Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions 303108182X, 9783031081828

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Michael Greenberg

Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions

Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions

Michael Greenberg

Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions

Michael Greenberg Edward J. Bloustein School of Planning and Public Policy Rutgers University Highland Park, NJ, USA

ISBN 978-3-031-08182-8    ISBN 978-3-031-08183-5 (eBook) https://doi.org/10.1007/978-3-031-08183-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 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. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to our three youngest grandchildren Lola, Layla, and Luna Suggs who are 7, 6, and 4 years old, respectively. They make me smile, laugh and think about the future.

Preface

Every book has a story to tell about its author. When I was a pre-teenager, I had no idea that I lived near so many famous places, that is, good and infamous symbols of New York City and in many cases the United States. They included the Bronx Zoo, Yankee Stadium, the Grand Concourse, and the New York Botanical Gardens. Each was only a few minutes away by foot or bicycle. My parents never had to worry about me running away from home because home was the launching pad for visits to those attractions. If we were feeling more adventurous, we would ride the subway for 20–30 minutes and go to the American Museum of Natural History, the Hayden Planetarium, Central Park, and many other attractions in Manhattan. My friends and I argued about which attraction to visit. It was like deciding what flavor ice cream to buy; you couldn’t make a bad choice. Five of these attractions mentioned in this paragraph are among the more than 70 attractions included in this book. Also located 5 minutes from our one-bedroom apartment was another kind of attraction for young boys: explosions and noise. The Cross Bronx Expressway (CBE), a new road, was under construction across the Bronx. Under the direction of the much praised and despised Robert Moses, the CBE was a wonder for little boys. If we got up early enough, we would run to the excavation site under the Grand Concourse, look down and west to see and hear the explosions loosening up the granite base for the expressway. We loved those explosions. They were more entertaining than July 4th fireworks. My parents and grandparents opposed the CBE; they were angry at Robert Moses, who was Jewish. My grandmother Rose had a choice Yiddish label for him: a Paskudnik, which means evil person. Frankly, I loved the Expressway until Dr. Goldberg, our physician, explained to me that he had to move his office because of all the noise and explosions interfered with his practice located in the medical building next to the intersection of the Grand Concourse and the CBE.  I became more upset when some of my school friends had to relocate because of the project. Eventually, the CBE became an American icon of imposing social and environmental justice inequities. In addition to the CBE, our neighborhood was quietly being stigmatized by the Home Owner’s Loan Corporation (HOLC), a federal agency, charged with advising lenders on where to invest. HOLC categorized our neighborhood of apartments and vii

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single-family homes as “definitely declining” (the color was yellow on their map) and “infiltrated by Russians with a moderate number of relief families.” My father always wondered why he could not get a loan to buy a house on that street and why the apartment house owners would not keep up the apartments. Years later, when he learned about this practice, he was livid because he had worked in a hazardous job during World War II and felt betrayed. HOLC was bad enough for our College Avenue location, but worse four blocks to the east which was characterized as “hazardous” (the color red on the map), and the HOLC note was that the redlined area had 15% Negro residents. (See Chap. 1 for more about redlining and Fig. 9.1.) My introduction to good and bad physical symbols of America continued when I was 11, and we moved 9  miles north to southeast Yonkers, NY, in prestigious Westchester County. My father and mother had saved enough money to buy a small house in southeast Yonkers. Little did they know that the HOLC had classified this part of Yonkers as “definitely declining” and said that the area was occupied by “less than desirable types of native Whites.” My parents were required to put down half of the money to obtain mortgage in this “yellow” zoned area. But my father got his workshop, and I was introduced to two more physical attractions: the Hillview Reservoir where New York City’s water is stored before flowing south into the city, and the Yonkers Raceway and now Empire Casino, which is located adjacent to reservoir. I loved those places. The reservoir was surrounded by a 2-mile long running path. As a teenager, we ran around the 2-mile oval. I especially loved the east-­ facing hill next to the reservoir. We went for picnics, we watched the horses training, and on a clear day, we could see Long Island Sound 6 miles away. I was lucky to have these well-known attractions near me. However, southeast Yonkers was marked by segregation by race/ethnicity and religion. Yonkers was the symbol of a city that opposed housing and school segregation, and it was the first city sued by the federal government for both, which badly hurt its reputation and cost it federal funding for many years. The school across the street from our family home became a symbol of school segregation, and eventually, our baseball field was appropriated to build a bigger school to accommodate a desegregated school system. Years later, I analyzed social and environmental justice on the Bronx-Yonkers border using some of the data sources used in this book. One of my colleagues called it my catharsis paper. These early experiences on College Avenue and in Hyatt Avenue in southeast Yonkers have led to a life-long interest in the spatial intersection of physical attractions and social and environmental justice challenges. I do not take what I see in the street at face value. Over the course of the last half-century, when I visit attractions, I look for evidence of social and environmental injustice. For example, when I visited Yellowstone National Park, I enjoyed the park. Yet, I walked around the two of the neighborhoods just outside the park to get a feel for who lived nearby. The neighborhood visits outside of Yellowstone were very early in the morning in late December when the temperature was between −5 and −20 °F (see Fig. 2.2). I admit to being a little crazy, but only a little – I did see quite a bit. My personal history merged with an academic one when I began to study social and environmental justice issues associated with small hazardous waste sites, National Priority List sites, US ports, chemical weapons storage facilities, and

Preface

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nuclear weapons sites. I learned how to use spatial data sets to look for social and environmental justice issues. My curiosity about people who live near attractions led me to write this book about the association of popular attractions and social and environmental justice. In essence, I wanted to learn who lives near popular attractions; what, if any, social and environmental justice challenges these places represent; and what solutions exist and are being implemented. The book should have two primary audiences. One is the strong and growing social and environmental justice community that has increasingly been scrutinizing parks and many other icons for evidence of injustice. This book will interest them, even though all the results do not consistently support their positions. The second audience is businesses, not-for-profits, and government agencies who manage parks, zoos, museums, and other attractions. I expect they will also be interested in the findings, albeit some may resist them. This belief is the product of my studies of hazardous waste sites and other so-called “noxious” facilities. Some managers argue that they have been unfairly branded, and they are not responsible for the conditions in the community. Lastly, some resist because they do not want to hear or read what makes them uncomfortable. However, they have read or have been briefed about the research findings. My hope is that the breadth of places examined and the effort to consistently apply data and tools to the issue will allow members of these groups to embrace the challenge rather than try to avoid it because it is not going away. Highland Park, NJ, USA May 15, 2022

Michael Greenberg

Acknowledgments

I was primed to love museums, parks, and zoos by my family, friends, and neighbors. My father painted, sketched, and built clocks. I have many of them. My mother played the piano, and my sister was an opera singer and a music teacher. My aunt Beatrice had a beautiful singing voice, and my cousin Geraldine received a scholarship from NYU for her artistic skills. Our Bronx neighbors, Cutlers played musical instruments, and when the Greenbergs and Cutlers got together, there was a lot of talent on display. Fast forward a few decades, my nuclear family has similar interests. My wife Gwen sings and draws, as does our daughter Alexandra. I’m in my home office looking at several of their drawings and paintings. All of us love going to museums, zoos, and parks. I have no artistic talents, but I learned to appreciate the culture found in heritage attractions. Many of my Rutgers and Columbia University colleagues have pushed me to think about what I am seeing and not just settle for the obvious relationships such as environmental and social justice challenges are associated with refineries, ports, and other locally unwanted land uses. Peeling back the layers to find associations between social and environmental justice and heritage attractions is not an obvious task. I acknowledge my father Sydney, my mother Mildred, and my uncle Sol as well as my PhD thesis advisor George Carey, Douglas McManis, Leonard Zobler, William Vickrey, Rose Keisler, Charles Lee, Donald Krueckeberg, Henry Mayer, Dona Schneider, and other colleagues and friends who always had more questions for me to answer, especially if they led me to confront conundrums. I owe all of you a hearty thanks. I thank Tamara Swedberg, my Rutgers colleague, for helping me with the technology required to compile and manage the documents. I wanted to do the research for this book for decades. However, it would not have been possible to do a credible study without the intervention of the U.S. Environmental Protection Agency (EPA), the Centers for Disease Control and Prevention, and the of the University of Wisconsin. EPA created the idea of a public database that would provide anyone in the United States with a computer, iPad, or cell phone the opportunity to look at data and create maps of demographic and environmental conditions. The first release occurred in 2019, and the second in 2022. The data already have made a difference in how we view where we live, and the 2022 release improves xi

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on what already was an extremely valuable tool. I know it will only get better. A big hole in the EPA 2019 database for public-health oriented people like me was the absence of health data. The EPA’s 2022 EJScreen release, which has data for heart disease, asthma, and life expectancy, begins to address that gap. You will see quite a few maps from EJScreen in this book. While EJScreen has some health data, the 500 cities project now called PLACES was created by CDC to allow us to see information about 27 health behaviors and outcomes at the census tract scale in large American cities. I used these data throughout the book. The University of Wisconsin’s Population Health Institute (2022) created and maintains a county-­ scale database with demographic, environmental, health, and local asset information. These three databases allowed me to secure the data, map, and statistically analyze some of it. I thank these organizations and their staff for developing, maintaining, and continuing to build these databases that I believe are at the heart of what should be publicly available in a democracy. The findings, opinions, conclusions, and recommendations expressed in this book are mine and do not necessarily represent the views of the organizations that built and published these data bases.

Contents

Part I Context and Design 1

Creating Attractions and Tolerating Inequity ��������������������������������������    3 1.1 Introduction��������������������������������������������������������������������������������������    4 1.2 Processes Leading to Wealth and Health Clusters Around Heritage Sites������������������������������������������������������������������������������������    6 1.2.1 The Industrial Revolution, 1800–1960s: Building Wealth, Heritage Sites and Injustice��������������������������������������������������    7 1.2.2 Erosion of the U.S. Manufacturing Base and Emergence of Leisure and Hospitality Industries: The 1960s–1980s����������    8 1.2.3 Redlining, Gentrification, and the Primacy of Economic Growth Beliefs����������������������������������������������������������������������    9 1.2.4 Social and Environmental Justice ����������������������������������������   11 1.3 Five Key Themes������������������������������������������������������������������������������   13 1.4 This Volume��������������������������������������������������������������������������������������   14 References��������������������������������������������������������������������������������������������������   17

2

 Designing a Multiple-Scale and Multiple-­Metric Data Analysis ��������   21 2.1 Introduction��������������������������������������������������������������������������������������   21 2.2 Finding Heritage Attraction Sites������������������������������������������������������   22 2.3 Finding and Using Data��������������������������������������������������������������������   25 2.3.1 Shapes for Collecting Data: The Census Tract Challenge����   26 2.3.2 Selection of Metrics and Statistical Tools����������������������������   28 2.3.3 Displaying the Data as Maps������������������������������������������������   33 2.4 Expanding the Search for Non-Heritage Attraction Cluster Sites����   35 References��������������������������������������������������������������������������������������������������   35

Part II Case Studies 3

 America’s Forever Beautiful Heritage Attraction Sites: The U.S.’s Most Popular National Parks ��������������������������������������������������������������������������   39 3.1 Introduction��������������������������������������������������������������������������������������   39 3.2 Data and Methods ����������������������������������������������������������������������������   40 xiii

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3.2.1 Choosing National Park Sites ����������������������������������������������   41 3.2.2 Choosing Metrics������������������������������������������������������������������   43 3.3 Results����������������������������������������������������������������������������������������������   45 3.3.1 Comparisons Among the National Park Areas and Their Hosts ������������������������������������������������������������������������������������   45 3.3.2 Associations Among the Environmental, Demographic, Public Health and Built Environment Metrics����������������������   49 3.4 National Parks and the Justice Challenge ����������������������������������������   52 3.4.1 Glacier National Park������������������������������������������������������������   54 3.4.2 Indiana Dunes National Park������������������������������������������������   56 3.5 Discussion ����������������������������������������������������������������������������������������   58 References��������������������������������������������������������������������������������������������������   59 4

 Remnants of the Industrial Revolution: America’s Historic Grand Concourses as Heritage Attractions ������������������������������������������������������   61 4.1 Introduction��������������������������������������������������������������������������������������   62 4.2 Data and Methods ����������������������������������������������������������������������������   65 4.3 Results����������������������������������������������������������������������������������������������   68 4.4 Grand Concourses and the Evolutions of their Cities ����������������������   70 4.4.1 Group 1: The Grand Concourse��������������������������������������������   70 4.4.2 Group 2: The EJ-SJ Challenges of Five Midwest Industrial Revolution Cities������������������������������������������������������������������   71 4.4.3 Group 3: Four Southern and Western Grand Avenues with Symbolic Advantages and Challenges����������������������������������   73 4.4.4 Group 4: Grand Concourses Remaining Grand��������������������   76 4.5 Discussion ����������������������������������������������������������������������������������������   79 References��������������������������������������������������������������������������������������������������   79

5

Zoos as Endangered Attractions������������������������������������������������������������   83 5.1 Introduction��������������������������������������������������������������������������������������   84 5.2 Data and Methods ����������������������������������������������������������������������������   85 5.3 Results����������������������������������������������������������������������������������������������   87 5.4 Zoos: Responding to Multiple Challenges����������������������������������������   90 5.4.1 Animal Rights ����������������������������������������������������������������������   90 5.4.2 Zoos at the Intersection of Social and Environmental Justice������������������������������������������������������������������������������������   95 5.5 Discussion ��������������������������������������������������������������������������������������   101 References������������������������������������������������������������������������������������������������   101

6

 America’ Iconic Urban Parks and the Gentrification Challenge��������  103 6.1 Introduction��������������������������������������������������������������������������������������  103 6.2 Data and Methods ����������������������������������������������������������������������������  106 6.3 Results����������������������������������������������������������������������������������������������  106 6.4 Large Urban Parks at the Heart of a Balancing Act��������������������������  110 6.4.1 Four Urban Parks in Industrial Cities ����������������������������������  112 6.4.2 Western Cities ����������������������������������������������������������������������  117 6.4.3 Five Major Parks in Four Symbolic Cities����������������������������  119

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6.5 Discussion ����������������������������������������������������������������������������������������  124 References��������������������������������������������������������������������������������������������������  124 7

Museums, the Building of Wealth Clusters and Soft Power����������������  127 7.1 Introduction��������������������������������������������������������������������������������������  127 7.2 Data and Methods ����������������������������������������������������������������������������  129 7.3 Results����������������������������������������������������������������������������������������������  130 7.4 The Tension Between Building Soft Power, Accumulation of Wealth, and Preserving Communities����������������������������������������������  135 7.4.1 Metropolitan Regions with Less than Five Million������������  135 7.4.2 Metropolitan Regions with More than Five Million ����������  140 7.5 Discussion ����������������������������������������������������������������������������������������  145 References������������������������������������������������������������������������������������������������  145

Part III Looking for Other Species of Heritage Sites and Better Solutions 8

Other Species of Heritage Sites: Commercial and Political Symbols  151 8.1 Introduction��������������������������������������������������������������������������������������  151 8.2 Data and Methods ����������������������������������������������������������������������������  152 8.3 Results����������������������������������������������������������������������������������������������  154 8.4 Different Species of Attractions��������������������������������������������������������  156 8.4.1 Group 2 Attractions��������������������������������������������������������������  157 8.4.2 Group 1 Attractions��������������������������������������������������������������  165 8.5 Discussion ����������������������������������������������������������������������������������������  168 References������������������������������������������������������������������������������������������������  168

9

Looking for Better Affordable Housing Solutions��������������������������������  171 9.1 Introduction��������������������������������������������������������������������������������������  172 9.2 The U.S. Federal Government’s Housing Policies ��������������������������  172 9.3 Gentrification Is the Answer But What Is the Question?������������������  174 9.4 Looking Around the Country for Evidence of Providing Affordable Housing and Including Heritage Attractions������������������������������������  176 9.5 Looking at the OECD Nations and Beyond��������������������������������������  184 9.6 Discussion ����������������������������������������������������������������������������������������  186 References������������������������������������������������������������������������������������������������  186

10 Epilogue:  Summary and Looking Forward������������������������������������������  189 10.1 Introduction������������������������������������������������������������������������������������  189 10.2 Summary ����������������������������������������������������������������������������������������  190 10.3 Looking Forward����������������������������������������������������������������������������  191 10.3.1 Improved Monitoring Capacity������������������������������������������  191 10.3.2 Coping with Low Probability: High Consequence Hazards and Cascading Effects������������������������������������������  192 10.3.3 Demographic Change and Growing Disparities ����������������  193 10.3.4 Managing Increasing Uncertainty��������������������������������������  193 References��������������������������������������������������������������������������������������������������  194 Index������������������������������������������������������������������������������������������������������������������  195

List of Figures

Fig. 1.1 The U.S. economic imperative, housing and heritage attractions�������  5 Fig. 2.1 Friendly Buffalo in Yellowstone Park�������������������������������������������������  24 Fig. 2.2 Gardiner, Montana outside of Yellowstone Park���������������������������������  25 Fig. 2.3 Yonkers-Bronx border and segregation�����������������������������������������������  34 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4

Location of eighteen national parks����������������������������������������������������  43 Asthma rates, Glacier National Park and the Blackfeet Nation����������  54 Life expectancy in the Indiana Dunes National Park region���������������  56 Low income population in the Indiana Dunes National Park region������������������������������������������������������������������������������������������  57

Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4

Location of grand concourses�������������������������������������������������������������  66 Illustration of EJScreen along Fifth Avenue and Central Park�����������  67 Asthma rates near Fifth Avenue and the Grand Concourse����������������  70 Poor physical health along the Troost Avenue segregation line in Kansas City������������������������������������������������������������������������������  78

Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4

Location of Zoos���������������������������������������������������������������������������������  86 Heart disease rates near the Cincinnati Zoo����������������������������������������  96 Insurance rates near the Doorly Omaha Zoo��������������������������������������  97 Demographic index near the Bronx Zoo���������������������������������������������  99

Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4

Location of Urban Parks��������������������������������������������������������������������� 107 Asthma rates near Tulsa’s Gathering Place Park�������������������������������� 110 Low income rates near Philadelphia’s Fairmount Park and Zoo�������� 112 People of color near Baltimore’s Patterson Park and Inner Harbor������������������������������������������������������������������������������������������������� 115 Asthma rates near Chicago’s Grant Park�������������������������������������������� 116 Unemployment rate near St. Louis’s Forest Park������������������������������� 117 Traffic density near Austin’s Zilker Park�������������������������������������������� 118 People of color near San Diego’s Balboa Park����������������������������������� 119 Life expectancy near New Orleans’s Audubon Park and Zoo������������ 120 Demographic index near Prospect Park and Zoo�������������������������������� 121

Fig. 6.5 Fig. 6.6 Fig. 6.7 Fig. 6.8 Fig. 6.9 Fig. 6.10

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List of Figures

Fig. 6.11 Sea level rise near Boston Common and inner harbor������������������������ 122 Fig. 6.12 People of color near San Francisco’s Golden Gate Park�������������������� 123 Fig. 7.1 Location of museums�������������������������������������������������������������������������� 131 Fig. 7.2 Demographic index near Indianapolis’s Children’s Museum������������� 135 Fig. 7.3 Proximity of hazardous waste sites to New Orleans’s National World War II Museum������������������������������������������������������������������������ 138 Fig. 7.4 Asthma rates near Cleveland’s Museum of Art���������������������������������� 139 Fig. 7.5 Coastal flood hazard near New York’s 911 Memorial Museum��������� 142 Fig. 7.6 Demographic index near New York’s 911 Memorial Museum���������� 142 Fig. 7.7 People of color near Houston’s Fine Arts Museum���������������������������� 144 Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 8.4 Fig. 8.5 Fig. 8.6 Fig. 8.7 Fig. 8.8

Location of economic and political attractions����������������������������������� 154 People of color near the Las Vegas strip��������������������������������������������� 158 People of color near the Alamo����������������������������������������������������������� 159 Risk management plan sites near Niagara Falls��������������������������������� 160 Population over age 64 near the Mall of America������������������������������ 162 Population over age 64 near Fisherman’s Wharf�������������������������������� 164 Older housing near Fisherman’s Wharf���������������������������������������������� 164 Hollywood sign and fires�������������������������������������������������������������������� 167

Fig. 9.1 Hip Hop Museum and affordable housing in the South Bronx���������� 178 Fig. 9.2 Asthma rates near 18th & Vine in Kansas City����������������������������������� 180

List of Tables

Table 2.1 Number of attraction sites in each state and the District of Columbia��������������������������������������������������������������������������������������  23 Table 2.2 Metrics used in Chaps. 4, 5, 6, 7, 8 and 9�����������������������������������������  29 Table 2.3 Disease rate example (City rate is 55 per 100,000 age-adjusted)������������������������������������������������������������������������������������  32 Table 2.4 Correlations among five health indicators and low income population across 26 cities in the book��������������������������������������������  34 Table 3.1 Eighteen most visited national parks and their county hostsa�����������  42 Table 3.2 County-scale metrics������������������������������������������������������������������������  44 Table 3.3 Host county environmental exposure metrics compared to their host states (50 = same as state as a whole)��������������������������  45 Table 3.4 Host county demographic and social justice metrics compared to their host states (50 = same as state as a whole)����������  46 Table 3.5 Public health outcomes and behaviors compared to the United States������������������������������������������������������������������������������������������������  46 Table 3.6 Built environment metrics compared to the United States���������������  47 Table 3.7 Two underlying dimensions: environment, demographics, public health, and built environment [numbers in the table are correlation coefficients (R-values) between the two factors and original variables]����������������������������������������������������������������������  50 Table 4.1 Twelve grand concourses������������������������������������������������������������������  65 Table 4.2 Population data for grand concourses����������������������������������������������  67 Table 4.3 Comparison of one-mile-wide polygons surrounding the grand concourses to the host cities���������������������������������������������������  68 Table 4.4 Demographic, public health, and environmental metric results for grand concoursesa�����������������������������������������������������������������������  69 Table 5.1 Zoos & botanical gardens and city population���������������������������������  86 Table 5.2 Population for zoo areas�������������������������������������������������������������������  87 Table 5.3 Thirteen zoos: Comparison of 1-mile, 3-mile, and city values��������  88 xix

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List of Tables

Table 5.4 Demographic, public health, and environmental metric results for zoo areas��������������������������������������������������������������������������  89 Table 6.1 Parks and their host city populations������������������������������������������������ 106 Table 6.2 Population for city park areas����������������������������������������������������������� 108 Table 6.3 Twelve parks: Comparison of 1-mile, 3-mile, and host city rates�������������������������������������������������������������������������������������������������� 109 Table 6.4 Demographic, public health, and environmental metric results for park areasa������������������������������������������������������������������������ 111 Table 7.1 Museums and their city populations������������������������������������������������� 130 Table 7.2 Population for museum areas����������������������������������������������������������� 132 Table 7.3 Twelve museums: Comparison of 1-mile, 3-mile, and host city rates������������������������������������������������������������������������������������������� 133 Table 7.4 Demographic, public health, and environmental metric results for museum areasa����������������������������������������������������������������������������� 134 Table 8.1 Populations of cities with commercial and political symbols���������� 153 Table 8.2 Population for commercial and political symbol hybrid site areas������������������������������������������������������������������������������������������� 155 Table 8.3 Twelve commercial and political symbol sites: Comparison of 1-mile, 3-mile, and host city rates������������������������������������������������ 156 Table 8.4 Demographic, public health, and environmental metric results for twelve hybrid sitesa���������������������������������������������������������� 157 Table 9.1 U.S. housing policies, 1934–2008���������������������������������������������������� 173

Part I

Context and Design

Chapter 1

Creating Attractions and Tolerating Inequity

“Man’s capacity for justice makes democracy possible; but man’s inclination to injustice makes democracy necessary.” Reinhold Niebuhr from the foreword of Children of Light and Children of Darkness (1944)

Abstract  This introductory chapter describes conditions that emerged during the industrial revolution leading to the accumulation of wealth, which in turn led to building of zoos, urban parks, museums, grand concourses, and other heritage attractions, highlighted by national parks. In urban centers, heritage attractions have become magnets for wealthy and healthy people. US housing policy favors investors. Hence, when investors and the wealthy find locations that they desire, poor and middle class people are displaced. The book uses public data sets to compare the demographic and health conditions of those who live near Central Park, the Getty Museum, the Art Institute of Chicago, and over 100 other heritage attractions. Also, it examines the environmental conditions of those areas, finding that air quality issues near most heritage attractions are about the same or worse than adjacent neighborhoods and host cities. While focusing on health and wealth clusters, the book examines economic and politically symbolic places such as the Empire State Building, Fisherman’s Wharf, and others comparing their demographic, health and environmental conditions with the heritage wealth and health clusters, as well as searching for evidence that affordable housing projects are linked to heritage attraction clusters in Boston, Chicago, New York, and San Francisco. Keywords  Heritage attractions · Industrial revolution · Zoos · Museums · National parks · Urban parks · Grand concourses · Segregation · Gentrification · Redlining · U.S. housing policy · Leisure and hospitality · American manufacturing belt · Economic imperative · Real estate industry

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Greenberg, Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions, https://doi.org/10.1007/978-3-031-08183-5_1

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1.1 Introduction Karl Paul Reinhold Niebuhr was an American ethicist and theologian. His often-­ quoted view about humans is a perfect entry point for this book. Creating national parks, zoos, parks, museums, art galleries and other iconic heritage attractions are what people, businesses, and elected representatives can accomplish on behalf of society. This book examines many of the most visited American heritage attractions. Yet, the tolerance for injustice in the United States has created painful realties for many people, including displacing them to accommodate the wealthy and healthy. The major focus of this book is to find out what level of environmental and social injustice has occurred near American heritage attractions during this first quarter of the 21st century and what is being done to address these challenges. Before proceeding, it is important to define a heritage attraction site. A heritage site focuses on providing information and experiences about history and culture. Art galleries, botanical gardens, conservation areas, museums, parks and zoos are heritage attraction sites. Yes, they have gift shops, cafeterias and other commercial components and marketing associated with them. They need money. Nevertheless, each is primarily focused on providing something about U.S. or international heritage, history and culture. Heritage sites provide some entertainment in the form of short video clips, art and exotic species, but they are not amusement parks, arcades, and water parks that provide entertainment with limited reference to heritage. Bowling allies, golf courses, gyms, marinas, and ski slopes focus on recreation. A visitor might learn that the local marina once hosted a famous family’s boat, or the Olympic trials were once held at the ski slope. However, trips to those sites are more about sailing and skiing than about heritage. Shopping centers, large stores, and other commercial enterprises concentrate on selling products. They also send messages about U.S. culture and history, but they exist to earn profits. A complicating reality for this study is that heritage sites may be located close to commercial facilities. But the center of attention of the book is heritage attractions, that is, places that focus on offering information and experiences about history and culture. Figure 1.1 focuses on the processes that build heritage attraction clusters. The economic imperative that has long driven the United States privileges those who can use their resources to invest and make a good return on their investment and pick place(s) to live that allow them to enjoy the best leisure time, including places that offer the most beautiful art, music and other entertainment, the most desirable homes and shopping, access to higher education and medical care, and other privileges. Building geographical clusters displaces poor and middle class. As clusters of heritage sites grow and become exclusive residential locations, those threatened by displacement may resist. If they own property, they may make a large profit on their or the family’s original investment. Nevertheless, they may be displaced by actions allowed under zoning, public health and other legal land use planning tools. In short, without strong intervention, the process of creating clusters of wealth and health around heritage attraction sites ultimately displaces poor and middle class people making them pawns in an economic process of building wealth and desirable communities and earning large returns on investments.

1.1 Introduction

5 Build heritage aiiractions: zoos, museums, parks, music halls

Aiiract visitors

Gentrify area to obtain land & housing

Offer housing vouchers Build affordable housing

Investors make money

Displace poor & middle class creating social & environmental injustice

Aiiract wealthy people & upgrade housing stock

Use redlined & yellow-lined areas Adjust zoning & building codes Offer tax incentives Upgrade Infrastructure Market housing & investment opportunities

Establish exclusive wealth & health clusters

Fig. 1.1  The U.S. economic imperative, housing and heritage attractions

A colleague asked the author why he was examining social and environmental justice near national parks, art galleries, museums, zoos and other physical attractions. Should not social and environmental justice be focused on refineries, steel mills, ports, highways, landfills, and other places that are noxious? Indeed, saying objectionable museum or park sounds like an oxymoron. Heritage attraction sites should not be objectionable, even though we know that parks, zoos, museums and other heritage sites attract some people who make noise, park their cars in places they should not, and otherwise disturb the neighbors. They can create light-related pollution, sound from heating and air conditioning, and other chronic annoyances, especially auto-traffic. My expectation was that the most popular museums, parks, and other heritage sites would function like magnets do with iron, drawing affluent and healthy people as neighbors, that is, the people who can afford the locations and enjoy them. However, most does not mean all. Without gathering and evaluating data, it is premature to assume that the processes operating in Fig.  1.1 always lead to exclusive attraction-based-neighborhoods for the wealthy and healthy. Eventually these processes should lead to displacement. If there are exceptions to the model in Fig. 1.1, it may be because they take time to build and may still be developing. In other words, I might be reporting about a snapshot of a relentless process that inevitably produces exclusive residential and attractions in places with the most accumulated wealth. Yet, I have been to many of the attractions analyzed in this book and believe that in some places local government, enlightened business and community groups have been trying to prevent the full evolution of exclusive wealth and health heritage communities. Regarding environmental exposure, zoos, museums, parks and other heritage attractions bring people, cars and buses. I assume that even well-developed wealth

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and health neighborhoods with heritage sites have a high density of automobile traffic and manifest relatively high levels of auto-related air pollution. I did not expect them to have potential exposure from refineries, port activities, waste management sites and other sites commonly known as locally unwanted land uses (LULUs). When I studied social and environmental justice issues near the U.S.’s 50 largest ports, I found high concentrations of poor people of color who lived with relatively high values of air pollution and nearby hazards (Greenberg 2021). Ports, hazardous waste sites, petroleum refineries, steel and coke plants are among the set of enduringly objectionable locations that that help create environmental and social injustice conditions and stigmatize places, albeit not every study reports that they do (Greenberg 2012). As industrial areas become stigmatized, many people with resources move to more desirable locations. Their former neighborhoods are occupied by less affluent individuals and/or by those who have other reasons to live in a stigmatized place. In short, heritage attraction sites should have relatively high values of air pollution-related emissions, but lower values of non-highway-related LULUs like refineries and power plants. While museums, urban parks, opera houses and zoos are magnets for the wealthy and healthy, it does not follow that heritage attractions cannot be part of affordable housing projects (see Chap. 9). Also, some attractions are not heritage-based, and Chap. 8 explores those for evidence of social and environmental injustice. The first chapter of this book has four goals: 1. Describe the U.S. industrial revolution as the foundation for creating heritage attractions. 2. Beginning with the 1960s, describe the decline of the U.S. manufacturing base and the growth of attraction industries. 3. Review social and environmental justice challenges that have been emerging in the United States, including redlining and gentrification associated with heritage attraction sites. 4. Summarize the content of Chaps. 2, 3, 4, 5, 6, 7, 8, 9 and the epilogue.

1.2 Processes Leading to Wealth and Health Clusters Around Heritage Sites The analysis sections of this book focus on the decade beginning in about 2013. Yet it would be a mistake to skip the historical context. Therefore, before examining the recent past and present, this section summarizes the historical foundation and heritage attraction-centered places of wealth and health, beginning with role of the industrial revolution as a foundation for creating heritage attractions and then the 1960’s-1980 period that saw both deindustrialization and concerted efforts to advance civil rights, as well as noting the significance of redlining and gentrification at the neighborhood scale.

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1.2.1 The Industrial Revolution, 1800–1960s: Building Wealth, Heritage Sites and Injustice The Civil War devastated large areas of the United States. When it ended, the economy doubled in value between 1880 and 1990 as the U.S. became a major economic power. The 1900–1920 period marked an unprecedented increase of over 400 percent in the gross domestic product. During the period 1940 to 1960, the U.S. economy massively increased in response to World War II and then switched to producing durable products like autos and refrigerators. From 1960 to 1980 white collar jobs expanded, further changing the economy and people’s lives. The amount of money available to invest in heritage attractions was nothing short of remarkable after 1870. It should come as no surprise that every zoo in this study was built between 1870 and 1970. Fourteen of the 18 National Parks were opened during the same period, as were many of the museums/art galleries and parks. Allan Pred’s (1966) brilliant historical geography describes the spatial expression of the industrial revolution and wealth creation in the United States. Pred concentrated on the American Manufacturing belt (AMB), a slightly misshapen rectangular-shaped area of approximately 850  miles (1369  km) by 400  miles (644 km) that starts north of Boston and its suburbs in New Hampshire and Vermont. From New England, the AMB headed west for about 450 miles crossing the great lakes near Buffalo and north of the great auto and steel centers in Cleveland, Detroit, and Pittsburgh. It continued south along eastern side of Lake Michigan and then back up to Chicago and Milwaukee. From the western side of Lake Michigan, the AMB headed southwest through Illinois for about 375  miles arriving along the Mississippi at St. Louis, its southwest corner. Then having completed about half of its distance, the AMB changed direction heading east for about 820 miles stopping in Cincinnati and then on to Baltimore along the Chesapeake Bay near Fort McHenry. Finally, the AMB curved northeast along what French geographer Jean Gottmann called Megalopolis, stopping in Philadelphia and New  York City and continuing along the Long Island Sound to Boston. Pred observed that in 1958, 47.3% of the U.S. population lived in this area. This compared with 65.3% of the nation’s value added by industry. Focusing on high value-added industries, he noted that 17 of the 23 high value industries produced 80% or more of their value added in the American Manufacturing Belt. Behind the geography was a process whereby growing manufacturing centers attracted entrepreneurs and created more jobs and wealth. The concentration led to rapid growth of retailing, wholesaling, and many other businesses. In turn, some of created wealth was turned into schools, hospitals, infrastructure, and attractions like parks, art galleries, zoos, and other heritage icons. Pred concluded that this pattern of expansion could change, and indeed it did as the American Manufacturing Belt was devastated by competition from other regions of the United States and other countries. The Belt has lost a great deal of its manufacturing, and some now label it or parts of it the American “rust” belt because of all the old, abandoned factories, as will be described in the next section and in Chap. 4 which is

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about the U.S.’s grand concourses. In short, wealth creation followed by decline were clear outcomes of the industrial revolution, along with heritage attractions. The people side of the industrial revolution was the need for low wage workers in manufacturing centers. In 1910, 9 of the 10 most populated cities in the United Sates were in the American Manufacturing Belt, led by New York City with more than 5 million residents. (Los Angeles, the geographical exception, ranked number 10). When immigrants arrived in the U.S., they tended to concentrate based on nationality, religion, and income (Daniels 1997; Lemann 1991). For example, my mother and grandmother who escaped in a hay wagon from a pogrom in Russia, arrived at Ellis Island in 1921, reported to me that as Jews from Slavic areas were treated as inferior by their Jewish counterparts from northern Germany and Austria. The treatment of Southern-born African Americans was even less charitable. Taeuber and Taeuber (1965) described the migration of Southern Black Americans from the lower Mississippi River north to the industrial cities of Chicago, Cleveland, Detroit, Pittsburgh, and St. Louis and from North and South Carolina to Baltimore, Boston, Philadelphia, and New York. Using special census tabulations and records for 209 cities, Taeuber and Taeuber focused on the years 1940, 1950, and 1960, and they were able to trace the concentration of Southern African Americans back to the 19th century in some cities. They found widespread segregation of Blacks and income-related segregation. Their meticulous study demonstrated that income alone could not account for the extent of segregation in the case of Blacks.

1.2.2 Erosion of the U.S. Manufacturing Base and Emergence of Leisure and Hospitality Industries: The 1960s–1980s Between 1939 and 1945, the number of manufacturing jobs in the U.S. doubled to more than 16 million (Harris 2020). Temporarily falling after World War II, manufacturing jobs then increased to almost 20 million by 1979. Accounting for 22% of nonfarm employment, manufacturing was then the largest sector of the U.S. economy. A series of precipitous declines followed during the next four decades, so that by June 2019, manufacturing jobs decreased to less than 13 million. The U.S. remained the second largest manufacturing country, but manufacturing accounted for 9% of jobs in 2019, and now even less. Hardest hit were the cities of the American manufacturing belt. Cleveland, Detroit, St. Louis and many others lost wealth and their ability to support schools, parks, museums, and other assets that had been built during the industrial revolution. Their grand concourses suffered (see Chap. 4). Many American cities also built up a legacy of thousands of contaminated brownfield sites that signaled a decline in wealth and added an environmental health burden to their communities. Among the most populated cities only New York City gained in population. Baltimore, Detroit, Cleveland, and St. Louis lost more than half of their population, and increasingly those cities hosted poorer people who were mostly African Americans and

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descendants of those who earlier had migrated north looking for what Nicholas Lemann (1991) called the promised world (see Chap. 4). The leisure and hospitality (L&H) industry benefited from the wealth created during the industrial revolution. In 1979, about 7% of U.S. nonfarm employment was in the L&H sector, or about one-third of the manufacturing employment. By 2019, leisure and hospitality accounted for 11% of jobs, 2% more than manufacturing. If trade, transportation and utility industries are included, which are part of the business serving L&H, then by 2019, leisure and hospitality was a much more important sector of the U.S. economy than manufacturing. L&H more than replaced the jobs lost by the hemorrhaging of manufacturing, albeit often with lower pay and less benefits. Along with the education and health services, government information and financial services, white and now pink, purple, and other collar industries have replaced manufacturing. Old manufacturing cities like Buffalo, Cleveland, Detroit and others (see Chap. 4) have felt the brunt of the challenge to replace manufacturing with emerging alternatives, such as leisure and hospitality. Summarizing, the industrial revolution created enormous wealth, as well as concentrated people in urban centers. These events created wealth and demand for L&H attractions. De-industrialization led to a loss of manufacturing jobs with painful consequences in some parts of the U.S. and yet the rise of a post-industrial economy has created even more wealth reinforcing the need for attractions for people with money and time to enjoy them.

1.2.3 Redlining, Gentrification, and the Primacy of Economic Growth Beliefs Redlining and gentrification are two practices that have impacted large cities. Much has been written about these two, and while I cannot do justice to them, this section describes them as context for the case studies in Chaps. 4, 5, 6, 7, 8, and 9. The “great depression” so traumatized the U.S. economy that as part of President Roosevelt’s New Deal programs the U.S. federal government created the Home Owner’s Loan Corporation (HOLC) to help homeowners who were in default to avoid foreclosure. With limited resources to distribute, in the 1930s the HOLC was charged with assessing the risks of granting mortgages and other financial aid at the neighborhood scale. The bottom-line was to divide neighborhoods into four groups: 1 . Green – best sites for investment 2. Blue – still desirable 3. Yellow – definitely declining 4. Red – hazardous for investment This classification on maps was enhanced with notes. In the preface, I noted that my neighborhoods in the Bronx and Yonkers were both labeled as yellow and characterized in unflattering ways. Those living in a red zone had little opportunity to obtain

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a loan. Using Chicago as an example, Coates (2014) finds the impact on neighborhoods to be so severe that he argues for reparations, an argument that my father would doubtless support. Mitchell and Franco’s (2016) National Community Reinvestment Coalition (NCRC) project prepared interactive maps of this history, that we can go on-line and look at a census tract scale. The striking finding is that areas that were redlined in the 1930s have been stigmatized. The NCRC study found that 92% of the zones in well over 100 U.S. cities that were labeled “best” are now middle and upper income compared to only 25% that were classified as “hazardous” (red color). Regarding race/ethnicity, the so-called “best” group had 14% minority residents compared to 64% in those that were redlined. The designations are associated with striking differences in health outcomes, also documented by Nardone et al. (2020) and in several chapters in this book. Redlining became illegal under the Fair Housing Act of 1968. Rothstein (2017) underscores the reality that these definitions mark places 80  years later. Almost every attraction site in this book is in a city with a HOLC map and some less than lovable attached comments about their residents by the HOLC analysts. The maps are hard to read. Nevertheless, they show the lingering impact on neighborhoods near the attractions. In general, the grand avenues, zoos, museum and parks with poor health outcomes and high poverty compared to their cities are surrounded by maps with red or yellow HOLC banding. For example, two of the most red and yellow branded areas are in the Bronx and the Doorly Omaha zoo areas (see Chap. 5). The colors are distressing enough, many of the comments are just as bad or even worse than the labels. Although redlining is no longer legal, the practices informally continue by steering investors. Gentrification occurs when relatively affluent and well-educated mostly white populations move into poor and usually minority neighborhoods, leading to displacement. Attention has been focused on artists and small art galleries as agents of gentrification. The literature is extensive and NCRC has studied the linkage of redlining and gentrification. The NCRC reports show that 13 of the 20 most gentrified cites are included in this book, including six of the seven with the highest gentrification rates. The link between redlining and gentrification mechanism is that rezoning of formerly redlined areas opens the neighborhoods to gentrification. Mitchell and Franco (2016) found that redlined areas that have been heavily gentrified have expanded economies and more interaction between races, but greater economic inequality, in other words, a mixed message. Not everyone believes gentrification is a key issue. McMillan (2021) characterizes it as a bunch of debunked theories created in the 1980s and argues that the real focus should be on neighborhood inequality, absence of affordable housing and low wages (see Chap. 9). Demsas (2021) asserts that neighborhoods without gentrification are in a worse shape than those with gentrification. Chapters 6 and 7 examine the evidence in the context of urban parks and museums, respectively. For example, a New York City study found that gentrification does not increase displacement of poor children. This is not necessarily a good sign because poor children are much more likely to move wherever they live.

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11

Some of most outspoken critics of gentrification were former colleagues (Smith 1996), which influenced me, and I did come to this topic with the view that economic growth is the primary private and public policy path followed in the U.S. Gentrification and redlining are touched upon in Chaps. 3, 4, and 5. Yet the evidence is most apparent in Chaps. 6 and 7 in the clustering of museums, galleries, theaters and other assets in cities that attract affluent visitors and affluent residents into clusters of wealth. Chapter 9 revisits these processes in the context of providing affordable housing.

1.2.4 Social and Environmental Justice The movement for Civil Rights in the United has had a long history. Some of the most vigorous efforts to decrease injustice occurred during the 1960s when cites were losing their manufacturing base, roads opened suburbs and the Sunbelt, federal housing legislation favored suburbs and hurt cities. The Civil Rights Movement changed after 1965 when some African Americans began to engage in aggressive protests. They wanted immediate changes that would end legally permitted discrimination. On August 5, 1965, President Lyndon Johnson signed the Voting Rights Act. The call for change has continued. A massive amount of research has produced information for books, articles, plays, media products that describes the evolution of the civil rights movement from 1954 to 1968. None is better than Branch’s (1988, 1998, 2006) three volumes focused around the years of Martin Luther King. Linking the social and environmental justice movements was an important step. The link was forged when a landfill for holding polychlorinated biphenyl (PCB) soil wastes was proposed and then built near rural Afton, Warren County, North Carolina. Local members of the United Church of Christ (UCC) contacted their national headquarters and the UCC group used tactics from the 1960s civil rights movement in rural Warren County. When they returned to UCC’s offices, they organized a study about distribution of hazardous waste landfills across the United Sates, finding that zip codes with one of the worst hazardous waste sites in the United Sates averaged 36% so-called nonwhites. Those with several hazardous waste sites but not one of the worst had 24% nonwhites and the thousands of zip code areas with no hazardous waste sites had 12% nonwhites. The study also showed that these places consistently had a disproportionately high proportion of low-income residents. The Warren County landfill case brought a great deal of pressure on the U.S. federal government leading to President Clinton’s environmental justice Executive Order 12898. Many other studies of hazardous waste sites were conducted. Most found that poor minorities disproportionately lived near hazardous waste sites, but others did not (Anderton et al. 1997; Atlas 2001; Boer et al. 1997; Bullard 1990; GAO 1983; Greenberg and Anderson 1984; Mohai and Saha 2007; United Church of Christ 1987, 2007). The environmental justice literature has grown enormously since publication of the United Church of Christ’s (1987) Toxic Waste and Race. Air quality (Bell et al.

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2014; Bell and Ebisu 2012; Binder 1989; Brochu et  al. 2011; Clark et  al. 2014; Morello-Frosch and Jesdale 2006; Levy et al. 2007) has been one focus. Ports have been another (Greenberg 2021; Rosenbaum et al. 2011; Gonzales and Miller 2015; Goulielmos 2000; Houston et al. 2008, 2014; Karner et al. 2009; Ng and Song 2010; Norsworthy and Craft 2013; OECD 2011; Ruffin 2011; U.S.EPA 2007, 2016; Wilson et al. 2011; World Bank 1990, 2017). Regarding manufacturing facilities, Graham et al. (1999) conducted a fascinating study of 36 coke plants and 46 petroleum refineries. They found concentrations of Blacks and Hispanics around the sites, which was not surprising. What was interesting was their historical study that showed that when Caucasian populations left neighborhoods near the plants, poor and nonwhites replaced them. Chemical plants have been an issue. The Clean Air Act Amendments of 1990 required that U.S. chemical production plants with an above threshold amount of specific toxic or flammable chemicals report accident histories. Elliot et al. (2004) studied the accident history of over 15,000 U.S. chemical plants during the period 1994–2000. Their observations were interesting: larger chemical production facilities were in counties with larger African American populations; however, counties had both higher median incomes and high levels of income inequality. Elliott et al. (2004) found a higher risk of accidents for facilities in heavily African American counties (OR of accident = 1.9, 95% CI = 1.5 to 2.4). Highways have been another growing issue (Mills and Neuhauser 2000; Rowangould 2013; Verter and Kara 2001; Zhu et al. 2002; Zhou and Levy 2007). Lead paint in housing continues to be a major issue, even though few homes used lead paint after 1960 (Centers for Disease Control and Prevention 2012; Gaitens et al. 2009; Jacobs et al. 2002). Ecosystems and water resources are part of the environmental justice concern (Austin and McKinney 2016; Balbidge 2016; Burger et al. 2001; Burger and Gochfeld 2011; USEPA 2008; Vickrey and Hunter 2016). Beginning with slavery and including indentured servitude, the United States has a long history of taking two steps forward and one step back in dealing with social and environmental justice. The current administration has taken strong steps to deal with both. For example, over 25 years after Executive Order 12898 (Clinton 1994) was signed, the Biden Administration’s Executive Order 14008 on Climate Change includes provisions for environmental justice actions by the federal government, a new White House Interagency Council, and a White House Environmental Justice Advisory Council Justice40 Initiative, Climate and Environmental Justice Screening Tool, and revisions to Executive Order 12898. These arguably are the most forward steps taken since the mid-1990s. But even if fully implemented, presidential orders and legal remedies are not going to solve environmental and social justice legacies in the United States, especially during a time when ideology appears to supersede analysis and negotiation. The U.S. federal government is the major dispenser of money and a source of ideas, and the states are thought to be “laboratories of democracy.” Yet the idea of states as laboratories to test ideas seems to be states having ideology-based positions and finding solutions to defend them. The set of heritage attraction sites and the social and environmental justice challenges geographically associated with them defy a simple national

1.3  Five Key Themes

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solution and even state ones. The areas impacted are small and the communities represented are not easily generalized. The same solution will not work everywhere. What works in one place may not in another. I believe that there are solutions that will be mutually agreed upon and amount to a quiet revolution with little national fanfare and spotlight. Chapter 9 discusses and looks for the emergence of several of these.

1.3 Five Key Themes Before turning to the individual chapters, I point to five themes found in almost every chapter of the book. I consider these the book’s major contributions. • Identifying and measuring heritage-attraction-centered wealth clusters. Museums in or near parks, zoos, and some grand concourses have become exclusive places where people of wealth and health reside. The idea is first brought up in the grand avenues chapter in the context of Fifth Avenue in New York and Ward Parkway in Kansas City. The evidence builds in every chapter and is most marked in clusters of museums, parks, zoos and other attractions in the wealthiest neighborhoods in New York, Chicago and Los Angeles. Several other cities are moving in the direction of building wealth clusters, for example, Houston and San Diego. • Recognizing poor and lower middle-income people as pawns in the building of these wealth clusters. Those with few resources and little political power have limited options to resist the economic, legal and political power directed at creating heritage-attraction-wealth clusters. The poor become pawns. Even if they are not displaced, they lose control of local culture and assets that were part of their feeling at home. Too often they move over-and-over again, losing the benefits of living in a stable place and the chance to create important social contacts that go with stability. • Acknowledging pressure on local officials and site managers to balance priorities. National government creates and funds programs. However, the burden of balancing between the desires of wealthy parties and under protected and underserved members of the public falls on local officials and government site managers when they are responsible for an area under great pressure from wealth and its instruments. Chapters 3, 4, 5, 6, 7, 8, and 9 feature examples of how these responsible parties cope with the balancing act they perform. Clearly, listening, communicating, and negotiating have become important attributes for management positions that involve working with parties that do not trust each other’s goals or tactics. • Improving Public Data Bases. I could not have done this study of over 100 places with standardized data bases a few years ago. The data used in this book were not available in a suitable format before 2019. While I applaud the developers of these data bases, there is room for improvement. Government and private

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resources are needed to add data about water supply and quality, noise and other environmental indicators, neighborhood attributes and to make data more readily available in formats that can accommodate single users that want to look at their community and groups that want to analyze large regions across the nation. Chapter 2 reviews these issues in detail, and the reader can see how the data limitations play out in the case study chapters where drinking water quality data, for example, would have been a big help. • Working out solutions that make life a little better for every group. The challenges imposed by creating heritage-attraction clusters are endemic, constituting a conundrum with no simple answers. There are no policies that will make everyone happy. Massive doses of rhetoric make people on opposite poles angry at each other and frustrate people who want a solution that we can live with even it is does not make all of us happy. In the last chapter and in other chapters I evaluate solutions and endeavors to evaluate them on their ability to make improvements, as well as powerful trends that will impact heritage clusters.

1.4 This Volume Chapter 2 of Part I presents the design of the research used to paint a picture of local demographics, environmental exposure, human health and local attributes of the study areas. It begins with how more than 100 heritage attraction sites were selected from among thousands that were possible. The major data bases used to evaluate these sites are from the U.S. Environmental Protection Agency, the Centers for Disease Control and Prevention, the University of Wisconsin, and the U.S Census Bureau. Some of the data were collected from other agencies. The chapter explains the strengths and weaknesses of these data, including a detailed discussion of why not every chapter uses the same data. Chapter 2 also explains statistical methods used to answer the research questions, emphasizing the limitations of the health data and how these limitations are reflected in the statistical methods. Part II includes Chap. 3, 4, 5, 6, 7 and consists of case studies. Chapter 3 is about the most visited National Parks in the United States. The chapter sites include the following National Parks: Great Smoky Mountains, the Grand Canyon, Zion, Yosemite, Yellowstone, Glacier, Cuyahoga Valley, the Indiana Dunes, the Gateway Arch and nine others. Using county-scale data for 46 counties that include 18 national parks, the chapter examines demographic indicators, environmental measures, health outcomes and behaviors, and local amenities. With only a few exceptions, low levels of air pollution are apparent in these counties, an expected finding. Yet, the research found some counties with relatively high levels of social inequity compared to their host states, such as Glacier National Park and the Indiana Dunes, for example. These counties and several others have poor health outcomes and limited amenities. In other words, the beauty we see in our national parks is not necessarily reflected in social justice just outside of all of them. The chapter reviews the origins of equity-related challenges and efforts made by National Park Service

1.4  This Volume

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headquarters and two sites to meet their objectives as national parks while adjusting to challenges from communities that expect more from the National Park Service. Chapter 4 focuses on grand concourses built during the industrial revolution to accommodate the needs of wealthy residents and enterprise. A century or more later, the areas adjacent to these wide concourses have relatively high levels of air pollution which is not surprising because the roads carry a great deal of traffic. Yet, as whole they present less social inequity and less evidence of poor health than their host cities. Like every chapter in Part 2, the chapter includes a typology that groups the 12 concourses into groups and considers why they evolved as they have. At the one end of the spectrum is the Grand Concourse in the Bronx NY which presents striking evidence of social and environmental injustice and on the other hand are Ward Parkway and New York City’s Fifth Avenue (only a few miles away from the Bronx Grand Concourse) with much better outcomes than their host cities. The study areas are 1-mile-wide linear polygons following the path of these grand concourses, for example, Fifth Avenue (New York), Massachusetts Avenue (DC), Ward Parkway (Kansas City), Richmond’s Monument Avenue, St. Charles Avenue (New Orleans), and Los Angeles’s Wilshire Boulevard, and six others. Chapter 5 is about the most popular zoos in the United States, many constructed in the late 19th and early 20th century when urban populations and wealth were growing, and cities were adding entertainment for the growing populations. Now zoos have become an endangered species because animal rights activists have targeted them as abusive to animals and immoral. Some have also experienced financial and political control issues, worsened by the COVID-19 epidemic. Regarding the zoo areas, the one-mile radius areas around the 13 zoos in general present relatively less social and environmental injustice evidence than their surrounding neighborhoods and host cities. However, the Bronx, Doorly, Cincinnati, and Indianapolis zoos do demonstrate issues, especially about demographic, health, and traffic issues. This chapter shows that these large zoos have proven to be resilient and increasingly have become responsive to the growing society calls for conservation and sustainability. Given their local settings, Cincinnati, Doorly, and the Bronx zoos are used to illustrate responses that are likely to lead to better treatment of animals and more constructive responses to calls for action against climate change and for sustainability. Larger zoos appear to have the capacity to more than survive. Chapter 6 examines some of the most famous city parks in the United States, many built before the Civil War and others shortly thereafter. The chapter shows that large urban parks attract wealthy and healthy people and increase the potential for gentrification and displacement of incumbents, as well as create a great deal of automobile traffic. New York’s Central Park is the most obvious of these, but the same development pattern is being repeated in Austin, San Francisco, Tulsa, Chicago, Philadelphia, and around several other city parks in major U.S. cities. Case studies describe approaches proposed and used in many of the cities to respond to the displacement challenge, frankly without much success. The direct challenge is to local government officials to intervene to more equitably balance viewpoints rather than side with real estate interests. Community groups have emphasized the need for upgrading smaller parks rather than focusing on a few large parks and for

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economic and legal tools that would give incumbents financial assistance to remain in their communities and for incentivizing realtors to help residents who want to stay. Zilker Park in Austin and the Gathering Place in Tulsa illustrate the pressure on incumbent residents when new parks open offering walkability, restaurants, and other attributes that young and highly educated people value. Chapter 7 examines the most visited art, history and science museums. Many were built in the mid-19th century and others somewhat later to recognize the sacrifices associated with wars. The message from the data is not subtle. Museums cluster in large metropolitan regions and co-locate with parks, zoos and other assets in neighborhoods where only a select few can afford to live. The area around Central Park, except to the north, the Art Institute of Chicago, the Getty Center (Los Angeles), and the National Mall illustrate the accumulation of wealth leading to exclusive residential communities. The same approach seems to be taking place in Houston and San Diego around museums, parks, and other attractions. Residents of historic Black and Latino neighborhoods in these cities and in Cleveland, Indianapolis and New Orleans have little chance to stay in their neighborhoods without organizing and gaining cooperation from local governments, banks, and realtors. Museums in these settings are economic magnets drawing wealth and investments to them and displacing anyone without considerable economic resources. Ultimately, the challenge posed by these economic magnets is the willingness of local governments, the real estate industry and the community to work together to find more equitable solutions. While these parks draw investment and create wealth for investors, smaller parks in less attractive places are reported as much less likely to attract investments. Part III is about looking outside wealthy and healthy heritage clusters for better solutions. Chapter 8 centers on widely visited commercial and politically symbolic places in every corner of the United States. These include the Alamo, Alcatraz, the Empire State Building, the French Quarter, the area around the Hollywood Sign, the White House and five others. I found that some economically-focused clusters are more likely to have poor and people of cluster nearby, but others are surrounded by wealthy and relatively healthy people. The sites that are most like the heritage attraction clusters are in proximity to incredibly valuable land in New  York, Washington, DC, Los Angeles and Chicago. The most interesting are the Las Vegas strip, Niagara Falls, the Alamo, Disney Park and Mall of America where the government, private interests and communities face difficult challenges providing accessible affordable housing because of their historical economic base was so narrowly focused. Chapter 9 focuses on affordable housing, especially incorporating heritage attractions into plans in New York City, San Francisco, Boston, Kansas City and Chicago. The evidence for combining affordable housing with heritage attractions is interesting, and includes projects in New  York City, Boston, and Chicago. However, my cynical New York City upbringing makes me wonder if these places will eventually become targets for gentrification. The United Kingdom and some northern European states have had much more ambitious social housing programs. However, they started in a different political place than the U.S.  Without a

References

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commitment to affordable social housing, I find it hard to believe that U.S. local government, even its superstar cities will make a great deal of progress without strong support from the federal government, that is, make the commitment to substantially increase their affordable housing portfolio and add heritage attractions to those plans. Chapter 10 is a short epilogue summarizing main findings from the research and exploring the reality that data sets and mapping tools able to monitor changes are rapidly expanding. The epilogue concludes with thoughts about the challenges of coping with low probability – high consequence hazards and cascading effects, demographic change and growing disparities, and managing increasing uncertainty.

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Centers for Disease Control and Prevention (2012) CDC response to advisory committee on childhood lead poisoning prevention recommendations in “low level lead exposure harms children: A renewed call of primary prevention”. Accessed from https://www.cdc.gov/nceh/lead/acclpp/ cdc_response_lead_exposure_recs.pdf. Accessed 22 July 2020 Clark L, Millet D, Marshall J (2014) National patterns in environmental injustice and inequality: outdoor NO2 air pollution in the United States. PLoS One 9(4):e94431. https://journals.plos. org/plosone/article?id=10.1371/journal.pone.00944312 Accessed 22 July 2020 Coates T-N (2014) Case for reparations. The Atlantic. https://www.theatlantic.com/magazine/ archive/2014/06/the-­case-­for-­reparations361631/. Accessed 18 Dec 2021 Daniels R (1997) Not like us: immigrants and minorities in America, 1890–1924. Rowman & Littlefield Demsas J (2021) What we talk about when we talk about gentrification. Vox. September 5, 2021. https://www.vox.com/22629826/gentrification-­definition-­housing-­racism-­segregatin-­cities. Accessed 16 Dec 2021 Elliott M, Wang Y, Lowe R, Kleindorfer P (2004) Environmental justice: frequency and severity of US chemical industry accidents and the socioeconomic status of surrounding communities. J Epidemiol Community Health 58(1):24–30 Gaitens J, Dixon S, Jacobs D, Nagaraja J, Wilson J, Ashley P (2009) Exposure of U.S. children to residential dust lead, 1999-2004: I. Housing and demographic factors. Environ Health Perspect 117(3):461–467 General Accounting Office (1983) Siting of hazardous waste landfills and their correlation with racial and economic status of surrounding communities. GAO, Washington, DC. https://www. gao.gov/products/rced-­83-­168. Accessed 22 July 2020 Gonzales C, Miller J (2015) Fighting for environmental justice in the diesel death zone. http:// climatehealthconnect.org/stories/fighting-­for-­environmental-­justice-­in-­the-­diesel-­death-­zone/. Accessed 22 July 2020 Goulielmos A (2000) European policy on port environmental protection. Global Nest Int J 2(2):189–197 Graham J, Beaulieu N, Sussman D, Sadowitz M, Li Y-C (1999) Who lives near coke plants and oil refineries? An exploration of the environmental inequity hypothesis. Risk Anal 19(1):171–186 Greenberg M, Anderson R (1984) Hazardous waste sites: the credibility gap. Center for Urban Policy Research, New Brunswick, NY, reprinted 2012 by Transaction Publishers, New Brunswick, N.J Greenberg M, Popper F, Truelove H (2012) Are LULUs still enduringly objectionable? J Environ Plan Manag 55(6):713–731 Greenberg M (2021) Ports and environmental justice: a sscreening analysis. Risk Anal 41(11):2112–2126 Harris K (2020) Forty years of falling manufacturing employment. Beyond the numbers BLS 9(16):10. https://www.bls.gov/opub/btn/volume-­9/forty-­years-­of-­falling-­manufacturing-­ employment.htm. Accessed 12 Oct 2021 Houston D, Krudysz M, Winer A (2008) Diesel truck traffic in low-income and minority communities adjacent to ports: environmental justice implications of near-roadway land use conflicts. Transport Res Rec J Transport Res Board 2067:38–46 Houston D, Wei Li WJ (2014) Disparities in exposure to automobile and truck traffic and vehicle emissions near the Los Angeles–Long Beach Port complex. Am J Public Health 104(1):156–164 Jacobs D, Clickner R, Zhou J, Viet S, Marker D, Rogers J, Zeldin D, Broene P, Friedman W (2002) The prevalence of lead-based paint hazards in U.S. housing. Environ Health Perspect 110(10):a599–a606 Karner A, Eisinger D, Bai S, Niemeier D (2009) Mitigating diesel truck impacts in environmental justice communities: transportation planning and air quality in Barrio Logan, San Diego, California. Transport Res Record J Transport Res Board 2125(1):1–8 Lemann N (1991) The promised land: the great black migration and how it changed America. Vintage, New York

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Levy J, Wilson A, Zwack L (2007) Quantifying the efficiency and equity implications of power plant air pollution control strategies in the United States. Environ Health Perspect 115(5):743–750 McMillan B (2021) ‘Gentrification’ is not the real problem. https://shelterforce.org/2021/06/18/a-­case-­to-­stop-­saying-­gentrification/ Accessed 16 Dec 2021 Mills G, Neuhauser K (2000) Quantitative methods for environmental justice assessment of transportation. Risk Anal 20(3):377–384 Mitchell B, Franco J (2016) “Redlining” maps: The persistent structure of segregation and economic inequality. National Community Reinvestment Coalition (NCRC). https://ncrc.org/holc/ Accessed 15 Dec 2021 Mohai P, Saha R (2007) Racial inequality in the distribution of hazardous waste: a rational-level reassessment. Soc Probl 54(3):343–270 Morello-Frosch R, Jesdale BM (2006) Separate and unequal: residential segregation and estimated cancer risks associated with ambient air toxics in U.S. metropolitan areas. Environ Health Perspect 114(3):386–393 Nardone A, Chiang J, Corburn J (2020) Historic redlining and urban health today in U.S. cities. Environ Justice 13:109–119 Ng A, Song S (2010) The environmental impacts of pollutants generated by routine shipping operations on ports. Ocean Coast Manag 53:301–311 Norsworthy M, Craft E (2013) Emissions reduction analysis of voluntary clean truck programs at US ports. Transp Res D 22:23–27 OECD (2011) Environmental impacts of international shipping: the role of ports. OECD Publishing, Paris Pred A (1966) The spatial dynamics of U.S. urban-industry growth, 1800-1914. MIT Press, Cambridge, MA Rosenbaum A, Hartley S, Holder C (2011) Analysis of diesel particulate matter health risk disparities in selected US harbor areas. Am J Public Health 101(S1):S217–S223 Rothstein R (2017) The color of law: a forgotten history of how our government segregated America. Liveright Publishing Company, New York Rowangould G (2013) A census of US near-roadway population: public health and environmental justice considerations. Transp Res Part D: Transp Environ 25:59–67 Ruffin J (2011) A renewed commitment to environmental justice in health disparities research. Am J Public Health 101, S1:S-512-513 Smith N (1996) The new urban frontier: gentrification of the revanchist city. Routledge, New York Taeuber K, Taeuber A (1965) Negroes in cities: residential segregation and neighborhood change. Aldine Publishing, Chicago United Church of Christ (1987) Toxic waste and race in the United States: a national report on the racial and socio-economic characteristics of communities with hazardous waste sites. UCC, New York, NY United Church of Christ, Commission for Racial Justice (2007) Toxic wastes and race at twenty: 1987-2007. https://www.ucc.org/environmental-­ministries_toxic-­waste-­20/ Accessed 22 July 2020 U.S. EPA (2008) Fish consumption and environmental justice. USEPA, National environmental justice advisory council, 2008. https://www.epa.gov/environmentaljustice/epa-­fish-­consumptionand-environmental-justice. Accessed 22 July 2020 U.S. EPA (2007) An environmental management system primer for ports: advancing port sustainability. https://archive.epa.gov/sectors/web/pdf/ems_primer.pdf. Accessed 22 July 2020 U.S.  EPA (2016) Evironmental justice primer for ports. https://www.epa.gov/community-­port-­ collaboration/environmental-­justice-­primer-­ports/. Accessed 1 April 2022 Verter V, Kara B (2001) GIS-based framework for hazardous materials transport risk assessment. Risk Anal 21(6):1109–1120 Vickery J, Hunter L (2016) Native Americans: where in environmental justice research? Soc Nat Resour 29(1):36–52

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Wilson S, Rice L, Fraser-Rahim H (2011) The use of community-driven environmental decision making to address environmental justice and revitalization issues in a port community in South Carolina. Environ Justice 4(3):145–155 World Bank (1990) Environmental considerations for port and harbor developments. Technical Paper WTP 126. World Bank, Washington, D.C. World Bank (2017) Environmental, health, and safety guidelines for ports, harbors, and terminals. https://www.ifc.org/wps/wcm/connect/topics-­ext-­content-­/ifc_external_corporate__ site/sustanainability-­at-­ifc/publications/publications_policy_ehs-­ports-­harbors-­terminals/ April 1, 2020 Zhou Y, Levy J (2007) Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis. BMC Public Health 7(89). https://doi.org/10.1186/1471-­2458-­7-­89 Zhu Y, Hinds W, Kim S, Sioutas C (2002) Concentration and size distribution of ultrafine particles near a major highway. J Air Waste Manag Assoc 52(9):1032–1042

Chapter 2

Designing a Multiple-Scale and Multiple-­Metric Data Analysis

Abstract  This describes the choice of 109 heritage attraction places, more than 30 variables that measure demographic, environmental, health and local attributes, as well as statistical tools. The national park study used county-scale data because of the enormous size of nearly all the national parks, and the reality that few people live near the access points to the most popular parks. Regarding grand concourses, I drew snake-like linear polygons from the center line of the roads out to one-half mile distance in both directions from the center of the roads. Most of the sites are in urban centers, and therefore considerable space is devoted to the use of census tract data, which was a key building block in the urban attraction site studies. I used 1-mile-radius circles in cities and conservative statistics to avoid a few high or low values distorting the results. Data from EJScreen, the 500 cities project, CDC and the U.S. Bureau of the Census were incorporated into the analyses, and I review the strengths and weaknesses of those data sets and underscore the reality that these data bases stand as a remarkable contribution to our ability to assess social and environmental justice issues in the United States. Keywords  Public data bases · EJScreen · PLACES · Health Rankings & Roadmaps · Census tracts · Health outcomes · Demographics · Built environment · Environmental metrics · Simpson’s paradox · Circles · Polygons · Geographic scales · Sample sizes · Data limitations

2.1 Introduction Addressing the objectives described in Chap. 1 required three steps: 1. Identifying the most popular heritage attraction sites and geographical units to assess them. 2. Accessing data that allows consideration of demographic, environmental, health and other attributes of the areas surrounding heritage attraction sites, as well as

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Greenberg, Environmental & Social Justice Challenges Near America’s Most Popular Museums, Parks, Zoos & Other Heritage Attractions, https://doi.org/10.1007/978-3-031-08183-5_2

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using several of these data bases to examine affordable housing policies in some of these same cities and neighborhoods. 3. Searching for a different species of attraction sites to determine how similar they are to the heritage attractions ones.

2.2 Finding Heritage Attraction Sites Heritage attractions include national parks, urban parks, zoos, museums, and grand avenues that address primarily, but not exclusively, American history and culture. The tourist industry and popular media publish ratings of heritage attraction sites. Some of ratings are strictly based on attendance. Yet most also involve assessments based on conversations with peers in the tourist industry and subjective experiences. I read many of these publications and found that they tend to agree on the highest rated sites, with some curious exceptions. For example, I found one study that did not mention New York’s Central Park as among the top 20 urban parks. In contrast, another survey said that Central Park was so popular that it belonged in its own park category. My point is that picking the most popular attraction sites is not an exact science. It allows for subjective evaluation, which is true for best movies, food, news sources, and many consumer-oriented products and services. Part of the site choice decision was how many attraction sites to pick. If I choose 250 or more sites, then unless this book was to be an encyclopedia, it would not be possible to dig into the places and report on any of them in detail. The decision was to pick approximately one dozen heritage sites in each of chapters, that is, about 100 sites. This meant less confidence in the statistical results than for 250 or 500 sites because a few exceptionally low or high values would distort the average values (see Sect. 2.3.2 below). Yet, the tradeoff was worth making because it allowed the space to provide depth on some sites. With the decision made to pick about 100 sites, I collected attraction surveys from the period 2017–2021 (see for example, Attractions of America 2021; Bubochkina 2021; Kulshrestha 2021; Law 2021; Thrillist Travel 2021; Trip Adviser 2021a, b; USA Today 2021; Vacation Idea 2021; Washington Times 2021). Surveys for the year 2020 were not used because of COVID-19-reduced attendance. I settled on a process that began with the top 20 listed in each category, for example, zoos. This initial list, however, was problematic because Boston, Chicago, Los Angeles, New Orleans, New  York City, San Francisco, and Washington D.C. accounted for the clear majority of the urban heritage attraction sites. Precisely following the numbers meant ignoring most of Midwest and South, in other words, focusing on sites that were not even close to representing the geography of the United States, which is important if this book is about the United States, which is its intent. Table 2.1 shows that I chose 109 attraction sites in 29 states and the District of Columbia. Forty-six of the 109 are counties surrounding 18 national parks (see Chap. 3 for details). Each county was compared to its host state. The county was the logical choice for the national park analysis because most of the sites are large covering multiple counties.

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Table 2.1  Number of attraction sites in each state and the District of Columbia State California New Yorka Washington, (State of) Missouri Colorado, Ohio Illinois Tennessee, Utah, Montana, Indiana, Louisiana Washington, D.C.,a Texas, Wyoming Arizona, North Carolina, Maine, Pennsylvania, Massachusetts, Nevada Idaho, Virginia, Michigan, Nebraska, Florida, Maryland, Oklahoma, Minnesota Total (29 states & the District of Columbia)

Number of sites per state 17 11 8 7 6 5 4 3 2

Cumulative 17 28 36 43 55 60 80 89 101

1

109 109

New York City had multiple-site-clusters and Washington D.C. had one clustered site. In other words, New York State had 18 sites combined in a few clusters and Washington, D.C. had several sites combined in one cluster. The centroids for the 1-mile-radius circles were placed on the most important site in the set and all the sites are within a mile of that site, with one exception (see Chap. 8)

a

It is possible to investigate areas on the border of the large national parks. For example, the preface mentioned a trip to Yellowstone National Park. Figure 2.1 is an actual resident of the park who decided to visit our tour bus in January 2017. I stayed in Gardiner, Montana, the north entrance (Fig. 2.2). I learned from EJScreen that Gardiner had an estimated population of 1190 and had lower values of fine particles, less traffic, not many people of color, few people without a high school education and a lower unemployment rate than Montana as a whole (Please note that a few days before I received these proofs, Yellowstone National Park was devastated by flood waters and much of what you see in Fig. 2.2 was destroyed. With three family members, I was about to enter the park, and we were turned back). I also did the same analysis for the community nearest to the other four entrances. The populations varied quite a bit. Trying to characterize the populations of the national parks through the lenses of few who live immediately adjacent to the park entrances parks would produce misleading results about the communities around the large parks. Hence, the national parks are characterized with county data in Chap. 3.  The remaining 63 heritage sites out of 109 are in urban areas. The spatial unit is a 1-mile-radius circle surrounding each heritage site, except for the grand concourses (see below). These 63 were compared to the 3-mile-radius circle around each heritage site and to their host city. In other words, the 1-mile radius circle (3.14 square miles) around the centroid of Chicago’s Grant Park is compared to a 3-mile circle from the same centroid (28.27 square miles) and to Chicago as a whole. Data allows the user to compare each area to the host state and the U.S.. However, I did not emphasize all these comparisons in this report because the immediate surrounding areas and the city are more relevant.

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Fig. 2.1  Friendly Buffalo in Yellowstone Park

An effective way to look at the geography of the places in the study is that New York City is in six case study chapters, Chicago in five, New Orleans in four, and San Diego, San Francisco, St. Louis, Kansas City, and Washington D.C, and Los Angeles in three chapters. East coast and west coast cities dominate the urban sites, and they would have been more dominant had I not deliberately chosen to pick some of the best zoos (Chap. 5), parks (Chap. 6) and museums (Chap. 7) located in Indianapolis, Kansas City, and St. Louis. Overall, New York City, Chicago, New Orleans, Los Angeles, Boston, and San Francisco accounted for the 46% of the urban heritage attraction sites. Had I strictly adhered to rankings and attendance, about two-thirds of the urban attraction sites would have been in and around these cities, which is too many. Regarding the urban grand concourses, they appear as lines that snake across the landscape. Their impact on surrounding populations should decrease with distance from the road. Hence, a one-half mile line from the centerline of the road in both directions was used to capture the major effects of the concourse (see Sect. 2.3.1 below). A final decision about choosing sites was not to be limited to heritage attraction sites. One reason is that if the assertions in Chap. 1 are predictive about U.S.’s housing policies, then there should be other species of attraction sites that produce different outcomes than the heritage ones. For example, sites that are primarily focus on economic activity (e.g., Las Vegas strip) and political messages (the White House) should produce different results than museums and parks. Also, I wanted to

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Fig. 2.2  Gardiner, Montana outside of Yellowstone Park

determine if some cities were using heritage attractions as part of efforts to provide affordable housing (e.g., New York City, Boston). This meant that other species of sites were studied in Chap. 9. Table 2.1 shows the geographical distribution of the 109 sites by state. The West with its many popular national parks had 43% of the heritage sites compared to 24% of the national population. In contrast, the South had 38% of the population and 19% of the attraction sites. The Northeast and Midwest had the same proportion of the population and heritage sites (38% of the population and 39% of the sites).

2.3 Finding and Using Data The most critical point to make about the data used in this report is that the United States has been collecting a great deal of demographic, environment, health and urban amenity data. Yet, the current study was not possible before 2019 because the

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information was not available in a user-friendly publicly accessible data base. By user-friendly and publicly available I mean the data can be downloaded without complex programming instructions and the data can be mapped with a GIS-software incorporated into the data package. The work by the federal government to build and make available data during the last decade is nothing less than a spectacular contribution to understanding the geographical distribution of social, environmental, health and amenities in the United States. Each of the responsible agencies has been clear about data limitations, and their view is that these data are living documents to be augmented in the coming years. In short, I commend the work of the Bureau of the Census, the EPA, CDC, organizations like the Robert Wood Johnson Foundations, and the Council on Environmental Quality (CEQ) (see acknowledgements).

2.3.1 Shapes for Collecting Data: The Census Tract Challenge Metropolitan region, state, county, and city data bases are easy to access and use. Also, they usually contain large numbers of people, which means that if you are studying the proportion of people with a 4-year college degree among those ≥25 years old and one city of 250,000 has 40% college graduates and another had 36%, you can be quite certain that the first has a higher proportion than the second. However, if two adjacent census tracts with only 1000 people present with 40% and 36%, then that difference may be explained by chance, that is, the two tracts arguably have the same number because of the uncertainty associated with small sample sizes. Census tracts and block groups allow a deep dive into differences among small areas with few people, but we need to be careful about what we read into the results. Census tract data in some cities go back to the 1950s, and I used these data for my master’s thesis research about affordable housing in the mid-1960s. The U.S. Bureau of the Census staff does an excellent job of trying to keep within the concept that a census tract has 1000 to 8000 people. However, the number of people in a census tract in my old Bronx neighborhood with a density of over 80,000 per square mile is nothing like a suburb located ten miles north of that neighborhood that has 8000 residents. Furthermore, approximately 10 percent of Americans move every year, and new developments, roads, and other changes on the ground make it challenging to adjust census tract boundaries to the changing landscape. To complicate matters, census tracts have different shapes. Some are rectangles, others look like cigars, and some have odd shapes caused by the reality that they follow hills and river meanders. Comparing census tract data of different shapes and sizes is like comparing three kinds of fruit. Differences in shape can be critical when results are sensitive to distance and direction from a site, which is case in this study of heritage attractions. Specifically, Simpson’s paradox occurs when a trend in one place disappears with distance or even reverses. A pattern that appears in one or two census tracts can change in the next census tract. There is no perfect distance or

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shape that allows users to avoid Simpson’s paradox. What I did was pick one design and apply it to every urban site to be sure that at least I was consistent in organizing the data. EPA’s EJScreen released in 2019 and updated in 2022 allows analysts to pick areas that are triangles, circles, or draw polygons with many shapes. This is an important capability and made an enormous difference in this study. For example, grand concourses are linear paths that might be as short as 3 miles and as long as almost 18 miles (see Chap. 4). Census tracts overlap the concourses. Because of the flexibility of the EJScreen software, I was able draw a polygon along the midpoint of each concourse with a constant one-mile width. The alternative was to gather all the census tract data for each tract that is close to the roads, which means mixing tracts that are entirely or almost entirely close to the site with those that are only partly near the site. Rather than arbitrarily deciding which tracts to include and not include, I drew 1-mile wide snake-like polygons along the concourses. The EJScreen package then estimated all the demographic and environmental data in the polygon. This does not mean that the computer perfectly matches the distribution of people within each grand concourse, but the computer process is much more consistent than an individual analyst. Before committing to the computerized process, I tried to estimate the population in each census tract and realized that this was impossible without visiting every tract, which in the case of this study is over 2000. In short, the GIS-grounded algorithm may not be elegant, but it certainly is consistent, and the process is replicable. Regarding data for the zoos, museums, urban parks, and other sites in Chaps. 5, 6, 7, 8, and 9, I chose to build circles with a 1-radius around the centroids of each site, and then compare those results with three-mile circles around the site and the host city. In other words, I drew a 1-mile-radius circle around the Metropolitan Museum of Art in New York City located on Fifth Avenue and 82nd Street in Central Park. Then I drew a 3-mile circle around the site to compare to the 1-mile circle. These data were then compared to the City of New York as a whole. I repeated this process for every heritage attraction in Chaps. 5, 6, 7, and 8. That is, the analysis is the same for every attraction site, even if the places are different. The GIS tool estimates a value for the entire circle, which means that it is apportions information based on distance from the centroid of the census tract. This introduces estimation errors, but in an imperfect data world this is a much better outcome than an individual analyst deciding about New  York City and other analysts deciding about New Orleans and San Francisco. Overall, EJScreen offers a malleable set of shape choices, which is commendable. The most questionable data in the EJScreen set are the air pollutants because the software reaches out to nearby monitoring stations, each of which has a specific latitude and longitude. Places with a higher density of air quality monitoring stations should have more accurate estimates. The 500 cities project was a marvelous addition to the toolbox for this study. As a person with ongoing interest in public health that I attribute to my bout with childhood asthma, I could never match census tract data with public health data. Some cities divided their space into health districts. However, some would not provide

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their data, and when they did provide data the match with census tract data often was clumsy. This enormous gap for large cities was filled by the 500 cities project. The CDC, the CDC Foundation, and Robert Wood Johnson Foundation provided city- and census tract data for 27 indicators for the 497 most populous cities and three others in Vermont, West Virginia, and Wyoming - - states without a city of 50,000 or more-for the period 2016–2019. The data were published in an interactive “500 cities” website. This truly was a breakthrough, allowing connections between demographic and health data (CDC 2022). The data are available to anyone with a functioning computer, iPad or even cell phone, albeit maps are a challenge to read on small phones. CDC published some excellent papers that explained how they calculated values for so many places, and those publications by themselves are important for analysts. The strengths and the limitations of this data base are striking. Anyone can look up metrics about the health in the area where they live. My students would spend hours using the data from the 500 cities project. Here is a paraphrasing of a class conversation in my undergraduate course: My uncle told my parents that everyone in our neighborhood was coming down with cancer (he meant a cancer cluster). I (the student) didn’t believe it, so I went into EJScreen and the 500 cities data for our census tract and the ones around it and found average levels of cancer cases in the area and less than average values for air contaminants. They (my relatives) were shocked when I reported this information. I told them that they could access these kinds of data. What they needed to do was talk to the city health officer.”

To be clear, this student was smart, motivated, tenacious and this example illustrates how these data may inform people with open minds. In addition to the 500 cities project data, the February 2022 updated version of EJScreen has a mapping tool for several health outcomes, as well as demographic data. The mapping tool results are much more visible than the one from the 500 cities project. Accordingly, nearly all the maps in this book are from the updated EJScreen data set. To be complete, another important data set was released in February 2022. As part of President Biden’s Justice40 Initiative, the CEQ created a Climate and Environmental Justice Screening Tool, which was being beta-tested when this chapter was written. The federal government’s intent is to use that data base in the process of deciding where to allocate infrastructure funds. Those data are not used in this report, as I prefer the others described above. The author expects to see changes to the initial release and has submitted comments.

2.3.2 Selection of Metrics and Statistical Tools Regarding metrics, the best data set was for the National Parks because of the numerous indicators in County Health Rankings and Roadmaps (University of Wisconsin 2022). I gathered 33 metrics at the county scale divided into four categories:

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Demographic (7) Environmental exposure (8) Public Health (10) Built environment (8) These data are described in Chap. 3. The most important points are that the 33 include 10 health and eight built environment variables. I had the most confidence in these data, and this is reflected in the use of parametric statistical tools in Chap. 3 to analyze the 46 counties surrounding the 18 national parks. Regarding the data bases for the urban attraction sites in Chaps. 4, 5, 6, 7, 8 and 9, I focused on 14 indicators. Eleven of the 14 came from EJScreen. The 2019 version of EJScreen (U.S. Environmental Protection Agency 2019) has 18 demographic and environment metrics. The 11 used in this study are in Table 2.2. The version of EJScreen released in February 2022 has the exact same set of variables, with one exception. The unemployment rate was added in the 2022 version.

Table 2.2  Metrics used in Chaps. 4, 5, 6, 7, 8 and 9 Indicators Demographic % Low-income population % People of color % Population with less than high school education % Linguistic isolation/city rate Environment Particulate matter (PM 2.5 in ug/m3) Ozone, ppb NATA diesel pm (ug/m3) Traffic proximity and volume Lead paint indicator Superfund proximity RMP proximity Health^ Physical health last month Without insurance Obesity

Explanation Assumed more vulnerable population Assumed more vulnerable population Assumed more vulnerable population Assumed more vulnerable population

Annual average, of particles 2.5 microns or less in diameter. Numbers derived from a combination of monitoring and modeling. May-September average, ozone level. A combination of monitoring and modeling Diesel particulate matter level in air, National Air Toxics Assessment program focused on 187 hazardous air pollutants Annual average vehicle counts at major roads within 500 meters of block centroid divided by distance in meters % of housing built pre-1960, as an indicator of potential lead exposure. Count of proposed and listed divided by distance in km. Sites within 5 km or nearest one beyond 5 km divided by distance in km. Physical health not good for ≥14 of the last 30 days among adults (≥18 years old) Current lack of health insurance among adults 18–64 years Obesity among adults aged ≥18 years

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Four of the 11 metrics used in Chaps. 4, 5, 6, 7, 8 and 9 are demographic. Low income, people of color, less than high school education and linguistic isolation are good indicators of socioeconomic status and race/ethnicity. Regarding the seven environmental metrics, one set assumes that it is safer not to be located near potential hazards, specifically superfund sites, facilities with risk management plans (RMP), and heavy traffic. The traffic measurement calculates linear distance from nearby heavily traveled roads. Superfund, otherwise known as National Priority List (NPL) sites, are abandoned hazardous waste and industrial sites, some of which were remediated and others that have not. The risk is through direct contact or indirectly through the soil or groundwater, if there is any exposure, which may not be the case for controlled superfund site and RMP facilities. RMP plans are in response to Section 112(r) of the 1990 Clean Air Act amendments which requires facilities that use an extremely hazardous substance(s) to have a risk management plan. The RMP’s are reviewed every five years. Please note that living near an RMP, a superfund site or a major highway site does not imply serious exposure. It does imply potentially serious exposure. Another potential exposure indicator is to lead paint. The actual indicator EPA uses is proportion of housing built prior to 1960. After 1960, the use of lead paint in the U.S. was dramatically reduced. The actual indicators are the U.S. Bureau of the Census indicator of houses built prior to 1960. EJScreen also includes three types of outdoor environmental metrics. Particulates (2.5 pm) and ozone are national ambient air quality standards, and I added diesel because high concentrations have been associated with large industrial complexes with a great deal of truck traffic. The environmental metrics in the first version were for the years 2014–2019 and updated in the 2022 release. Fine particles and diesel particles tend to be geographically localized. Ozone is a regional contaminant that routinely travels hundreds of miles. I expected to find larger spatial differences in the particle measures than in the ozone one. More specifically, my expectation was that heritage attraction sites in urban areas would have the same or even worse air pollution exposures related to motor vehicles than their surrounding areas. The 500 cities project health data was a key addition to the data set. This CDC reported data for cities and census tracts for 27 health indicators for 500 populous U.S. cities. This effort represents the first time health data for so many small areas within cities was made public. The CDC has been collecting health survey data since 1984, but it was this project that made the data available at a scale that allowed people to compare their neighborhoods with others. However, I was concerned about relying on sample data sets in areas with small populations. Given the high standard errors and measurement errors within these data, I picked three of the 27 health indicators. Note that ten were used in the National Park chapter because I consider the data to be more reliable. Regarding the 63 urban places, I picked one health outcome, one health prevention and one unhealthy behavior metric: • Health outcome: Physical health not good for ≥14 of the last 30  days among adults (≥18 years old). The data set also includes arthritis, asthma, high blood

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pressure, cancer, high cholesterol, all teeth lost; a total of 13 metrics). The question’s strength is that it asks people for their subjective judgement. Its limitation is that it is a subjective judgement and only for the latest one month period. • Prevention metrics: Current lack of health insurance among adults 18–64 years. The data set also includes indicators about respondent visits to doctors and dentists, screening for high cholesterol, mammography, cervical cancer and colon cancer, vaccinations, and a total of 9; and • Unhealthy behaviors: obesity among adults aged ≥18  years. The data also includes information about binge drinking; current smoking, no leisure-time physical activity; and sleeping less than 7 hours. Before choosing these three, I downloaded all the 27 metrics, running correlations among them with city-scale data. These three illustrate the potential utility of the CDC’s behavioral risk factor data survey data. On the other hand, at this stage, it is important to not overexpose these data until many analysists have had the opportunity to work with them. In this case, I assumed that if as expected areas with heritage attractions are clustering wealthy people, then these three metrics should find that the 1-mile circles around attraction clusters should have low rates of people with physical health problems, and they should have high rates of insurance and low rates of obesity. In this case, a challenge was that multi-shape options for accumulating data offered by EJScreen were not available for the health data in the 500 cities project. To be consistent with the EJScreen data that allowed every attraction site to be the same size across all the urban centers, I fit the census tract data into the one-mile-­ radius circles and three-mile-radius circles. First, I randomly selected five one-mile-­ radius circles and then tested them for consistency on three occasions during a one-week period. Even with recall bias, I was unable to duplicate the results across the five. This led to the selection of a conservative statistical test to compare the 1and 3-mile-radius circle with each other and their host cities. The problem is that the 1-mile-radius circles with only a few census tract results are overly influenced by a single extremely high or extremely small number. In the grand concourse analyses (Chap. 4), 50–100 census tracts were in the grand concourse polygons, which dampened the impact of a few extreme values. Yet, many of the 1-mile-radius circles had only 4–6 census tracts. A single extremely high or extremely low value strongly influenced the results of the 1-mile-radius areas. This called for non-parametric statistics, that is, I the used the Spearman rank correlation (rho) in Chaps. 4, 5, 6, 7, 8 and 9, not Pearson-R, and use of medians instead of means. A hypothetical example with 10 census tracts illustrates the advantage of using non-parametric statistics. Assume a cluster of 10 census tracts around a zoo has disease rates ranging from 40 to 120 per 100,000 (Table 2.3). The city rate is 55 per 100,000. The average value of this rate across the 10 tracts surrounding the attraction site is 61, which is higher than the city rate of 55. Yet, 8 of the 10 census tracts have lower values than the host city and the median rate is 50. Without ignoring the two high rates, I would suggest that 61 is a poor representation of the clustered

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Table 2.3  Disease rate example (City rate is 55 per 100,000 age-adjusted) Census tract A B C D E F G H I J

Age- adjusted disease rate / 100,000 50 40 120 45 55 50 110 45 50 45

Rate compared to city lower lower higher lower same lower higher lower lower lower

area’s rate. I saw multiple examples of this potentially misleading results in this data base. A clean and conservative way of presenting the results is to use Jenks (1967) method to divide each city’s data into nine categories. The CDC used this method, which is really K-means clustering. Instead of an author downloading raw data for all the census tracts, s/he examines the 9-category maps and uses the sign-test to evaluate the statistical significance of each heritage cluster compared to the city. Table 2.3 contains the data that explains how these tests were made. Table 2.3 shows that census tract E is in the same as the city, whereas census tracts C and G have higher rates, and all the others (A, B, D, F, H, I and J) have lower rates. The sign test asks if the attractions cluster of 10 tracts has a lower rate than the city. The assumption is that the heritage attraction leads to clustering of healthier and wealthier people, in other words, the expectation is that the heritage attraction site acts like a magnet for healthy and wealthy people. Hence, I used a one-tailed statistical test. The test example has 10 cases, but the test does not count tracts with the same value as the city, which means that census tract E is excluded from the results. The statistical question is what is the probability that 7 of the 9 tracts would have better health values than the city. The probability is P = .09. In other words, unlike the message from the mean rate of 61, which is that the attraction cluster has higher rates than the city, the 1-mile-radius area in this hypothetical example shows healthier rates than the city, noting however that the two high-value tracts are outliers that should not be ignored. Regarding Chaps. 5, 6, 7, and 8, I applied the same process to the 1-mile cluster site compared to the 3-miles area surrounding the heritage site and then to the city. For example, supposing 9 of the 1-mile-radius circles around 13 urban parks had lower poverty rates than their host cities, 2 were in the same category and 2 were higher. What is the probability that 9 of 11 would have lower values? The probability is 3.3%. If the number was 8 of 11, then the probability is 11.3%, and if it is 7 of

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11, then the value is 27.4%. In other words, with small numbers of cases, the sign test is a conservative way of evaluating the likelihood that the influence of the heritage attraction is significant. All the numbers in the third table in Chaps. 5, 6, 7, and 8 used the sign test. In each case, the expectation is that the heritage attraction brings people to the cluster. I also tried the Wilcoxon signed-rank test, which has more statistical power, but requires a ranking of the census tracts and I did not feel was justified given the small number of census tracts in some of the comparisons. Table 5.3, 6.3, 7.3, and 8.3 of Chaps. 5, 6, 7, and 8 are presented as percentages of attractions sites with lower values than their host city and then adjacent 3-mile zone. For example, in the urban parks chapter, the 1-mile-radius zones had a score of 92 and a score of 83 compared to the 3-mile-radius area for without insurance. The score of 92 means that the 1-mile radius circles had less likelihood of having no insurance in 11 of 12 comparisons. These are strong findings. On the other hand, regarding diesel particles the numbers were 8 and 50, which means that the 1-mile radius zones have higher or the same diesel particle values, which was not surprising given motor vehicle traffic near some of these sites.

2.3.3 Displaying the Data as Maps Zoos, urban and national parks, and museums imply pictures and photographs of some of the most beautiful places and cultural treasures in the United States. Books and web sites deliver photography of the places in this book. Figures 2.1 and 2.2 were such photos. This book is not about the beauty of the attraction sites. It is about the people who live near them. Hence, most of the figures in this book are from the 2022 version of EJScreen. Figure 2.3 is an illustration of the value of this mapping tool. The 1-mile radius circle is centered on the Empire Casino and Yonkers Raceway in Yonkers, New York (see preface). To the south of the circle is the north Bronx, to the west is the older area of Yonkers, and to the east is the City of Mount Vernon. The map shows a clear distinction in proportion of people of color. This is historic place because the city of Yonkers was accused of having segregated schools and of refusing to build-court-ordered affordable housing that would accommodate minorities. Yonkers was the first city in the United States to be sued by the federal government for both segregated schools and segregated housing. The suit took decades to settle. The city eventually settled including re-organizing all its public schools. The lingering effect of this segregation remains visible in the map. The health data presented in the updated version of EJScreen is for premature deaths, asthma rates, and heart disease. These are used as maps. The 500 city project data used in tables is about insurance, physical health during the last month, and obesity. The two data sets are not comparable because one is measured against the host city and the EJScreen data is measured against the U.S. However, these metrics are associated. I could not recreate the data for the low life expectancy in the EJScreen 2022 update. However, I could for the other five metrics across 26 host cities in this book. Table 2.4 shows the Pearson R correlation value among the five

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Fig. 2.3  Yonkers-Bronx border and segregation

Table 2.4  Correlations among five health indicators and low income population across 26 cities in the book Metric Heart disease & physical health during the last month Low income & physical health during the last month Low income & heart disease Heart disease and obesity Low income & physical health during the last month Low income & obesity Heart disease & asthma rate Low income & lack of insurance Physical health during the last month & lack of insurance Obesity & asthma rate Physical health during the last month & asthma rate Low income & asthma rate Obesity & lack of insurance Heart disease & lack of insurance Lack of insurance & asthma rate

Pearson-R correlation .89**

**Correlation statistically significant at P