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Big Data for Regional Science [Hardback]

Edited by (University of Southern California, USA), Edited by
  • Formāts: Hardback, 350 pages, height x width: 234x156 mm, weight: 657 g, 55 Tables, black and white; 84 Line drawings, black and white; 84 Illustrations, black and white
  • Sērija : Routledge Advances in Regional Economics, Science and Policy
  • Izdošanas datums: 15-Aug-2017
  • Izdevniecība: Routledge
  • ISBN-10: 1138282189
  • ISBN-13: 9781138282186
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  • Cena: 210,77 €
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  • Formāts: Hardback, 350 pages, height x width: 234x156 mm, weight: 657 g, 55 Tables, black and white; 84 Line drawings, black and white; 84 Illustrations, black and white
  • Sērija : Routledge Advances in Regional Economics, Science and Policy
  • Izdošanas datums: 15-Aug-2017
  • Izdevniecība: Routledge
  • ISBN-10: 1138282189
  • ISBN-13: 9781138282186
Citas grāmatas par šo tēmu:
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
ix
List of tables
xiii
List of contributors
xv
Foreword xxi
1 Introduction
1(8)
Laurie A. Schintler
Zhenhua Chen
PART I New big data sources in regional science
9(98)
2 Opportunities for retail data and their geographic integration in social science
11(11)
Guy Lansley
Paul Longley
3 Use of probe data generated by taxis
22(16)
Josep Maria Salanova
Michal Maciejewski
Joschka Bischoff
Miquel Estrada Romeu
Panagiotis Tzenos
Iraklis Stamos
4 The emerging geography of globalizing Chinese cities based on web-based information services
38(14)
Jean-Claude Thill
Jae Soen Son
Min Chen
5 Using web-crawled data for urban housing research
52(12)
Zhenhua Chen
6 Examining intraurban migration in the Twin Cities metropolitan area using parcel data
64(11)
Shipeng Sun
7 Crowdsourcing street beauty: Visual preference surveys in the big data era
75(19)
Robert Goodspeed
Xiang Yan
8 Public response to campus shootings using social media
94(13)
Xinyue Ye
Zhuo Chen
Shengwen Li
PART II Big data integration and management
107(82)
9 Using big (synthetic) data to identify local housing market attributes
109(12)
A. Yair Grinberger
Daniel Felsenstein
10 Using recurrent spatio-temporal profiles in GPS panel data for enhancing imputation of activity type
121(10)
Tao Feng
Harry J.P. Timmermans
11 Processing uncertain GPS trajectory data for assessing the locations of physical activity
131(12)
Sungsoon Hwang
Sai Yalla
Ryan Crews
12 Exploring digital technology industry clusters using administrative and frontier data
143(10)
Max Nathan
Anna Rosso
13 The integration of Internet data and census data for spatial analysis in a geoportal
153(11)
Bing She
Hua Li
Shuming Bao
14 Big data, socio-environmental resilience and urban systems planning support
164(12)
Brian Deal
Aaron Petri
Haozhi Pan
Stephanie Timm
15 Big data perspectives: Adoption of a regional environmental information system
176(13)
Andrea De Montis
Sabrina Lai
Nicoletta Sannio
Gianluca Cocco
PART III Big data analytics in regional science
189(86)
16 From `big data' to big regions: The geography of the American commute
191(13)
Alasdair Rae
Garrett Nelson
17 Big data, agents and the city
204(10)
Andrew Crooks
Nick Malleson
Sarah Wise
Alison Heppenstall
18 Damage assessment of the urban environment during disasters using volunteered geographic information
214(15)
Carolynne Hultquist
Elena Sava
Guido Cervone
Nigel Waters
19 Integrating big data into a geospatial framework of disaster impact analysis
229(14)
Yuri Mansury
Sutee Anantsuksomri
Nij Tontisirin
20 A big data application of spatial microsimulation for neighborhoods in England and Wales
243(14)
Michelle A. Morris
Stephen Clark
21 Big data clustering and its applications in regional science
257(8)
Yazhou Ren
22 Big data and shrinking cities: How Twitter can help determine urban sentiments
265(10)
Justin B. Hollander
Maxwell D. Hartt
PART IV New frontiers of big data in regional science
275(72)
23 Big data in emerging cities
277(15)
Pranab K. Roy Chowdhury
Susanna H. Sutherland
Kathleen M. Ernst
Alexander Pawlowski
Erik H. Schmidt
Janna R. Caspersen
Ziliang Zhao
Budhendra L. Bhaduri
24 Recommendations for big data programs at transportation agencies
292(12)
Gregory D. Erhardt
Michael Batty
Elsa Arcaute
25 Towards data-driven cities: Incorporating big data into urban management
304(11)
Hossein Estiri
Nader Afzalan
26 Big data, privacy and the policy process in the United States: In regional economic development
315(9)
Roger Stough
Dennis Mcbride
27 Urban informatics: Defining an emerging field
324(12)
Robert Goodspeed
28 The constantly shifting face of the digital divide: Implications for big data, urban informatics and regional science
336(11)
Laurie A. Schintler
Index 347
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.