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

Description

https://www.routledge.com/p/book/9780367860851 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 worktables 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. Cover Half Title Title Page Copyright Page Dedication Contents Preface Acknowledgments Author 1. Understanding the Context and Relevance of Spatial Analysis Learning Objectives Introduction From Data to Information, to Knowledge, and Wisdom Spatial Analysis Using a GIS Timeline Spatial Analysis in the Post-1990s Period Data Science, GIS, and Artificial Intelligence Geographic Data: Properties, Strengths, and Analytical Challenges Concept of Scale Concept of Spatial Dependency Concept of Spatial Proximity Modifiable Areal Unit Problem Concept of Spatial Autocorrelation Conclusion Worked Examples in R and Stay One Step Ahead with Challenge Assignments Working with R Getting Started Working with Spatial Data Tips for Working with R Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 2. Making Scientific Observations and Measurements in Spatial Analysis Learning Objectives Introduction Scales of Measurement Nominal Scale Ordinal Scale Interval Scale Ratio Scale Two Main Approaches for Data Collection That Involve Deductive and Inductive Reasoning Population and Sample Spatial Sampling Conclusion Worked Examples in R and Stay One Step Ahead with Challenge Assignments Step I. View Data Structure Step II. Basic Data Summaries Step III. Exploring the Spatial Data Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 3. Using Statistical Measures to Analyze Data Distributions Learning Objectives Introduction Descriptive Statistics Measures of Central Tendency Deriving a Weighted Mean Using the Frequency Distributions in a Set of Observations Measures of Dispersion Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data Spatial Measures of Central Tendency Spatial Measures of Dispersion Random Variables and Probability Distribution Random Variable Probability and Theoretical Data Distributions Concepts and Applications Binomial Distribution Poisson Distribution Normal Distribution Conclusion Worked Examples in R and Stay One Step Ahead with Challenge Assignments Exploring Z-Score to Assess the Relative Position in Data Distributions Using R Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 4. Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing Learning Objectives Introduction Exploratory Data Analysis, Geovisualization, and Data Visualization Methods Data Visualization Geographic Visualization New Stunning Visualization Tools and Infographics Exploratory Approaches for Visualizing Spatial Datasets Visualizing Multidimensional Datasets: An Illustration Based on U.S. Educational Achievements Rates, 1970–2012 Hypothesis Testing, Confidence Intervals, and .p.-Values Computation Statistical Conclusion Conclusion Worked Examples in R and Stay One Step Ahead with Challenge Assignments Generating Graphical Data Summaries Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 5. Understanding Spatial Statistical Relationships Learning Objectives Engaging in Correlation Analysis Ordinary Least Squares and Geographically Weighted Regression Methods Procedures in Developing a Spatial Regression Model Examining Relationships between Regression Variables Examining the Strength of Association and Direction of All Paired Variables Using a Scatterplot Matrix Fitting the Ordinary Least Squares Regression Model Primary Model Examining Variance Inflation Factor Results Reduced Model Best Model Examining Residual Changes in Ordinary Least Squares Regression Models Fitting the Geographically Weighted Regression Model Examining Residual Change and Effects of Predictor Variables on Local Areas Summary of Modeling Result Conclusion Worked Examples in R and Stay One Step Ahead with Challenge Assignments Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 6. Engaging in Point Pattern Analysis Learning Objectives Introduction Rationale for Studying Point Patterns and Distributions Exploring Patterns, Distributions, and Trends Associated with Point Features Quadrat Count Nearest Neighbor Approach K-Function Approach Kernel Estimation Approach Constructing a Voronoi Map from Point Features Exploring Space-Time Patterns Conclusions Worked Examples in R and Stay One Step Ahead with Challenge Assignments Explore Potential Path Area and Activity Space Concepts Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 7. Engaging in Areal Pattern Analysis Using Global and Local Statistics Learning Objectives Rationale for Studying Areal Patterns The Notion of Spatial Relationships Quantifying Spatial Autocorrelation Effects in Areal Patterns Join Count Statistics Interpreting the Join Count Statistics and Methodological Flaws Global Moran’s I Coefficient of Spatial Autocorrelation Interpreting Moran’s I and Methodological Flaws Global Geary’s C Coefficient of Spatial Autocorrelation Interpreting Geary’s C and Methodological Flaws Getis-Ord G Statistics Interpretation of Getis-Ord G and Methodological Flaws Local Moran’s I Local G-Statistic Local Geary Using Scatterplots to Synthesize and Interpret Local Indicators of Spatial Association Statistics Conclusions Worked Examples in R and Stay One Step Ahead with Challenge Assignments Quiz Review and Study Questions Glossary of Key Terms References 8. Engaging in Geostatistical Analysis Learning Objectives Introduction Rationale for Using Geostatistics to Study Complex Spatial Patterns Basic Interpolation Equations Spatial Structure Functions for Regionalized Variables Kriging Method and Its Theoretical Framework Simple Kriging Ordinary Kriging Universal Kriging Indicator Kriging Key Points to Note about the Geostatistical Estimation Using Kriging Exploratory Data Analysis Spatial Prediction and Modeling Uncertainty Analysis Conditional Geostatistical Simulation Inverse Distance Weighting Conclusions Worked Examples in R and Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References 9. Data Science: Understanding Computing Systems and Analytics for Big Data Learning Objectives Introduction to Data Science Rationale for a Big Geospatial Data Framework Data Management Data Warehousing Data Sources, Processing Tools, and the Extract-Transform-Load Process Data Integration and Storage Data-Mining Algorithms for Big Geospatial Data Tools, Algorithms, and Methods for Data Mining and Actionable Knowledge Business Intelligence, Spatial Online Analytical Processing, and Analytics Analytics and Strategies for Big Geospatial Data Spatiotemporal Data Analytics Classification Algorithms for Detecting Clusters in Big Geospatial Data Embedding Solutions/Algorithm with Topological Considerations Graph and Text Analytics Conclusions Worked Examples in R and Stay One Step Ahead with Challenge Assignments Review and Study Questions Glossary of Key Terms References Index