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

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