1st Edition

Big Data for Regional Science

Edited By Laurie A Schintler, Zhenhua Chen Copyright 2018
    376 Pages
    by Routledge

    376 Pages 84 B/W Illustrations
    by Routledge

    Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community.

    This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science.

    Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.


    List of Figures

    List of Tables

    List of Contributors

    Foreword by Michael Batty

    1. Introduction

    Laurie A. Schintler and Zhenhua Chen

    PART I New Big Data Source in Regional Science

    2. Opportunities for Retail Data and Their Geographic Integration in Social Science

    Guy Lansley and Paul Longley

    3. Use of Probe Data Generated by Taxis

    Josep Maria Salanova, Michal Maciejewski, Joschka Bischoff, Miquel Estrada Romeu, Panagiotis Tzenos, and Iraklis Stamos

    4. The Emerging Geography of Globalizing Chinese Cities Based on Web-based Information Services

    Jean-Claude Thill, Jae Soen Son, and Min Chen

    5. Using Web Crawled Data for Urban Housing Research

    Zhenhua Chen

    6. Examining Intraurban Migration in the Twin Cities Metropolitan Area using Parcel Data

    Shipeng Sun

    7. Crowdsourcing Street Beauty: Visual Preference Surveys in the Big Data Era

    Robert Goodspeed and Xiang Yan

    8. Public Response to Campus Shootings Using Social Media

    Xinyue Ye, Zhuo Chen, and Shengwen Li

    PART II Big Data Integration and Management

    9. Using Big (Synthetic) Data to Identify Local Housing Market Attributes

    A.Yair Grinberger and Daniel Felsenstein

    10. Using Recurrent Spatio-Temporal Profiles in GPS Panel Data for Enhancing Imputation of Activity Type

    Tao Feng and Harry J.P. Timmermans

    11. Processing Uncertain GPS Trajectory Data for Assessing the Locations of Physical Activity

    Sungsoon Hwang, Sai Yalla, and Ryan Crews

    12. Exploring Digital Technology Industry Clusters Using Administrative and Frontier Data

    Max Nathan and Anna Rosso

    13. The Integration of Internet Data and Census Data for Spatial Analysis in a


    Laurie A. Schintler is a computational social scientist with interests and research activity in the following areas related to Big Data analytics: geocomputation (socio-spatio modelling), transportation, regional science, scientometrics/bibliometrics and network modeling and analysis. She also has expertise on the policy-side of Big Data - specifically, issues related to the digital divide, job automation, workforce education and training and emerging technologies.

    Zhenhua Chen is an assistant professor in City and Regional Planning at the Knowlton School of Architecture at The Ohio State University. His research interest includes regional science, big data analytics, risk and resilience, infrastructure planning and policy.