GIS and Basic Spatial Analysis Tasks
Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools p. 1
Spatial and Attribute Data Management in ArcGIS p. 1
Map Projections and Spatial Data Models p. 2
Attribute Data Management and Attribute Join p. 3
Case Study 1A: Mapping the Population Density Pattern in Cuyahoga County, Ohio p. 4
Spatial Analysis Tools in ArcGIS: Queries, Spatial Joins, and Map Overlays p. 8
Case Study 1B: Extracting Census Tracts in the City of Cleveland and Analyzing Polygon Adjacency p. 12
Part 1: Extracting Census Tracts in Cleveland p. 12
Part 2: Identifying Contiguous Polygons p. 14
Summary p. 15
Importing and Exporting ASCII Files in ArcGIS p. 17
Notes p. 18
Measuring Distances and Time p. 19
Measures of Distance p. 19
Computing Network Distance and Time p. 21
Label-Setting Algorithm for the Shortest-Route Problem p. 21
Measuring Network Distance or Time in ArcGIS p. 23
Case Study 2: Measuring Distance between Counties and Major Cities in Northeast China p. 24
Part 1: Measuring Euclidean and Manhattan Distances p. 24
Part 2: Measuring Travel Distances p. 26
Part 3: Measuring Travel Time (Optional) p. 31
Summary p. 31
The Valued-Graph Approach to the Shortest-Route Problem p. 31
Notes p. 33
Spatial Smoothing and Spatial Interpolation p. 35
Spatial Smoothing p. 35
Floating Catchment Area Method p. 36
Kernel Estimation p. 37
Case Study 3A: Analyzing Tai Place-Names in Southern China by Spatial Smoothing p. 38
Part 1: Spatial Smoothing by the Floating Catchment Area Method p. 38
Part 2: Spatial Smoothing by Kernel Estimation p. 41
Point-Based Spatial Interpolation p. 42
Global Interpolation Methods p. 42
Local Interpolation Methods p. 43
Case Study 3B: Surface Modeling and Mapping of Tai Place-Names in Southern China p. 45
Part 1: Surface Mapping by Trend Surface Analysis p. 45
Part 2: Mapping by Local Interpolation Methods p. 46
Area-Based Spatial Interpolation p. 47
Case Study 3C: Aggregating Data from Census Tracts to Neighborhoods and School Districts in Cleveland, Ohio p. 48
Part 1: Simple Aggregation from Census Tracts to Neighborhoods in the City of Cleveland p. 49
Part 2: Areal Weighting Aggregation from Census Tracts to School Districts in Cuyahoga County p. 49
Summary p. 51
Empirical Bayes (EB) Estimation for Spatial Smoothing p. 52
Notes p. 53
Basic Quantitative Methods and Applications
GIS-Based Trade Area Analysis and Applications in Business Geography and Regional Planning p. 55
Basic Methods for Trade Area Analysis p. 56
Analog Method and Regression Model p. 56
Proximal Area Method p. 56
Gravity Models for Delineating Trade Areas p. 57
Reilly's Law p. 57
Huff Model p. 59
Link between Reilly's Law and Huff Model p. 60
Extensions to the Huff Model p. 61
Deriving the [beta] Value in the Gravity Models p. 62
Case Study 4A: Defining Fan Bases of Chicago Cubs and White Sox p. 63
Part 1: Defining Fan Base Areas by the Proximal Area Method p. 65
Part 2: Defining Fan Base Areas and Mapping Probability Surface by the Huff Model p. 66
Discussion p. 68
Case Study 4B: Defining Hinterlands of Major Cities in Northeast China p. 68
Part 1: Defining Proximal Areas by Railroad Distances p. 69
Part 2: Defining Hinterlands by the Huff Model p. 69
Discussion p. 71
Concluding Remarks p. 71
Economic Foundation of the Gravity Model p. 73
Notes p. 75
GIS-Based Measures of Spatial Accessibility and Application in Examining Health Care Access p. 77
Issues on Accessibility p. 77
The Floating Catchment Area Methods p. 79
Earlier Versions of Floating Catchment Area Method p. 79
Two-Step Floating Catchment Area (2SFCA) Method p. 80
The Gravity-Based Method p. 82
Gravity-Based Accessibility Index p. 82
Comparison of the 2SFCA and Gravity-Based Methods p. 83
Case Study 5: Measuring Spatial Accessibility to Primary Care Physicians in the Chicago Region p. 84
Part 1: Implementing the 2SFCA Method p. 85
Part 2: Implementing the Gravity-Based Model p. 89
Discussion and Remarks p. 91
A Property for Accessibility Measures p. 95
Notes p. 96
Function Fittings by Regressions and Application in Analyzing Urban and Regional Density Patterns p. 97
The Density Function Approach to Urban and Regional Structures p. 97
Studies on Urban Density Functions p. 97
Studies on Regional Density Functions p. 99
Function Fittings for Monocentric Models p. 101
Four Simple Bivariate Functions p. 101
Other Monocentric Functions p. 102
GIS and Regression Implementations p. 102
Nonlinear and Weighted Regressions in Function Fittings p. 105
Function Fittings for Polycentric Models p. 107
Polycentric Assumptions and Corresponding Functions p. 107
GIS and Regression Implementations p. 110
Case Study 6: Analyzing Urban Density Patterns in the Chicago Region p. 110
Part 1: Function Fittings for Monocentric Models (Census Tracts) p. 111
Part 2: Function Fittings for Polycentric Models (Census Tracts) p. 115
Part 3: Function Fittings for Monocentric Models (Townships) p. 116
Discussion and Summary p. 117
Deriving Urban Density Functions p. 120
Mills-Muth Economic Model p. 120
Gravity-Based Model p. 121
OLS Regression for a Linear Bivariate Model p. 121
Sample SAS Program for Monocentric Function Fittings p. 123
Notes p. 124
Principal Components, Factor, and Cluster Analyses, and Application in Social Area Analysis p. 127
Principal Components and Factor Analysis p. 127
Principal Components Factor Model p. 128
Factor Loadings, Factor Scores, and Eigenvalues p. 129
Rotation p. 130
Cluster Analysis p. 131
Social Area Analysis p. 134
Case Study 7: Social Area Analysis in Beijing p. 135
Discussion and Summary p. 143
Discriminant Function Analysis p. 145
Sample SAS Program for Factor and Cluster Analyses p. 146
Notes p. 147
Advanced Quantitative Methods and Applications
Geographic Approaches to Analysis of Rare Events in Small Population and Application in Examining Homicide Patterns p. 149
The Issue of Analyzing Rare Events in a Small Population p. 149
The ISD and the Spatial-Order Methods p. 150
The Scale-Space Clustering Method p. 152
Case Study 8: Examining the Relationship between Job Access and Homicide Patterns in Chicago at Multiple Geographic Levels Based on the Scale-Space Melting Method p. 155
Summary p. 163
The Poisson-Based Regression Analysis p. 164
Notes p. 165
Spatial Cluster Analysis, Spatial Regression, and Applications in Toponymical, Cancer, and Homicide Studies p. 167
Point-Based Spatial Cluster Analysis p. 168
Point-Based Tests for Global Clustering p. 168
Point-Based Tests for Local Clusters p. 168
Case Study 9A: Spatial Cluster Analysis of Tai Place-Names in Southern China p. 170
Area-Based Spatial Cluster Analysis p. 172
Defining Spatial Weights p. 172
Area-Based Tests for Global Clustering p. 172
Area-Based Tests for Local Clusters p. 173
Case Study 9B: Spatial Cluster Analysis of Cancer Patterns in Illinois p. 175
Spatial Regression p. 181
Case Study 9C: Spatial Regression Analysis of Homicide Patterns in Chicago p. 182
Part 1: Spatial Regression Analysis at the Census Tract Level by GeoDa p. 183
Part 2: Spatial Regression Analysis at the Community Area Level by GeoDa p. 185
Discussion p. 185
Summary p. 187
Spatial Filtering Methods for Regression Analysis p. 187
Notes p. 188
Linear Programming and Applications in Examining Wasteful Commuting and Allocating Health Care Providers p. 189
Linear Programming (LP) and the Simplex Algorithm p. 190
The LP Standard Form p. 190
The Simplex Algorithm p. 190
Case Study 10A: Measuring Wasteful Commuting in Columbus, Ohio p. 193
The Issue of Wasteful Commuting and Model Formulation p. 193
Data Preparation in ArcGIS p. 194
Measuring Wasteful Commuting in SAS p. 197
Integer Programming and Location-Allocation Problems p. 199
General Forms and Solutions p. 199
Location-Allocation Problems p. 200
Case Study 10B: Allocating Health Care Providers in Cuyahoga County, Ohio p. 203
Part 1: Polygon-Based Analysis p. 203
Part 2: Network-Based Analysis p. 207
Discussion and Summary p. 212
Hamilton's Model on Wasteful Commuting p. 213
SAS Program for the LP Problem of Measuring Wasteful Commuting p. 214
Notes p. 217
Solving a System of Linear Equations and Application in Simulating Urban Structure p. 219
Solving a System of Linear Equations p. 219
The Garin-Lowry Model p. 221
Basic vs. Nonbasic Economic Activities p. 221
The Model's Formulation p. 222
An Illustrative Example p. 224
Case Study 11: Simulating Population and Service Employment Distributions in a Hypothetical City p. 225
Task 1: Computing Network Distances (Times) in ArcGIS p. 226
Task 2: Simulating Distributions of Population and Service Employment in the Basic Case p. 227
Task 3: Examining the Impact of Basic Employment Pattern p. 229
Task 4: Examining the Impact of Travel Friction Coefficient p. 229
Task 5: Examining the Impact of the Transportation Network p. 230
Discussion and Summary p. 230
The Input-Output Model p. 231
Solving a System of Nonlinear Equations p. 232
FORTRAN Program for Solving the Garin-Lowry Model p. 234
References p. 243
Index p. 253
Related Titles p. 265