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

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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