This set includes the third edition of Computational Methods and GIS in Social Science and the accompanying lab manual. It integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition of the textbook, and its accompanying lab manual, showcase the best practices of spatial computational social science and include numerous new examples and case studies with step-by-step instructions in ArcGIS Pro, and open-source platform KNIME. KNIME supports visual programming and multiple scripting languages such as R, Python, and Java. It helps readers sharpen their GIS skills by applying GIS techniques in detecting spatiotemporal crime hot spots, measuring the accessibility of primary care physicians, forecasting the impact of hospital closures on the local community, or siting the best locations for business.
Features
- Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME
- Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy with clear step-by-step instructions
- Provides newly automated programs for regionalization, functional region delineation, accessibility measures, maximal accessibility equality problem, and agent-based crime simulation
- Includes 22 case studies from USA and China that parallel the methods developed in the textbook and enable readers to easily replicate and expand their work
- Adds two new chapters on agent-based modeling and big data analytics
- Provides support materials for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual
This set is intended for upper-level undergraduate and graduate students taking courses in quantitative geography, spatial analysis, GIS applications in socioeconomic studies, GIS applications in business, location theory. Researchers in similar fields: geography, city and regional planning, sociology, criminology, public health, and public administration.
Fahui Wang is Associate Dean of the Pinkie Gordon Lane Graduate School and Cyril & Tutta Vetter Alumni Professor in the Department of Geography and Anthropology, Louisiana State University. He earned a BS degree in geography from Peking University, China, and an MA degree in economics and a PhD in city and regional planning from the Ohio State University. His research has revolved around the broad theme of spatially integrated computational social sciences, public policy and planning in Geographic Information Systems. He is among the top 1% most-cited researchers in geography in the world.
Lingbo Liu is a postdoctoral fellow at the Center for Geographic Analysis, Harvard University, leading the development of Geospatial Analytics Extension for KNIME. He was a lecturer at the Department of Urban Planning, School of Urban Design, Wuhan University from 2005 to 2022, and obtained his PhD in Digital Urban Administration and Planning from Wuhan University in 2018. His research uses multi-source data and quantitative models to capture the spatiotemporal features of urban systems, and provides decision support for public policy, sustainable urban planning, and design.
