ADVANCED QUANTITATIVE RESEARCH METHODS FOR URBAN PLANNERS Advanced Quantitative Research Methods for Urban Planners provides fundamental knowledge and hands-on techniques about research, such as research topics and key journals in the planning field, advice for technical writing, and advanced quantitative methodologies This book aims to provide the reader with a comprehensive and detailed understanding of advanced quantitative methods and to provide guidance on technical writing Complex material is presented in the simplest and clearest way possible using real-world planning examples and making the theoretical content of each chapter as tangible as possible Hands-on techniques for a variety of quantitative research studies are covered to provide graduate students, university faculty, and professional researchers with useful guidance and references A companion to Basic Quantitative Research Methods for Urban Planners, Advanced Quantitative Research Methods for Urban Planners is an ideal read for researchers who want to branch out methodologically and for practicing planners who need to conduct advanced analyses with planning data Reid Ewing, PhD, is Distinguished Professor of City and Metropolitan Planning at the University of Utah, associate editor of the Journal of the American Planning Association and Cities, and columnist for Planning magazine, writing the column “Research You Can Use.” He directs the Metropolitan Research Center at the University He holds master’s degrees in Engineering and City Planning from Harvard University and a PhD in Urban Planning and Transportation Systems from the Massachusetts Institute of Technology A recent citation analysis found that Ewing, with 24,600 citations, is the sixth most highly cited among 1,100 planning academic planners in North America Keunhyun Park, PhD, is an assistant professor in the Department of Landscape Architecture and Environmental Planning at Utah State University He holds bachelor’s and master’s degrees in Landscape Architecture from Seoul National University and a PhD in Metropolitan Planning, Policy, Design from the University of Utah His research interests include technology-driven behavioral research (e.g drone, VR/AR, sensor, etc.), behavioral outcomes of smart growth, and active living ADVANCED QUANTITATIVE RESEARCH METHODS FOR URBAN PLANNERS Edited by Reid Ewing and Keunhyun Park First published 2020 by Routledge 52 Vanderbilt Avenue, New York, NY 10017 and by Routledge Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Taylor & Francis The right of Reid Ewing and Keunhyun Park to be identified as authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Library of Congress Cataloging-in-Publication Data A catalog record for this title has been requested ISBN: 978-0-367-34327-9 (hbk) ISBN: 978-0-367-34326-2 (pbk) ISBN: 978-0-429-32503-8 (ebk) Typeset in Bembo by Apex CoVantage, LLC CONTENTS 1 Introduction Divya Chandrasekhar, Fatemeh Kiani, Sadegh Sabouri, Fariba Siddiq, and Keunhyun Park Companion Book: Basic Quantitative Research Methods for Urban Planners 2 Structure of the Advanced Methods Book Techniques Not Included in This Book Data and Measurements Conceptual Framework Statistics 8 Chapter Structure Datasets 10 Computer Software Used in This Book 15 Technical Writing Robin Rothfeder and Reid Ewing Overview 19 Purpose 20 Preliminaries 26 Mechanics 28 Rewriting, Editing, and Polishing 36 Literature Reviews 37 Planning Examples 39 Conclusion 43 19 vi Contents Planning Journals and Topics Kathryn Terzano, David Proffitt, Fariba Siddiq, and Reid Ewing 46 Overview 46 Planning Journals 47 Impact Factors 48 Peer Review 50 Overview of Planning Topics 51 Methodological Issues 51 Climate Change and the Natural Environment 56 Social Justice Issues 58 Land Use and Development Regulations 60 Sprawl,Travel, and the Built Environment 60 Urban Design 68 Other Topics 69 Conclusion 71 Poisson and Negative Binomial Regression Analysis Anusha Musunuru, David Proffitt, Reid Ewing, and William H Greene 74 Overview 74 Purpose 75 History 75 Mechanics 76 Interpreting Results 79 Step by Step 80 Planning Examples 87 Conclusion 92 Principal Component and Factor Analysis Matt Wheelwright, Zacharia Levine, Andrea Garfinkel-Castro, Tracey Bushman, and Simon C Brewer 95 Overview 95 Purpose 95 History 97 Mechanics 98 Interpreting Results 103 Step by Step 104 Planning Examples 114 Conclusion 118 Cluster Analysis Andrea Garfinkel-Castro,Tracey Bushman, Sadegh Sabouri, Simon C Brewer, Yu Song, and Keunhyun Park Overview 121 Purpose 122 History 122 121 Contents vii Terminology 123 Methodology 125 Step by Step 128 Planning Examples 147 Conclusion 151 Multilevel Modeling Zacharia Levine, Robert Young, Roger Child, Brian Baucom, Reid Ewing, and John Kircher 154 Overview 154 Purpose 155 History 157 Mechanics 158 Step by Step 163 Planning Examples 178 Conclusion 182 Structural Equation Modeling Matt Miller, Ivana Tasic,Torrey Lyons, Reid Ewing, and James B Grace 185 Overview 185 Purpose 186 History 188 Mechanics 189 Interpreting Results 191 Step by Step 193 Planning Examples 209 Conclusion 214 Spatial Econometrics Keuntae Kim and Simon C Brewer 216 Overview 216 Purpose 217 Spatial Data 217 History 218 Mechanics 219 Step by Step 1: Spatial Data Analysis 238 Step by Step 2: Spatial Econometrics 246 Planning Examples 252 Conclusion 259 10 Meta-Analysis and Meta-Regression Mark Stevens,Torrey Lyons, and Reid Ewing Overview 261 History 262 261 viii Contents Purpose and Mechanics 263 Planning Examples 268 11 Mixed-Methods Research Adam Millard-Ball and Keuntae Kim 275 Overview 275 Purpose 275 History 277 Mechanics 278 Planning Examples 283 Conclusion 285 List of Contributors 288 Index291 INTRODUCTION Divya Chandrasekhar, Fatemeh Kiani, Sadegh Sabouri, Fariba Siddiq, and Keunhyun Park The world is an increasingly complex place, and the tools we use to understand it are also growing in sophistication There are many reasons for this Quantitative researchers of today wish to understand the world more holistically—to move away from traditional methods that isolate phenomena in order to study them and to move toward ways of studying phenomena within their broader (but also deeper) context Researchers of today also desire to push the field of quantitative analysis beyond its historical legacy of what? questions to questions of how? and why? Researchers in the field of planning are not an exception With the adoption of rigorous techniques, researchers are seeking to address the complex and multifaceted issues in urban planning In this, they are aided by the changing nature of the data: More studies are employing mixed methods designs, producing more discrete and categorical data, and doing so in much larger quantities The advent of big data provides tremendous explanatory power to quantitative research, but it also demands methodological innovations that embrace the complexity of data instead of rejecting it Changing times need novel ways of thinking, and the purpose of this book is to introduce urban planning researchers to some of these novel, sophisticated ways This book has two main objectives: first, to provide the reader with a comprehensive and detailed understanding of innovative, advanced quantitative methods in urban planning, and second, to provide guidance on technical writing since much of scientific advancement is predicated upon effective communication of research findings To the editors’ knowledge, there is no such book with detailed guidance on the use of advanced research methods and their applicability in urban planning research The audience for this book is primarily doctoral students and early career researchers in urban planning, although those in allied fields such as geography, public administration, public health, and sociology may also find it useful The readers of the book are expected to have a basic knowledge of statistics and quantitative research Descriptive statistics, t-test, ANOVA test, correlation, and chi-square have been referred to in different chapters of this book Particularly, understanding regression analysis is critical because more advanced methods, such as multilevel modeling, Poisson regression, and structural equation modeling, are subject to the same caveats and limitations as 240 Keuntae Kim and Simon Brewer the EPSG numeric codes to set the projection More information about these codes can be found at the EPSG website (http://spatialreference.org/ref/epsg/) proj4string(hts.sp) = CRS("+init=epsg:4326") proj4string(hts.sp) ## [1] "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" If we recheck the projection string, you will see that this has added information about the projection datum, ellipsoid, and so on We now make a simple map showing the location of the households We first modify the figure margins, then plot simple circles, where the color is specified by the col parameter as an RGB triplet, with each color specified as a value between and Note that the fourth number in the rgb() function specifies the transparency level hts.sp, pch=16, cex=.5, col=rgb(1, 0.55, 0, 0.2), plot( axes=TRUE) To improve this map, we can overlay other spatial layers We first import an existing shapefile of census tract boundaries (tract10.shp; a shapefile for Washington state available from the online resource page of this book), using the readOGR() function from the rgdal package In contrast to the point data, this is a set of polygons describing the tracts and is stored in R as a SpatialPolygonsDataFrame: library(rgdal) ct.sp