1st Edition
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.
1. Getting Started with KNIME and Its Geospatial Analytics Extension
2. Measuring Distance and Time and Analyzing Distance Decay Behavior
3. Spatial Smoothing and Spatial Interpolation
4. Delineating Functional Regions and Application in Health Geography
5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity
6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns
7. Principal Components, Factor Analysis and Cluster Analysis and Application in Social Area Analysis
8. Spatial Statistics and Applications
9. Regionalization Methods and Application in Analysis of Cancer Data
10. System of Linear Equations and Application of Garin-Lowry Model in Simulating Urban Population and Employment Patterns
11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers
12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations
13. Agent-Based Model and Application in Crime Simulation
14. Spatiotemporal Big Data Analytics and Applications in Urban Studies
Biography
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.
