Spatial Analysis with R : Statistics, Visualization, and Computational Methods

MATLAB ® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book's use or discussion of MATLAB ® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. 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 In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods , many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition , provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked out examples using R Presents a wide range of hands-on spatial analysis work tables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first year graduate students in geography and the geosciences. This second edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. It includes the implementation of new tools for spatial analysis using R.