Computational Methods and GIS Applications in Social Science - Lab Manual

This lab manual is a companion to the third edition of the textbook Computational Methods and GIS Applications in Social Science. It uses the open-source platform KNIME to illustrate a step-by-step implementation of each case study in the book. KNIME is a workflow-based platform supporting visual programming and multiple scripting language such as R, Python, and Java. The intuitive, structural workflow not only helps students better understand the methodology of each case study in the book, but also enables them to easily replicate, transplant and expand the workflow for further exploration with new data or models. This lab manual could also be used as a GIS automation reference for advanced users in spatial analysis.

FEATURES

• The first hands-on, open-source KNIME lab manual written in tutorial style and focused on GIS applications in social science
• Includes 22 case studies from the United States and China that parallel the methods developed in the textbook
• Provides clear step-by-step explanations on how to use the open-source platform KNIME to understand basic and advanced analytical methods through real-life case studies
• Enables readers to easily replicate and expand their work with new data and models
• A valuable guide for students and practitioners worldwide engaged in efforts to develop GIS automation in spatial analysis

This lab manual 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, and location theory, as well as researchers in the similar fields of geography, city and regional planning, sociology, and public administration.

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 earned a PhD in digital urban administration and planning at 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.

Fahui Wang is Associate Dean of the Pinkie Gordon Lane Graduate School and Cyril and Tutta Vetter Alumni Professor in the Department of Geography and Anthropology, Louisiana State University. He earned a BS in geography at Peking University, China, and an MA in economics and a PhD in city and regional planning at 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.