Computational Methods and GIS Applications in Social Science - Lab Manual

✍ Scribed by Lingbo Liu, Fahui Wang

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 284
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title
Copyright
Contents
List of Figures
List of Tables
Authors
1 Getting Started with KNIME and Its Geospatial Analytics Extension
1.1 KNIME Analytics Platform
1.2 KNIME Extension for Spatial Analysis
1.3 Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana
1.3.1 Creating Workflow, Reading and Visualizing Data
1.3.2 Extracting the Study Area
1.3.3 Examining Urban Population Density Pattern Based on Census Tract Data
1.3.4 Examining Urban Population Density Pattern Based on Census Block Data
1.4 Concluding Remarks
2 Measuring Distance and Time and Analyzing Distance Decay Behavior
2.1 Case Study 2A: Estimating Travel Times to Hospitals in Baton Rouge
2.1.1 Geocoding Hospitals from Street Addresses or Geographic Coordinates
2.1.2 Estimating Euclidean and Manhattan Distances Based on the OD Coordinates
2.1.3 Estimating and Comparing Distances by Different Sources
2.2 Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida
2.3 Concluding Remarks
3 Spatial Smoothing and Spatial Interpolation
3.1 Case Study 3A: Mapping Place Names in Guangxi, China
3.2 Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana
3.2.1 Implementing Areal Weighting Interpolation
3.2.2 Implementing Target-Density Weighting (TDW) Interpolation
3.3 Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana
3.3.1 Exploring Time Series Heatmap Based on Kepler.gl GeoView
3.3.2 Implementing Time Series Heatmap Based on Grid Aggregation
3.3.3 Implementing Spatiotemporal Kernel Density Estimation
3.4 Concluding Remarks
4 Delineating Functional Regions and Application in Health Geography
4.1 Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana
4.1.1 Defining HSAs by the Proximal Area Method
4.1.2 Defining HSAs by the Huff Model
4.2 Case Study 4B: Automated Delineation of Hospital Service Areas in Florida
4.2.1 Part 1: Delineating HSAs by the Refined Dartmouth Method
4.2.2 Part 2: Delineating HSAs by Spatialized Network Community Detection Methods
4.3 Concluding Remarks
5 GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity
5.1 Part 1: Measuring Accessibility of Primary Care Physicians in Baton Rouge
5.1.1 Implementing the 2SFCA Method
5.1.2 Implementing the Gravity-Based 2SFCA Method
5.2 Part 2: Implementing the 2SVCA Method
5.3 Part 3: Sensitive Analysis for Measuring Accessibility by Workflow Automation
5.3.1 Standard Component for 2SFCA Method
5.3.2 Sensitive Analysis with 2SFCA Component and Workflow Loop
5.4 Concluding Remarks
6 Function Fittings by Regressions and Application in Analyzing Urban Density Patterns
6.1 Part 1: Function Fittings for Monocentric Models at the Census Tract Level
6.2 Part 2: Function Fittings for Polycentric Models at the Census Tract Level
6.3 Part 3: Function Fittings for Monocentric Models at the Township Level
6.4 Concluding Remarks
7 Principal Components, Factor Analysis and Cluster Analysis and Application in Social Area Analysis
7.1 Part 1: Principal Components Analysis (PCA)
7.2 Part 2: Cluster Analysis (CA)
7.3 Part 3: Detecting Urban Structure Models by Regression
7.4 Concluding Remarks
8 Spatial Statistics and Applications
8.1 Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China
8.1.1 Part 1: Analysis of Multiethnic Place Names by Centrographic Measures
8.1.2 Part 2: Cluster Analysis of Multiethnic Place Names by SaTScanR
8.2 Case Study 8B: Detecting Colocation between Crime Incidents and Facilities
8.2.1 Part 1: Global Colocation Quotient (GCLQ)
8.2.2 Part 2: Local Colocation Quotient (LCLQ)
8.3 Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago
8.3.1 Part 1: Spatial Cluster Analysis of Homicide Rates
8.3.2 Part 2: Regression Analysis of Homicide Patterns
8.4 Concluding Remarks
9 Regionalization Methods and Application in Analysis of Cancer Data
9.1 Part 1: One-Level Regionalization
9.1.1 Implementing SKATER, AZP, MaxP, SCHC and REDCAP Methods
9.1.2 Implementing MSSC and MPC Modules in MLR
9.2 Part 2: Calibrating Clustering Indicators WTVR and Compactness
9.2.1 Calculating Within-Total Variance Ratio (WTVR)
9.2.2 Calculating Compactness Indices
9.3 Part 3: Mixed-Level Regionalization (MLR)
9.3.1 Implementing MLR on Disaggregation Parishes (Type I)
9.3.2 Implementing MLR on No-Action Parishes (Type II)
9.3.3 Implementing MLR on Aggregation Parishes (Type III)
9.3.4 Aggerating All Clusters as MLR
9.4 Concluding Remarks
10 System of Linear Equations and Application of Garin-Lowry Model in Simulating Urban Population and Employment Patterns
10.1 Implementing the Garin-Lowry Model in a Hypothetical City
10.2 Part 1: The Basic Scenario of a Monocentric City
10.3 Part 2: Exploring Other Scenarios
10.4 Concluding Remarks
11 Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers
11.1 Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio
11.2 Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China
11.2.1 Part 1: Data Preparation
11.2.2 Part 2: Location Optimization for Site Selection
11.2.3 Part 3: Implementing the MAEP to Derive Capacities for Sited New Hospitals
11.3 Concluding Remarks
12 Monte Carlo Method and Applications in Urban Population and Traffic Simulations
12.1 Case Study 12A: Deriving Urban Population Density Functions in Uniform Area Unit in Chicago by Monte Carlo Simulation
12.2 Case Study 12B: Monte Carlo Based Traffic Simulation in Baton Rouge, Louisiana
12.2.1 Part 1: Estimating Inter-Zonal O-D Trip Volumes
12.2.2 Part 2: Simulating Individual Trip Origins and Destinations
12.2.3 Part 3: Simulating Individual OD Pairs
12.2.4 Part 4: Trip Assignment and Model Validation
12.3 Concluding Remarks
13 Agent-Based Model and Application in Crime Simulation
13.1 Part 1: Simulating Crimes in the Base Scenario in ABM in Baton Rouge
13.2 Part 2: Evaluating ABM Crime Simulation Results
13.3 Part 3: Testing Various Hypotheses by ABM Crime Simulation
13.4 Concluding Remarks
14 Spatiotemporal Big Data Analytics and Applications in Urban Studies
14.1 Case Study 14A: Rebuilding Taxi Trajectory
14.2 Case Study 14B: Aggregating Taxi Trajectory between Grids
14.3 Concluding Remarks
References
Index