Supporting Shrinkage

George R. Chichirau, Eliza W. Kinsey, Justin B. Hollander, Michael P. Johnson

Description

Supporting Shrinkage describes a new approach to citizen-engaged, community-focused planning methods and technologies for cities and regions facing decline, disinvestment, shrinkage, and social and physical distress. The volume evaluates the benefits and costs of a wide range of analytic approaches for designing policy and planning interventions for shrinking cities and distressed communities. These include collaborative planning, social media, civic technology, game design, analytics, decision modeling and decision support, and spatial analysis. The authors present case studies of three US cities addressing shrinkage and decline, with a focus on issues of social justice, democratization of knowledge, and local empowerment. Proposed as a solution is an approach that puts community engagement and empowerment at the center, combined with data and technology innovations. The authors argue that decisions informed by qualitative and quantitative data and analytic methods, implemented through accessible and affordable technologies, and based on notions of social impact and social justice, can enable residents to play a leading role in the positive transformation of shrinking cities and distressed communities.
List of Tables and Figures
Acknowledgments

1. Planning, Technology, and Shrinking Cities

2. What Can Data and Technology Do for Shrinking Cities and Distressed Communities?

3. Three Shrinking Cities: History, Practice, Data, and Technology

4. Data and Modeling Preliminaries: An Application to Fall River, Massachusetts

5. Shrinking City Data and Decision Modeling: Baltimore, Maryland

6. Technology, Data, and Community-Building Where People Matter

7. Lessons Learned: How Can Data, Models, and Technology Support Shrinking Cities and Distressed Communities?

Works Cited
About the Authors
Index

Sujets

• Political economy

Informations

• Publié par: State University of New York Press
• Date de parution: 01 août 2021
• Nombre de lectures: 0
• EAN13: 9781438483474
• Langue: English

Extrait

SUPPORTING SHRINKAGE
SUPPORTING SHRINKAGE
Planning and Decision Making for Legacy Cities
Michael P. Johnson, Justin B. Hollander, Eliza W. Kinsey, and George R. Chichirau with Charla Burnett
Published by State University of New York Press, Albany
© 2021 State University of New York
All rights reserved
Printed in the United States of America
No part of this book may be used or reproduced in any manner without written permission. No part of this book may be stored in a retrieval system or transmitted in any form or by any means including electronic, electrostatic, magnetic tape, mechanical, photocopying, recording, or otherwise without the prior permission in writing of the publisher.
For information, contact State University of New York Press, Albany, NY
www.sunypress.edu
Library of Congress Cataloging-in-Publication Data
Names: Johnson, Michael P., author | Hollander, Justin B., author | Kinsey, Eliza W., author | Chichirau, George R., author.
Title: Supporting shrinkage / Michael P. Johnson, Justin B. Hollander, Eliza W. Kinsey, and George R. Chichirau.
Description: Albany : State University of New York Press, [2021] | Includes bibliographical references and index.
Identifiers: ISBN 9781438483450 (hardcover : alk. paper) | ISBN 9781438483474 (ebook)
Further information is available at the Library of Congress.
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Contents
L IST OF T ABLES AND F IGURES
A CKNOWLEDGMENTS
C HAPTER 1 Planning, Technology, and Shrinking Cities
C HAPTER 2 What Can Data and Technology Do for Shrinking Cities and Distressed Communities?
C HAPTER 3 Three Shrinking Cities: History, Practice, Data, and Technology
C HAPTER 4 Data and Modeling Preliminaries: An Application to Fall River, Massachusetts
C HAPTER 5 Shrinking City Data and Decision Modeling: Baltimore, Maryland
C HAPTER 6 Technology, Data, and Community-Building Where People Matter
C HAPTER 7 Lessons Learned: How Can Data, Models, and Technology Support Shrinking Cities and Distressed Communities?
W ORKS C ITED
A BOUT THE A UTHORS
I NDEX
Tables and Figures
Tables 1.1 U.S. Cities with the Highest Absolute Population Loss, 1950–2010 1.2 Candidate U.S. Cities for Shrinkage and Distress Analysis 1.3 Changes in Population and Vacancy Measures in Candidate U.S. Cities 1.4 Sample Cities Description 3.1 Sample Cities: Selected Characteristics 3.2 Population Changes, 1900–2016, Sample Cities 3.3 Changes in Population Race/Ethnicity, 1970–2016, Sample Cities 3.4 Changes in Housing Economic Characteristics, 1970–2016, Sample Cities 3.5 Population Race/Ethnicity in Flint: 1970–2016 3.6 Flint Housing Economic Characteristics: 1970–2016 3.7 Population Race/Ethnicity in Baltimore: 1970–2016 3.8 Baltimore Housing Economic Characteristics: 1970–2016 3.9 Population Race/Ethnicity in Fall River: 1970–2016 3.10 Fall River Housing Economic Characteristics: 1970–2016 3.11 Comparing Sample Cities: Primary City Goals 3.12 Comparing Sample Cities: Primary Decision-Making Systems and Processes 3.13 Comparing Sample Cities: Future Uses Considered 4.1 “Idea Space” for Data and Tech-Inspired Interventions in Shrinking Cities and Distressed Communities 5.1 Vacant Land Planning Model: Criteria for Clusters 5.2 Strengths and Weaknesses Identified in City Demolition Cluster Selection Process 5.3 Cross-Case Comparison of Process and Outcomes 6.1 Benefits and Costs of Big Data/Smart Cities Innovations for Shrinking Cities and Distressed Communities
Figures 3.1 Population Change in Flint, 2000–2016 3.2 Nonwhite Population in Flint: Nonwhites as Percentage of Population 3.3 Housing Vacancy in Flint: Percentage of Housing Units Classified as Other, Vacant 3.4 Abandoned Home in Flint 3.5 Shuttered Public School in Flint 3.6 Typical Row House in Baltimore Next to Vacant Lot 3.7 Urban Agriculture Is a Common Use for Large Blocks of Vacant Land in Baltimore 3.8 Population Change in Baltimore, 2000–2016 3.9 Housing Vacancy in Baltimore: Percentage of Housing Units Classified as Other Vacant 3.10 Nonwhite Population in Baltimore: Nonwhites as Percentage of Population 3.11 Homegrown Baltimore’s Linkage to Baltimore City Initiatives 3.12 Typical Residential Neighborhood in Fall River 3.13 Street View of Quequechan Mills District in Fall River 3.14 Population Change in Fall River, 2000–2016 3.15 Housing Vacancy in Fall River, 2016: Percentage of Housing Units Classified as Other, Vacant 3.16 Nonwhite Population in Fall River: Nonwhites as Percentage of Population 4.1 Neighborhood-Level Planning Model Objective Space Results: Corner Solutions and Two Compromise Solutions 4.2 Neighborhood-Level Planning Model Decision Space Results: Two Non-Dominated Solutions 4.3 Neighborhood-Level Planning Model Decision Space Results: Compromise Solution—Residential and Non-Residential Investments 5.1 Examples of “Demolition Clusters” in the City of Baltimore 5.2 Interview Map 5.3 Means-Ends Network for Smart Shrinkage Decision Problem 5.4 Vacant Land Planning Model Objective Space Results: Value Chart 5.5 Vacant Land Planning Model Decision Space Results: Compromise Solutions Associated with Objective Function Weights Suggested by City of Baltimore 6.1 Diagram Representing Constructs, Aspects, Premises, and Relations (Arrows) between the Constructs Comprising the Enhanced Adaptive Structuration Theory 2 (EAST2) Framework
Acknowledgments
T he research in this book was supported by The Abell Foundation, “Decision Modeling Tool for Vacant Structure Demolition and Redevelopment,” January 1 to December 31, 2013. We are grateful to the cooperation of the City of Flint Planning Division, Fall River Planning Department, and the Baltimore City Planning Department for ideas, data, interviews, and comments that made our research possible.
Our work benefitted greatly from the contributions of many research assistants over the course of this project: University of Massachusetts Boston students Merritt Hughes (PhD ’17), Hyun-Jung Lee (PhD ’18), Heather MacLean (MS ’21), and Omobukola Usidame (PhD ’18), and Tufts University student Jingyu Tu (MS ’15). University of Massachusetts Boston Public Policy PhD students Jason Wright, Liz James, Shengli Chu and Jamie Lannon, and Tufts University Urban and Environmental Policy and Planning masters student Sarah Cohen served as editorial assistants.
We appreciate the detailed and thoughtful feedback of anonymous reviewers.
We thank our families, employers, and colleagues for their support and guidance.
CHAPTER 1
Planning, Technology, and Shrinking Cities
1.1 Introduction: Policy, Planning Context, and Book Goals
M unicipal decline or urban shrinkage has been the subject of extensive academic research 1 and many recent stories in the popular press. 2 In the United States, a relatively large number of cities and regions have experienced increased distress over the past two decades, according to measures relating to population and economic decline, or vacant and abandoned housing. These changes have significantly reduced the quality of life for residents: extreme examples include severely degraded infrastructure in Flint, Michigan, and social unrest in Baltimore, Maryland. Declining urban centers usually fall into two large categories: the “legacy” cities of the postindustrial regions of New England, the Mid-Atlantic, and the Midwest that have dealt with industrial transition and suburban flight for many decades now (Mallach and Brachman 2013), as well as cities in the Sunbelt that have borne the brunt of the post-2008 foreclosure crisis and related abandonment of housing in suburban and suburban-style subdivisions (Hollander 2011). These trends are not limited to the United States: cities in Europe, Asia, and Africa also confront decline and shrinkage (Stohr 2004).
The reverse of the coin is that many other cities have become increasingly attractive over the past decade, as millennials and baby boomers alike recognize the benefits of easily accessible jobs and cultural amenities associated with urban living (Wieckowski 2010; Frey 2014; Nielson 2014). A recent best-selling book by Fallows and Fallows (2018) has made a highly persuasive case for the social and economic value of smaller cities, even in decline. Recent U.S. Census data is relatively ambiguous, although it seems to indicate a moderate resurgence in suburban population growth rates as compared to traditional cores (Frey 2017). It is thus probably best to not oversell any one storyline regarding central city versus suburban growth, and overall trends of urban growth versus decline (even more so in the wake of the 2020 coronavirus crisis).
Community distress is usually a corollary of the dynamic of shrinking cities (Beauregard 2009). To provide an example, the Economic Innovation Group (2018) measures community distress using an index composed of seven equally weighted components:
1. the percentage of the adult population without a high school diploma;
2. the housing vacancy rate;
3. the percentage of adult nonelderly population not currently employed;
4. the poverty rate;
5. the median household income as a percent of the state’s median household income;
6. the percent annual change in the number of jobs; and
7. the percent annual change in the number of business establishments.
By comparing distress measures between 2007 and 2011, the depths of the Great Recession, and 2012 and 2016, an era of sustained economic recovery, the EIG found evidence of migration from distressed zip codes toward more prosperous ones (the bottom and top quint