: labourers who come from rural areas and smaller towns to find work in the capital who take refuge in a temporary shanty or a slum. Not part of the plan, their addresses are not represented correctly either. Slums and other spaces where the poor reside are considered hotbeds of crime because of various reasons. I will put the first reason as caste. The residents of slums are mostly people from the lowest castes, who have the lowest chances of survival in Indian society. If indeed an upper caste person lands in a slum, they never stop reminding people around that they are indeed upper caste. They also use their upper casteness as a currency to get out of sticky matters (Khanikar 2018). These castes have been considered to be criminal by birth (Narayan 2021). However, Piliavsky (2015) notes that conflating caste and criminality, especially in the case of lower castes, nomadic tribes and other non-Brahmanical castes, was not a colonial invention as many historians have argued, but rather a legal formalisation in the form of laws what was commonly known and believed across India. Singha (1998) argues that it was the attitudes of high-caste elites towards the poor and low-caste communities and individuals that presented them as debased and criminally inclined and dangerous in the eyes of the colonial government. This “Dangerousness” was being policed as early as the 1800s when those belonging to the criminal tribes, people on the badmaash registers and those who belonged to the ‘bad livelihoods’ category (Section 109– 100 of CrPC 1882) were policed for being ‘habitual offenders’. The habitual offender need not be convicted of any crime but merely be arrested or apprehended for it. He was simply the “badmaash” (Singha 2015, 244). Badmaash could be anyone who does not fit the normative of the settled village agricultural life. Such people were fair game for police surveillance in India, unlike in the UK, where the habitual offender law and consequent surveillance applied to only ex-convicts. Singha further elaborates how police surveillance along with sections 109–10 of the Criminal Procedure Code (CrPC) codified people without a normative occupation as criminals (Singha 2015, 244–245). The procedure of a “bad-livelihood” enquiry, under the aforementioned sections, enquiry could erect quite a rigid frame of ‘habituality’ for a person under

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scrutiny.6 Someone ordered to furnish security under section 109 or 110 was no longer simply a ‘suspected person’. He acquired a record and, for purposes of police surveillance or jail discipline, was often categorised with convicted habituals. Though the Indian Jail Committee of 1919– 1920 stated that habituality should rest on one or more convictions, not on character alone. Nevertheless, it held that an order to find security under section 110 would count as a previous conviction because ‘such an order is made only as a result of a regular judicial proceeding and is in fact as much a judicial finding on facts directly in issue as a regular conviction or acquittal’ (Singha 2015, 250). In Despotism of the Law (1998) Singha argues how the alms-seeking Brahmin, who were also in a way nomadic, were excused from the punishment that other castes and tribes received from the police; the severest punishments were reserved for ‘offenders’ from the lowest castes. Such a perception was institutionalised in the Criminal Tribes Act 1871, which marked certain tribes and their occupations as criminal and under purview of colonial policing. Most of the times, people living on the fringes, nomadic tribes and lower castes were arrested mostly to assuage the panic in the ‘respectable’ classes about the rising crime in the village or to get hold of free labour for government projects (such as road building) (Singha 1998). Thus, from the colonial times police surveillance and times onwards, police action has not necessarily been carried out to reduce crime. The most disadvantaged and resourceless people have been routinely rounded up or designated as criminals without much proof of their actual involvement in crime in order to work for free for the government. Nandi (2016) also explains how “respectable fears” or the fear experienced by middle or upper middle-class people due to rapid changes in society that they then map onto the lower classes, in turn criminalising them (p. 3) about the migrant labourer from Bihar or inner Bengal led to the Goonda Act of 1923. The Goonda Act had the power to extern anyone on the basis of mere suspicion of criminality. Nandi explains 6 Anyone ordered to furnish security under section 109 or 110 was no longer simply a ‘suspected person’. He acquired a record and, for purposes of police surveillance or jail discipline, was often categorised with convicted habituals. For instance, the Indian Jail Committee of 1919–1920 stated that habituality should rest on one or more convictions, not on character alone. Nevertheless, it held that an order to find security under section 110 would count as a previous conviction because ‘such an order is made only as a result of a regular judicial proceeding and is in fact as much a judicial finding on facts directly in issue as a regular conviction or acquittal’ (Singha 2015, 250).

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respectable fears as the fear experienced by middle or upper middle-class people due to rapid changes in society that they then map onto the lower classes, in turn criminalising them. This caste culture continues in the Delhi Police till today. It could be because of its lack of diversity, especially in the lower rungs of the force. According to the Status of Policing in India Report (SPIR report 2019), Delhi ranks at the 11th spot in terms of diversity in its police force. Ghazala Jamil’s study (2014) on segregation in Delhi shows that the city is segregated along religious lines and Muslims, especially the poor ones, have already been placed at its margins. They are policed for small infractions such as venturing out late at night in the elite areas of the city, indicating their assumed ‘unsafe’ character by the police and thus, their need to be punished. The solid citizen, on the contrary, is always the recipient of legal protection and welfare benefits and is also the one continually worried about the ‘dangerousness’ of quasi citizens (Khanikar 2018). The solid citizen is hence interested in devising different methods to keep themselves safe which acts as a catalyst for sophisticated security products to enter the market. It is their demand that security companies rush to fulfil. Chapter 23 of the Punjab Police Rules (PPR 1933) which the manual for how the Delhi Police should function, still valid in 2023, deals extensively with what it calls “preventive policing” to surveil and record people who are considered to be habitual criminals or informally (because this does not have a legal definition), ‘bad characters’. Apart from that, Delhi Police maintains four registers labelled the Ruffian Register part 1, part 2, part 3, part 4 respectively. This is to record details of people who are ‘habitually addicted’ to crime, or someone, as defined by police officials, who has committed more than three crimes. Similarly, there are ‘bad characters’ or ‘rowdys’ who are people who have increased ‘propensities’ towards crimes (Narayan 2021). Khanikar (2018) claims, and my own conversations with police officials corroborate that bad characters, ruffians or ‘rowdys’ are overwhelmingly men from marginalised sections of society, such as Dalits and Muslims, who reside in slums. These registers are planned to become a part of the upcoming ICJS system, thus eventually becoming a part of the digital policing endeavour of Delhi police, reifying more firmly the relationship between caste and criminality. Second reason, the modern state is an ordered state. Everything, from the architecture to the people, is fixed according to a plan that helps in the collection of revenue, in making policies for maximising human

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resource production, and in conscription; anything that disturbs this order is deviant to the point of being criminal. Somewhere from the nineteenth century onwards, the ‘Police State’7 devolved into categories such as the economy, respect for law and order, population management, and finally the military apparatus of which the police eventually became a part. They were tasked to maintain the order of the state by treating the order breakers as criminals. In India too, the colonial police expanded its powers in the wake of the increasing presence of the urban poor due to the industrialisation and the accompanying urban migration to cities like Bombay in the 1900s. The Police Act of 1902 “rendered the police an increasingly intrusive presence in the social relations of the street and the urban neighbourhood” (Kidambi 2004). As a remnant of practices of that time, even today slums/immigrant colonies or ghettos are looked at suspiciously by state actors. Ghertner (2015) argues that all the applications for slum removal received at the municipality allude to the alleged threat to safety from a slum nearby a housing complex. They are argued to break the aesthetic and functional order of the city. Slums are not seen as a failure of the state to provide housing to those less fortunate but as ‘illegal’ spaces which breed crime. Just like how Ram Manohar remarked that once the illegal immigrants (main residents of slums) are removed from Delhi, it will become a crime free state. Most technologies of policing, contrary to expectations, create more continuities than breaks in the styles of policing because the police remain a ‘rational-legal organisation’ whose main concern is the policing of ‘classbased street crimes’ (ibid., 78). Therefore, the formulation of risk too is concentrated on cleaning the streets off beggars and vagrants and other so-called anti-social elements. It is the street where the social ideas of crime are rooted such as what could be seen in (UK) Vagrancy Act (1824) and the Metropolitan Act (1824). Modern policing, Coleman and McCahill (2011) contend, has consolidated the idea of criminal areas and now has more and more powers to brand areas as ‘disorderly’ and to 7 In the mid-seventeenth to the mid-eighteenth century—we take this time period because this is the transition period from Empires to nation-states when territories and their people were being defined more than ever—the police was, according to Foucault (2007), a set of “laws and regulations that concern the interior of a state and which endeavour to strengthen and increase the power of this state and make good use of its forces” (pp. 409–410) in other words, police was such that it was supposed to link together “state’s strength and individual felicity” (p. 421). In other words, the Police were a total disciplining force, entrusted in making the population docile.

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impose sanctions on them. Armed with this, police surveillance now has a territorial push to develop technologies that intrude on the lives of the more publicly visible while leaving alone those that can choose to stay away, citing their privacy. While talking to various people in the police headquarters in Delhi, in the Dial 100 call centre, dispatch and Digital Mapping Division, I found a few common assumptions about callers among the call takers. One of them was that “padhe likhe” or ‘educated’ people do not call the call centre often, indicating that they do not get into trouble often. Second, was the idea that the “environment” of the person was responsible for them becoming a criminal. In common parlance, the term “padhe likhe’’, doesn’t literally mean educated or literate. By invoking the phrase “padhe likhe” the officers are referencing a certain stratum of society that in their estimation does not, by nature, indulge in petty quarrels, neighbourhood conflicts and especially one that would call the police over trivial matters. This is a common perception of the police forces across Delhi (Khanikar 2018). The belief that the place of one’s residence is a harbinger to crime if it is poor or strife-laden also stems from the same understanding of crime being always petty or criminals being always poor. During my visit to a police station in North East Delhi, for instance, the police officers in the station told me that the area is crime-prone because children learn from their parents and others around them and crime is what they see here. They said this was because of the lack of social role models and education among the people who inhabited the locality. In the event of a lack of options for good education (there were no ‘good’ schools in the locality and the schools were bereft of good teachers), the possibility of social mobility was low. I was further informed that the most frequent crimes in the locality were snatching and eloping (involving young couples). Asked further about the latter, one of the police officers lamented that could not really do anything if the couple were adults; all the same, if there was a ‘missing’ complaint, they were charged with finding the young people and handing them over to their parents. When I pressed him further, he added that they take an undertaking from the parents that the children would not be harmed in any way on their return. Towards the end of the conversation, one officer remarked that the area was full of crime and the thana was always busy, but the “real crowd” came in only after 5 pm, indicating that criminals only operated in the after-hours. As a parting shot, the officer warned me not to walk around

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wearing my gold chain in the area, as it would be snatched and advised my partner, who had accompanied me, to be careful with his mobile phone. This belief of children becoming prone to crime because of a lack of social role models is not without its corroboration in several criminological theories, as mentioned before in Chapter 1. Police believe that marginalised, fringe sections of the city, many a times inhabited by religious and social outclasses, were home to criminal behaviour. During our conversations, the officers routinely spoke about migrants, Bangladeshis and slum dwellers as the reason for high crime numbers of the city. In a news report8 regarding the crime graph of Delhi in 2019, the commissioner of police at the time stated that immigrants and youth frustrations lead to crime in the city. He said that the youth who live in underprivileged neighbourhoods next to affluent neighbourhoods aspire to get rich quickly because of what they see around them. “The socio-economic disparities between the rich and the poor are giving rise to criminals”, the report stated. In another report,9 the commissioner of police stated, “There are identified clusters in all 14 districts where some youths are known to indulge in criminal and anti-social behaviour. Division and beat staff of all these colonies should be assigned to identify anti-social youths, who have high-end motorbikes, but do not have a proper mode of income. After identifying them, staff will have to monitor their activities and nab them”. Both these reports show how the police prioritise and categorise, both crimes and criminals, i.e., petty crimes that are committed by young people living in slums and poor areas of the city. There is no research to prove that people of certain communities commit more crime than people of other communities, but perceptions are hard to change. Ram Manohar firmly believed that Muslims committed crimes in other areas because (a) what did they even have to steal in their areas and (b) they were cunning enough to not implicate their own areas in crime. Such is the belief in the inherent criminality of Muslims among the officers of the DMD and the Delhi Police (and this

8 Delhi Police chief blames migrants, youth’s frustrations for rising crime graph, https://www.hindustantimes.com/delhi-news/delhi-police-chief-blames-migrantsyouth-s-frustrations-for-rising-crime-graph/story-jUOlAjinWl7i1Ae22tyYvL.html, accessed Tuesday, October 27, 2020. 9 To curb theft in the city, identify anti-social youth, https://indianexpress.com/ article/cities/delhi/to-curb-theft-in-city-identify-anti-social-youth-delhi-police-chief-539 6450/, accessed Wednesday, October 28, 2020.

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is a belief that extends to other marginalised communities) that (a) even when the data indicated them as not being criminals, they were presumed to be so, and (b) they suffered from surveillance and a controlling kind of policing because it was assumed that they could not be reformed, only disciplined into following the law. Keith Bottomley and Pease (1986) state that it is an ecological fallacy to conflate group behaviour with the behaviour of individuals of that group, in order to argue that factors such as unemployment and drug use are a cause of crime. This fallacy leads to the assumption that everyone who is unemployed and/or takes drugs is/could be a criminal. They formulate that while these factors correlate with high crime periods in any region, there has been no conclusive research that such correlation is equivalent to causation. They explain that Social Control Theory and Strain Theory (both discussed in Chapter 1) do indicate a relationship between unemployment and crime. However, most people who are unemployed and/or took drugs were also engaged in other activities that could have made them commit crime, activities that have been difficult to identify and operationalise as research variables to state their relationship with the act of committing crime. They further state that along with unemployment and drug use even factors such as “ethnicity, neighbourhood type, class and gender distribution and age” cannot be simply and causally linked with rates of crime (p. 140). If maps genuinely show the actual conditions of the region they map, rather than the cherry-picked variables that do not show the disparities between different regions, especially in a crime map of Delhi, they would be a lot less uniform and much more detailed than the maps present currently. I looked at the printed crime maps produced by DMD as well as the digital map in CMAPS and the one thing that stood out was how the colour palette, or the symbols, did not differentiate between the poor areas, slums, shanties and the posh or rich areas of Delhi, when these places look and feel wildly different in real life. What the maps failed to inform the lay reader was the inherent discriminations that places like slums face from the administration/government in terms of urban resources and facilities such as health, education and employment. The Cities of Delhi project report on exclusion, informality and predation (CPR India) in the different regions of Delhi show the differential access to basic services such as water, sanitation, waste removal, electricity and transport in planned colonies versus the slums of the city. The report states in no certain terms that people experience differential citizenship

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according to their residential status in Delhi where service delivery was a function of space and legality. This information however is not chosen to be mapped; on the contrary the only differentiating marker on the map is the number of crimes while everything else, be it the level of service delivery or the provision of essentials such as water and sanitation, is shown as of the same level everywhere. This instantly indicates that there is an inherent problem in the residents of a locality with high numbers of crime, which is, in turn, the reason they are unable to keep their crime numbers low. The making of an unsafe space starts with assumptions of inherent criminality of residents of certain spaces, which fuels and is fueled by the apathy of the state towards these residents. Technology such as crime mapping is not revolutionary but feeds into the same assumptions as that of humans on the ground and produces the same output. Just that with technology, it becomes harder and harder to negotiate and show the full picture.

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Latour, Bruno, and Steve Woolgar. 1979. Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills: Sage. https://archive.org/details/laborator ylifeso0000lato. Marda, Vidushi, and Shivangi Narayan. 2020. ‘Data in New Delhi’s Predictive Policing System’. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 317–24. FAT* ’20. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3351095. 3372865. McHaffie, Patrick H. 2011. ‘Manufacturing Metaphors: Public Cartography, the Market, and Democracy’. In The Map Reader, edited by Martin Dodge, Rob Kitchin, and Chris Perkins, 129–33. Wiley. https://doi.org/10.1002/978 0470979587.ch17. Monmonier, Mark. 2002. Spying with Maps: Surveillance Technologies and the Future of Privacy. Chicago, IL: University of Chicago Press. Nandi, Sugata. 2016. ‘Respectable Anxiety, Plebeian Criminality: Politics of the Goondas Act (1923) of Colonial Calcutta’. Crime, Histoire & Sociétés / Crime, History & Societies 20 (2): 77–99. Narayan, Shivangi. 2021. ‘Guilty Until Proven Guilty: Policing Caste Through Preventive Policing Registers in India’. Journal of Extreme Anthropology 5 (1). https://doi.org/10.5617/jea.8797. Pickles, J., ed. 1995. Ground Truth: The Social Implications of Geographic Information Systems. Mappings. New York: Guilford Press. Piliavsky, Anastasia. 2015. ‘The “Criminal Tribe” in India before the British’. Comparative Studies in Society and History 57 (2): 323–54. Puri, Harish. 2003. ‘Scheduled Castes in Sikh Community’. Economic and Political Weekly 38 (26): 2693–2712. Scott, James C. 1998. Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale Agrarian Studies. New Haven: Yale University Press. Singha, Radhika. 1998. A Despotism of Law: Crime and Justice in Early Colonial India. Delhi: Oxford University Press. ———. 2015. ‘Punished by Surveillance: Policing “Dangerousness” in Colonial India, 1872–1918’. Modern Asian Studies 49 (2): 241–69. https://doi.org/ 10.1017/S0026749X13000462. Sutherland, Edwin H., and C. C. Van Vechten. 1934. ‘The Reliability of Criminal Statistics’. Journal of Criminal Law and Criminology (1931–1951) 25 (1): 10. https://doi.org/10.2307/1135675. Wacquant, L. 2012. Punishing the Poor: The Neoliberal Government of Social Insecurity. New York: Guilford Publication. Wallace, Aurora. 2009. ‘Mapping City Crime and the New Aesthetic of Danger’. Journal of Visual Culture 8 (1): 5–24. https://doi.org/10.1177/147041290 8100900.

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Wood, Denis. 2010. Rethinking the Power of Maps. New York: Guilford Press. http://lib.myilibrary.com/?id=255284&entityid=https://netlogin.str ath.ac.uk/shibboleth. Wright, John K. 1942. ‘Map Makers Are Human: Comments on the Subjective in Maps’. Geographical Review 32 (4): 527–44. https://doi.org/10.2307/ 209994.

CHAPTER 5

Epilogue

A book on predictive policing cannot have a conclusion. We seem to have just started with AI systems less than a decade ago and already we are in the thick of it. There are talks of AI taking over the world. Predictive policing itself has produced a number of iterations since the fieldwork for this book was conducted in 2017–2019. I hope that this book is not just a study of policing but answers questions for a larger study of AI systems as well. This book needs to make aware of the assumptions we carry within ourselves about AI and in general about machines. One of the goals of the book is to make it clear that machines are very much situated in the social and it is the social from where they draw their energy. Another important aspect of this book is the discussion on caste and how it informs perceptions of criminalisation and eventually even legal and algorithmic categorisations. A popular perception in the tech industry is that it has nothing to do with the social or cultural (Forsythe and Hess 2001). Police officers in Delhi also keep emphasising that they only rely on ‘facts’, and go after criminals, deviants, not their caste. However, their attitudes towards slum dwellers, poor people, or even immigrants, as mentioned in the chapters, are telling of deep seated prejudice against them. These prejudices recur in the data produced for crime mapping, in actual mapping and in the design of predictive policing systems, such as in the selection of layers in GIS mapping systems.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2_5

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Vaghela et al. (2022) argue that while the tech industry and its upper caste members maintain the myth of ‘castelessness’ and a generic overall competence unrelated to their social network or capital, it practises caste discrimination. By negating experiences of those who do not belong to upper castes, the tech industry validates a caste-based normative way of living, validating notions such as habitual offenders, criminality by birth and criminality of the poor and lower castes. These ought to be contested, not validated in the year 2023. Thus, I hope this book becomes part of the literature that makes caste visible in technology. I want to add a small anecdote here about my visit to a police station in North East Delhi, a fringe area in Delhi which houses the most marginalised of the city, that would probably tell us more about predictive policing than any theory ever could. Officers in the PHQ (who had grown quite concerned for my safety as I had become a regular at the Delhi Police HQ, aggravated by me mentioning my plans to visit the North East part of the city known for their high crime rates) discouraged me from visiting the area, or at least not visiting it alone. Their actual words were “Aap kaha jayengi waha? Who koi jagah hai jaane wali?” (“why will you go there, is it even a place ‘people like you’ should go to?!”). Perceptions, not actual crime or criminality, are the basis of ideas of safety of a space, any space. In the previous chapters we have discussed at length about the fears of the solid citizens of a city. The ‘other’ being the quasi citizen here. The imaginaries of the quasi citizen as always ‘different’ from the solid citizen makes the latter suspicious of the former. These suspicions are based on the basic biology of the quasi citizen. The quasi citizen is deviant by birth. It is a characteristic they cannot help overcome; it is a characteristic that can only be controlled by the police. The police are thus an institution to cater to this suspicion and allay it, many a times using torture and violence as their method of choice (Khanikar 2018). Coming back to the story of my visit to the police station in North East Delhi, I ended up visiting the station, at one of the prominent areas of North East Delhi on 7 April 2018 at around 3 PM, with my husband. The police station was situated in a congested area where small houses, kirana (small grocery stores) stores and other small shops exist side by side. The whole place was swarming with open drains and sewers and seemed to be covered in a layer of soot. The police station or the thana had a front desk called the public facilitation desk which, I found out later, is now a common feature in most

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police stations. This is where people can consult with the police about their problems. This is apparently an initiative to make police stations seem more friendly to the local population. The reception desk is located at right angles to the public facilitation desk, where two police officers sit and prepare the roznamcha (Urdu word for Daily Diary of the police station) and other records. Right next to them was the room marked “Duty Officer”, but I could not locate any duty officer or even a workstation when I peeped through its transparent doors. I spotted a few young boys sitting inside. I could see them point fingers in my direction and laugh but chose to ignore them. Constable Billu Ram at the station was sceptical when I asked to be shown the record of a snatching incident recorded at the Police HQ. He asked me to meet the officers sitting at the reception desk who checked what my queries were and asked me to wait for the Duty Officer (DO). Meanwhile they attended to the many people who came to the station with their problems. There was a man whose son was missing and he had come with a poster-sized photo of him. The officers explained to him how they needed a smaller photo in order to look for the boy. The father was agitated and retorted he had already brought the photos but the police were not helping him, so the officers on the front desk had to pacify him. On the board behind the desk there were 29 duties listed for the duty officer which included upkeep of the police station and making sure police officers were on time and properly dressed. The duty officer turned out to be a woman named Manorama who asked me to check the record room for my query because she said that the March records had been sent upstairs to the record room. However, I could not find the register there. The record room was full with reams and reams of paper registers lying till wherever one could see. There were reams and reams of paper records despite the fact that two decades had gone by since the government has been trying to digitise crime records and make them available in a centralised manner. When I returned to report that I had been unable to locate the relevant register, the duty officer was speaking to a man with the complaint of a missing phone that had been snatched. When he used the word ‘looted’, the DO snapped that his case wasn’t serious enough to warrant the use of the word “loot” which means robbery. She asked the complainant whether he understood the meaning of loot to which he replied that a mobile worth 15 K and 3 K in cash would be qualified as loot. An argument ensued where the DO tried to downplay his request but the man

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stood his ground. He also had an e-FIR registered for the case but he wanted an FIR at the station because according to him the thana FIR would hold more weight and push the police to investigate his case. He told the DO that he wanted the e-FIR converted to thana FIR because that was his only hope of recovering his lost mobile phone. The DO assured him that the e-FIR was as good as the thana FIR but the man stood his ground again. Finally, she asked him to go to the public facilitation desk where someone would help him. His complaint was finally recorded, but with the caveat that the complaint number would remain the same as the one for his e-FIR. Amid this ruckus, the station received a call on the emergency number 100 where a man was trying to die by suicide and his relatives wanted the police to come and rescue him. The officer on emergency duty was sent to attend to him. There were two IOs at this police station who did 12-hour shifts plus one IO dedicated for heinous crimes such as murder, rape, child molestation, etc. At last, when I pointed to a fat notebook on the shelf behind the reception desk which appeared to be the record register I was looking for, I thought we would finally get somewhere. However, it did not have the record I was looking for either. Another officer asked me what I was doing and then helped me find the right register. He kept standing near me, but once he was convinced I was only looking at that particular record, he left me alone. I checked the record. There were two calls made, one at 12:05 and the other at 1:50. The second call was put as copy of the earlier call. They were given the diary numbers as 4A and 4B. The case notes mentioned that when the police reached the place, they found out that the complainant had some argument with another person and there was no matter of snatching “Kahasuni hui but snatching wali koi baat nahi hai” (There was an argument but the crime of snatching did not occur). The matter was filed thus. However, I was aware that in the PHQ, this case had been recorded as one of snatching in the Green Diary. The phone in the police station, did not stop ringing even for a moment with reports of suicide attempts, snatching, and other sundry activities. In the meanwhile, my husband got chided for talking on his phone outside the police station (but in the station compound). The officer told him that if his phone was snatched, it would not be the police’s responsibility. I narrated the story of an evening at a police station, in a fringe area in Delhi, away from the caring eyes of the state, to demonstrate a) how policing works on the ground where the citizens are more quasi than

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solid and b) that perceptions inform every step of data recording and even analysis. Even in the case of mapping, different reasons inform the recording of a crime as true in a police station and the Police HQ. Police stations have to maintain just the right number of recorded crime so that their station is neither split in two (if crime rate is high and cannot be managed by a single police station) nor dissolved completely (if crime rate is too low and officials realise that the region could do without a police station of its own). In HQ it depends on if the PCR van visited the site of crime in actuality. The reasons can be many but the basis of predictive policing—the data on which it runs—can be a product of a number of different situations. How does an algorithmic system account for all those situations? Moreover, how does an algorithm account for the different definitions of crime that the police officers work with? Crime is a subjective phenomenon—what one party considers crime can be a heroic act for the other. Let us recall Durkheim here, something is a crime because it hurts social conscience, not that it hurts social conscience because it is a crime. Whose hurt social conscience would the algorithm account for? If it is the dominant community’s then why are we even talking about neutrality or fairness because the algorithm is merely encoding current prejudices into technology and providing it a legitimacy that would be hard, even impossible, to contest. When we map these inconsistencies, these human arbitrations as ‘data’, it comes out looking like manna from heaven, without any association with the social or cultural. A user is unaware of the background processes that bring this map into being. Being a product of technology, there is hardly any question about its authenticity. Prejudices are mathematicised and presented as universally true. In this book, I have tried to argue against the supposed neutrality and objectivity of the hotspot crime map and its dependence on dominant prejudices against the marginalised. This, I hope, will bring us one step closer to questioning everything that technology tells us, especially, if it involves ‘predicting’ stuff that cannot be mathematised.

References Forsythe, Diana, and David J. Hess. 2001. Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. Writing Science. Stanford, Calif.: Stanford University Press. http://site.ebrary.com/id/10042929.

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Khanikar, Santana. 2018. State, Violence and Legitimacy in India. New Delhi: Oxford University Press. Vaghela, Palashi, Steven J Jackson, and Phoebe Sengers. 2022. ‘Interrupting Merit, Subverting Legibility: Navigating Caste In “Casteless” Worlds of Computing’. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. CHI ’22. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3491102.3502059.

Index

A Advanced Data Research Institute (ADRIN), 15, 23, 33 Algorithmic systems, 3, 5, 18, 20, 23, 42 Algorithms, 4–6, 10, 14, 18–23, 88, 94, 99, 112, 129

B Bangladeshi, 65, 93, 119 Bias(ed), 4, 10, 21 datasets, 4, 5 human biases, 5 Bio power, 32 Brahmin, 2, 115

C Central Police Control Room (CPCR), 15, 32, 53, 75, 76 Collective conscience, 2, 17 Command, Control, Coordination, Communication Integration (C4i), 16, 32, 33, 53, 57, 58, 82

Command Room, 35, 61, 66, 67, 83, 90 Commonsensical, 5, 99, 113 CRDD form, 38, 69, 71–73, 79, 83, 88 Crime map, 1, 35, 101, 102, 113, 120 Crime-mapping, 5, 9, 14, 21, 24, 29–31, 34–36, 39, 49–51, 55, 56, 62, 77, 82, 97, 98, 100–102, 105, 108, 109, 113, 121, 125 Crime Mapping and Predictive System (CMAPS), 14, 16, 23, 29–32, 35, 39, 44, 45, 50–55, 57, 58, 68, 79, 81, 94, 98–100, 102, 105–108, 111, 113, 120 Criminal Tribes Act, 2, 58, 115 D Daily Diary, 16, 48, 71, 127 Dial 100 call centre, 16, 22, 23, 32, 37, 38, 41, 43, 44, 52–54, 57, 67, 69–72, 76–78, 81, 83, 84, 86, 90, 92, 99, 100, 110, 118

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Narayan, Predictive Policing and The Construction of The ‘Criminal’, Palgrave’s Critical Policing Studies, https://doi.org/10.1007/978-3-031-40102-2

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INDEX

Digital Mapping Division (DMD), 1, 2, 11, 15, 16, 20, 22–24, 29–41, 43–58, 61, 63, 66, 67, 69, 72, 75, 77, 79–83, 88, 91, 94, 99, 100, 106, 107, 111–113, 118–120 Dispatch, 33, 41, 42, 57, 61, 63, 66, 67, 71, 72, 77, 82–87, 89, 90, 118

E Environmental Systems Research Organisation (ESRI), 16, 50, 106, 107, 109 Ethnography, 18–20 Eve teasing, 34, 35, 53, 61–64, 66, 99, 100, 106, 108

F First Information Report (FIR), 48, 53, 54, 68, 100, 128

G Geocoding, 32, 94, 110, 112 Geographical Information System (GIS), 5, 8, 11, 16, 37, 41, 45, 47, 55, 99, 100, 104–107, 110, 125 Green Diary, 16, 24, 57, 61–63, 66–69, 83, 85, 86, 89–94, 109, 112, 128

H Habitual Offenders Act, 1952, 2 Halaat , 66–68, 71, 84–87, 90–92 HIMMAT App, 43, 45, 49, 52 hotspot crime map, 129 Hot spot(s), 4, 24 analysis, 5, 15, 16

I Indian Space Research Organisation (ISRO), 14–16, 23, 33, 50, 51, 53 Infrastructure(s), 5, 14, 18, 22, 24, 30, 45, 47, 48, 50, 51, 55, 56, 58, 64, 75, 84, 94, 113 L Local police action (LPA), 68, 86 M Manusmriti, 2 N Neutrality, 3, 6, 129 O Objectivity, 3, 6, 97, 102 Other Backward Classes (OBC), 21 P PA 100 software, 38, 67, 73, 74, 77, 83, 85, 86, 89 Phablets, 72, 84, 109 Police Control Room (PCR), 35, 36, 42, 57, 64, 66–68, 71, 72, 79, 82–90, 92, 106, 109, 111, 129 Policing, 2–4, 6, 7, 9, 10, 12, 14, 18, 30–32, 46, 48, 50, 51, 55, 57, 97, 115, 117, 120, 125, 128 Predictive policing, 5, 6, 10, 14, 15, 18–20, 24, 51, 56, 57, 98, 125, 126, 129 Public Private Partnership (PPP), 49 Q Quasi citizens, 1, 2, 65, 101, 116, 126

INDEX

R Rape, 17, 34, 35, 53, 61–64, 66, 70, 85, 86, 99, 106, 128 Rational Choice Theory, 10, 11 Robbery, 3, 34, 35, 53, 61, 62, 66, 68, 77, 85, 89–91, 99, 106, 108, 127 Routine Activity Theory, 10 S Scheduled Castes (SC), 21 Scheduled Tribes (ST), 21 Snatching, 34, 35, 53, 61–63, 66, 68, 85, 89–91, 99, 106, 108, 109, 118, 127, 128

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Social Control Theory, 10, 12, 120 Social Disorganisation Theory, 10, 11 Social processes, 5, 6 Solid citizens, 1, 2, 65, 116, 126 Standard operating procedures (SOP), 35, 43, 50 Strain Theory, 10, 12, 120 Surveillance, 8–10, 57, 82, 85, 103, 104, 114, 115, 118

T Thanas , 46, 50