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 tobranch out methodologically and for practicing planners who need to conduct advancedanalyses with planning data.

Chapter 1: Introduction

Companion book: Basic Quantitative Research Methods for Urban Planners

Structure of the advanced methods book

Techniques not included in this book

Data and Measurements

Conceptual Framework

Statistics

Chapter Structure

Datasets

Computer Software Used In This Book

Chapter 2: Technical Writing

Overview

Purpose

Preliminaries

Mechanics

Rewriting, Editing, and Polishing

Literature Reviews

Planning Examples

Conclusion

Chapter 3: Planning Journals and Topics

Overview

Planning Journals

Impact Factors

Peer Review

Overview of Planning Topics

Methodological Issues

Climate Change and the Natural Environment

Social Justice Issues

Land-Use and Development Regulations

Sprawl, Travel, and the Built Environment

Urban Design

Other Topics

Conclusion

Chapter 4: Poisson and Negative Binomial Regression Analysis

Overview

Purpose

History

Mechanics

Interpreting Results

Step by Step

Planning Examples

Conclusion

Chapter 5: Principal component and factor Analysis

Overview

Purpose

History

Mechanics

Interpreting Results

Step by Step

Planning Examples

Conclusion

Chapter 6: Cluster Analysis

Overview

Purpose

History

Terminology

Methodology

Step by Step

Planning Examples

Conclusion

Chapter 7: Multilevel Modeling

Overview

Purpose

History

Mechanics

Step by Step

Planning Examples

Conclusion

Chapter 8: Structural Equation Modeling

Overview

Purpose

History

Mechanics

Interpreting Results

Step by Step

Planning Examples

Conclusion

Chapter 9: Spatial Econometrics

Overview

Purpose

Spatial Data

History

Mechanics

Step by Step 1: Spatial Data Analysis

Step by Step 2: Spatial Econometrics

Planning Examples

Conclusion

Chapter 10: Meta-Analysis and Meta-Regression

Overview

History

Purpose and Mechanics

Planning Examples

Chapter 11: Mixed Methods Research

Overview

Purpose

History

Mechanics

Planning Examples

Conclusion

List of contributors

Index

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.