Computational Methods and GIS Applications in Social Science

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Half Title
Title
Copyright
Contents
Foreword
Preface
Authors
Major GIS Datasets and Program Files
Quick References for Spatial Analysis Tasks
Part I GIS and Basic Spatial Analysis Tasks
Chapter 1 Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools
1.1 Spatial and Attribute Data Management in ArcGIS
1.1.1 Map Projections and Spatial Data Models
1.1.2 Attribute Data Management and Attribute Join
1.2 Spatial Analysis Tools in ArcGIS: Queries, Spatial Joins, and Map Overlays
1.3 Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana
1.3.1 Mapping the Population Density Pattern
1.3.2 Analyzing the Population Density Pattern across Concentric Rings
1.4 Summary
Appendix 1: Identifying Contiguous Polygons by Spatial Analysis Tools
Chapter 2 Measuring Distance and Travel Time and Analyzing Distance Decay Behavior
2.1 Measures of Distance and Travel Time
2.2 Case Study 2A: Estimating Drive and Transit Times in Baton Rouge, Louisiana
2.2.1 Estimating a Euclidean or Geodesic Distance Matrix
2.2.2 Estimating a Drive Time Matrix
2.2.3 Estimating a Transit Time Matrix
2.3 Estimating Distance Decay Functions
2.4 Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida
2.4.1 Estimating Distance Decay Functions by the Spatial Interaction Model
2.4.2 Estimating Distance Decay Functions by the Complementary Cumulative Distribution Method
2.5 Summary
Appendix 2A: The Valued Graph Approach to the Shortest-Route Problem
Appendix 2B: Estimating Drive Time and Transit Time Matrices by Google Maps API
Appendix 2C: Installing Geoprocessing Tools Based on R
Chapter 3 Spatial Smoothing and Spatial Interpolation
3.1 Spatial Smoothing
3.1.1 Floating Catchment Area (FCA) Method
3.1.2 Kernel Density Estimation (KDE) Method
3.2 Point-Based Spatial Interpolation
3.2.1 Global Interpolation Methods
3.2.2 Local Interpolation Methods
3.3 Case Study 3A: Mapping Place-Names in Guangxi, China
3.3.1 Implementing the Floating Catchment Area Method
3.3.2 Implementing Spatial Interpolation Methods
3.4 Area-Based Spatial Interpolation
3.5 Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana
3.5.1 Implementing Areal Weighting Interpolation
3.5.2 Implementing Target-Density Weighting Interpolation
3.6 Spatiotemporal Kernel Density Estimation (STKDE) Method
3.7 Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana
3.8 Summary
Appendix 3: Empirical Bayes Estimation for Spatial Smoothing
Part II Basic Computational Methods and Applications
Chapter 4 Delineating Functional Regions and Application in Health Geography
4.1 Basic Geographic Methods for Defining Functional Regions
4.1.1 Proximal Area Method
4.1.2 Huff Model
4.2 Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana
4.2.1 Defining HSAs by the Proximal Area Method
4.2.2 Defining HSAs by the Huff Model
4.3 Refining the Dartmouth Method
4.4 Spatialized Network Community Detection Methods
4.5 Case Study 4B: Automated Delineation of Hospital Service Areas (HSAs) in Florida
4.5.1 Delineating HSAs by the Refined Dartmouth Method
4.5.2 Delineating HSAs by Integrating the Huff Model and Dartmouth Method
4.5.3 Delineating HSAs by Spatialized Network Community Detection Methods
4.6 Summary
Appendix 4A: Economic Foundation of the Gravity Model
Appendix 4B: Installing Geoprocessing Tools Based on Python
Chapter 5 GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity
5.1 Issues on Accessibility
5.2 Floating Catchment Area Methods
5.2.1 Earlier Versions of Floating Catchment Area (FCA) Method
5.2.2 Two-Step Floating Catchment Area (2SFCA) Method
5.3 From Gravity-Based to Generalized 2SFCA Method
5.3.1 Gravity-Based 2SFCA Method
5.3.2 Generalized 2SFCA Method
5.4 Inverted 2SFCA Method
5.5 Two-Step Virtual Catchment Area (2SVCA) Method
5.6 Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge
5.6.1 Implementing the 2SFCA Method
5.6.2 Implementing the Gravity-Based 2SFCA Method
5.6.3 Implementing the 2SVCA Method
5.7 Summary
Appendix 5A: A Property of Accessibility Measures
Appendix 5B: A Toolkit for Automated Spatial Accessibility and Virtual Accessibility Measures.
Appendix 5C: Deriving the 2SFCA and I2SFCA Methods
Chapter 6 Function Fittings by Regressions and Application in Analyzing Urban Density Patterns
6.1 The Density Function Approach to Urban and Regional Structures
6.1.1 Urban Density Functions
6.1.2 Regional Density Functions
6.2 Function Fittings for Monocentric Models
6.3 Nonlinear and Weighted Regressions in Function Fittings
6.4 Function Fittings for Polycentric Models
6.5 Case Study 6: Analyzing Urban Density Patterns in Chicago Urban Area
6.5.1 Function Fittings for Monocentric Models at the Census Tract Level
6.5.2 Function Fittings for Polycentric Models at the Census Tract Level
6.5.3 Function Fittings for Monocentric Models at the Township Level
6.6 Discussion and Summary
Appendix 6A: Deriving Urban Density Functions
Appendix 6B: Centrality Measures and Association with Urban Densities
Appendix 6C: OLS Regression for a Linear Bivariate Model
Chapter 7 Principal Components, Factor Analysis, and Cluster Analysis and Application in Social Area Analysis
7.1 Principal Components Analysis (PCA)
7.2 Factor Analysis (FA)
7.3 Cluster Analysis (CA)
7.4 Social Area Analysis
7.5 Case Study 7: Social Area Analysis in Beijing
7.6 Summary
Appendix 7: Discriminant Function Analysis
Chapter 8 Spatial Statistics and Applications
8.1 The Centrographic Measures
8.2 Inferential Measures of Point Distribution Patterns
8.2.1 Spatial Clustering Pattern of One Type of Points
8.2.2 Cluster Analysis of Case-Control Points
8.3 Case Study 8A: Spatial Distribution and Clusters of Place-Names in Yunnan, China
8.3.1 Analysis of Multiethnic Place-Names by Centrographic Measures
8.3.2 Cluster Analysis of Multiethnic Place-Names by SaTScanR
8.4 Colocation Analysis of Two Types of Points
8.4.1 Spatial Weights Definition and Cross K-Function Analysis
8.4.2 Global Colocation Quotient (CLQ)
8.4.3 Local Indicator of Colocation Quotient (LCLQ)
8.5 Case Study 8B: Detecting Colocation between Crime Incidents and Facilities
8.6 Spatial Cluster Analysis Based on Feature Values
8.6.1 Tests for Global Clustering Based on Feature Values
8.6.2 Tests for Local Clusters Based on Feature Values
8.7 Spatial Regression
8.7.1 Spatial Lag Model and Spatial Error Model
8.7.2 Geographically Weighted Regression
8.8 Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago
8.8.1 Spatial Cluster Analysis of Homicide Rates
8.8.2 Regression Analysis of Homicide Patterns
8.9 Summary
Chapter 9 Regionalization Methods and Application in Analysis of Cancer Data
9.1 The Small Population Problem and Regionalization
9.2 GIS-Automated Regionalization Methods
9.2.1 The Spatially Constrained Hierarchical Clustering (SCHC) Methods
9.2.2 The Spatially Constrained Clustering with Partitioning (SCCP) Methods
9.3 The Mixed-Level Regionalization (MLR) Method
9.3.1 Modified Peano Curve Algorithm (MPC)
9.3.2 Modified Scale–Space Clustering (MSSC)
9.3.3 Integrating MPC and MSSC
9.3.4 Mixed-Level Regionalization (MLR)
9.4 Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana
9.4.1 Implementing the SKATER, AZP, Max-P, SCHC and REDCAP Methods
9.4.2 Implementing the MLR Method
9.5 Summary
Appendix 9: The Poisson-Based Regression Analysis
Part III Advanced Computational Methods and Applications
Chapter 10 System of Linear Equations and Application of the Garin–Lowry Model in Simulating Urban Population and Employment Patterns
10.1 System of Linear Equations
10.2 The Garin–Lowry Model
10.2.1 Basic vs. Nonbasic Economic Activities
10.2.2 The Model’s Formulation
10.2.3 An Illustrative Example
10.3 Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City
10.4 Discussion and Summary
Appendix 10A: The Input–Output Model
Appendix 10B: Solving a System of Nonlinear Equations
Chapter 11 Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers
11.1 Linear Programming and the Simplex Algorithm
11.1.1 The LP Standard Form
11.1.2 The Simplex Algorithm
11.2 Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio
11.2.1 The Issue of Wasteful Commuting and Model Formulation
11.2.2 Data Preparation in ArcGIS
11.2.3 Measuring Wasteful Commuting with the WasteCommuteR Tool
11.3 Integer Programming and Location-Allocation Problems
11.3.1 General Forms and Solutions for Integer Programming
11.3.2 Location-Allocation Problems
11.4 Quadratic Programming and the Maximal Accessibility Equality Problem (MAEP)
11.5 Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China
11.5.1 Data Preparation
11.5.2 Location Optimization for Site Selection
11.5.3 Capacity Optimization for Hospitals at Selected Sites
11.6 Summary
Appendix 11: Hamilton’s Model on Wasteful Commuting
Chapter 12 Monte Carlo Method and Applications in Urban Population and Traffic Simulations
12.1 Monte Carlo Simulation Method
12.1.1 Introduction to Monte Carlo Simulation
12.1.2 Monte Carlo Applications in Spatial Analysis
12.2 Case Study 12A: Deriving Urban Population Density Functions in Uniform Area Unit in Chicago by Monte Carlo Simulation
12.3 Travel Demand Modeling
12.4 Case Study 12B: Monte Carlo–Based Traffic Simulation in Baton Rouge, Louisiana
12.5 Summary
Appendix 12: Improving Measurement of Wasteful Commuting by Monte Carlo Simulation.
Chapter 13 Agent-Based Model and Application in Crime Simulation
13.1 From Cellular Automata to Agent-Based Model
13.2 Simulating Crimes in the Agent-Based Model
13.2.1 Theoretical Foundation in Environmental Criminology
13.2.2 Components of ABM Framework for Crime Simulation
13.2.3 Integration of GIS Functionalities with Simulation
13.3 Implementing an ABM Crime Simulator with Improved Daily Routines
13.3.1 Resident Agent Movement and Risk Avoidance Behaviors
13.3.2 Offender Motivation, Awareness Space, and Risk Assessment
13.4 Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana
13.4.1 Simulating Crimes in the Base Scenario
13.4.2 Testing the Hypotheses on Offender Behaviors
13.4.3 Testing the Effects of Police Patrol Strategies
13.5 Summary
Chapter 14 Spatiotemporal Big Data Analytics and Applications in Urban Studies
14.1 Using Taxi Trajectory Big Data in Urban Studies
14.1.1 Why Taxi Trajectory Big Data?
14.1.2 Taxi Trajectory Data Format
14.1.3 Application-Oriented Taxi Trajectory Data Queries
14.2 Case Study 14A: Exploring Taxi Trajectory in ArcGIS
14.3 Processing Taxi Trajectory Big Data with XSTAR
14.3.1 Taxi Trajectory Data Index Structure in XSTAR
14.3.2 Data Processing Steps in XSTAR
14.3.3 Analyzing Taxi Trajectory Big Data with XSTAR
14.4 Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai
14.4.1 Taxi Trajectory Raw Data Processing in XSTAR
14.4.2 OD Analysis in XSTAR
14.4.3 Destination Analysis in XSTAR
14.5 Summary
References
Index