Spatial analysis with R: statistics, visualization, and computational methods

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Contents......Page 8
Preface......Page 14
Acknowledgments......Page 18
Author......Page 20
 Introduction......Page 22
 From Data to Information, to Knowledge, and Wisdom......Page 24
 Spatial Analysis Using a GIS Timeline......Page 26
 Spatial Analysis in the Post-1990s Period......Page 29
 Data Science, GIS, and Artificial Intelligence......Page 31
 Geographic Data: Properties, Strengths, and Analytical Challenges......Page 33
  Concept of Scale......Page 35
  Concept of Spatial Proximity......Page 36
 Modifiable Areal Unit Problem......Page 38
 Concept of Spatial Autocorrelation......Page 42
 Getting Started......Page 45
 Working with Spatial Data......Page 46
 Tips for Working with R......Page 47
 Stay One Step Ahead with Challenge Assignments......Page 48
 Review and Study Questions......Page 50
 Glossary of Key Terms......Page 51
 References......Page 52
 Introduction......Page 56
  Ordinal Scale......Page 57
  Interval Scale......Page 59
  Ratio Scale......Page 60
   Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning......Page 61
 Population and Sample......Page 64
  Spatial Sampling......Page 65
  Step I. View Data Structure......Page 79
  Step III. Exploring the Spatial Data......Page 80
 Stay One Step Ahead with Challenge Assignments......Page 81
 Glossary of Key Terms......Page 83
 References......Page 84
 Introduction......Page 86
 Descriptive Statistics......Page 87
  Measures of Central Tendency......Page 88
   Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations......Page 89
  Measures of Dispersion......Page 90
 Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data......Page 93
  Spatial Measures of Central Tendency......Page 97
  Spatial Measures of Dispersion......Page 99
 Random Variables and Probability Distribution......Page 102
   Concepts and Applications......Page 103
   Binomial Distribution......Page 105
   Poisson Distribution......Page 106
   Normal Distribution......Page 108
  Exploring Z-Score to Assess the Relative Position in Data Distributions Using R......Page 116
 Stay One Step Ahead with Challenge Assignments......Page 118
 Review and Study Questions......Page 123
 References......Page 124
 Introduction......Page 126
 Exploratory Data Analysis, Geovisualization, and Data Visualization Methods......Page 127
 Geographic Visualization......Page 128
 Exploratory Approaches for Visualizing Spatial Datasets......Page 130
 Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970–2012......Page 141
 Hypothesis Testing, Confidence Intervals, and .p.-Values......Page 147
 Statistical Conclusion......Page 150
 Conclusion......Page 152
  Generating Graphical Data Summaries......Page 153
 Stay One Step Ahead with Challenge Assignments......Page 155
 Glossary of Key Terms......Page 160
 References......Page 161
 Engaging in Correlation Analysis......Page 164
 Ordinary Least Squares and Geographically Weighted Regression Methods......Page 169
 Procedures in Developing a Spatial Regression Model......Page 172
   Primary Model......Page 174
  Examining Variance Inflation Factor Results......Page 176
   Reduced Model......Page 177
  Examining Residual Changes in Ordinary Least Squares Regression Models......Page 179
  Examining Residual Change and Effects of Predictor Variables on Local Areas......Page 182
 Summary of Modeling Result......Page 184
 Conclusion......Page 185
 Worked Examples in R and Stay One Step Ahead with Challenge Assignments......Page 186
 Stay One Step Ahead with Challenge Assignments......Page 187
 Glossary of Key Terms......Page 194
 References......Page 196
 Introduction......Page 198
 Exploring Patterns, Distributions, and Trends Associated with Point Features......Page 200
 Quadrat Count......Page 201
 Nearest Neighbor Approach......Page 206
 K-Function Approach......Page 209
 Kernel Estimation Approach......Page 214
 Constructing a Voronoi Map from Point Features......Page 216
 Exploring Space-Time Patterns......Page 218
 Conclusions......Page 221
  Explore Potential Path Area and Activity Space Concepts......Page 222
 Stay One Step Ahead with Challenge Assignments......Page 231
 Glossary of Key Terms......Page 235
 References......Page 236
 Rationale for Studying Areal Patterns......Page 238
 The Notion of Spatial Relationships......Page 239
 Quantifying Spatial Autocorrelation Effects in Areal Patterns......Page 240
 Join Count Statistics......Page 242
  Interpreting the Join Count Statistics and Methodological Flaws......Page 246
 Global Moran’s I Coefficient of Spatial Autocorrelation......Page 247
 Global Geary’s C Coefficient of Spatial Autocorrelation......Page 250
 Getis-Ord G Statistics......Page 252
 Local Moran’s I......Page 255
 Local G-Statistic......Page 259
 Local Geary......Page 262
 Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics......Page 265
 Conclusions......Page 268
 Worked Examples in R and Stay One Step Ahead with Challenge Assignments......Page 270
 Quiz......Page 272
 Review and Study Questions......Page 273
 Glossary of Key Terms......Page 274
 References......Page 275
 Introduction......Page 278
 Rationale for Using Geostatistics to Study Complex Spatial Patterns......Page 279
 Basic Interpolation Equations......Page 281
 Spatial Structure Functions for Regionalized Variables......Page 282
 Kriging Method and Its Theoretical Framework......Page 285
 Ordinary Kriging......Page 286
 Indicator Kriging......Page 291
  Key Points to Note about the Geostatistical Estimation Using Kriging......Page 292
  Exploratory Data Analysis......Page 293
  Spatial Prediction and Modeling......Page 294
  Uncertainty Analysis......Page 297
 Conditional Geostatistical Simulation......Page 301
 Inverse Distance Weighting......Page 302
 Conclusions......Page 303
 Worked Examples in R and Stay One Step Ahead with Challenge Assignments......Page 305
 Review and Study Questions......Page 313
 Glossary of Key Terms......Page 314
 References......Page 315
 Introduction to Data Science......Page 318
 Rationale for a Big Geospatial Data Framework......Page 319
 Data Management......Page 321
  Data Warehousing......Page 322
  Data Sources, Processing Tools, and the Extract-Transform-Load Process......Page 323
  Data-Mining Algorithms for Big Geospatial Data......Page 324
  Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge......Page 325
  Business Intelligence, Spatial Online Analytical Processing, and Analytics......Page 326
 Analytics and Strategies for Big Geospatial Data......Page 331
 Spatiotemporal Data Analytics......Page 333
 Classification Algorithms for Detecting Clusters in Big Geospatial Data......Page 334
 Graph and Text Analytics......Page 336
 Worked Examples in R and Stay One Step Ahead with Challenge Assignments......Page 338
 Glossary of Key Terms......Page 342
 References......Page 343
Index......Page 346