Spatial Analysis with R Statistics, Visualization, and Computational Methods, Second Edition PDF by Tonny J. Oyana

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Spatial Analysis with R Statistics, Visualization, and Computational Methods, Second Edition
By Tonny J. Oyana
Spatial Analysis with R_ Statistics, Visualization, and Computational Methods


Contents

Preface ……………………………………………………………………………………………………. xiii
Acknowledgments ………………………………………………………………………………… xvii
Author ……………………………………………………………………………………………………. xix
1. Understanding the Context and Relevance of Spatial Analysis ………..1
Learning Objectives ……………………………………………………………………………..1
Introduction …………………………………………………………………………………………1
From Data to Information, to Knowledge, and Wisdom ………………………3
Spatial Analysis Using a GIS Timeline …………………………………………………5
Spatial Analysis in the Post-1990s Period ……………………………………………..8
Data Science, GIS, and Artificial Intelligence …………………………………….. 10
Geographic Data: Properties, Strengths, and Analytical Challenges …. 12
Concept of Scale ……………………………………………………………………………. 14
Concept of Spatial Dependency …………………………………………………….. 15
Concept of Spatial Proximity ………………………………………………………… 15
Modifiable Areal Unit Problem ………………………………………………………….. 17
Concept of Spatial Autocorrelation ……………………………………………………. 21
Conclusion ………………………………………………………………………………………… 24
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments …………………………………………………………………………………….. 24
Working with R …………………………………………………………………………………. 24
Getting Started ………………………………………………………………………………….. 24
Working with Spatial Data …………………………………………………………………25
Tips for Working with R …………………………………………………………………….26
Stay One Step Ahead with Challenge Assignments …………………………..27
Review and Study Questions ……………………………………………………………..29
Glossary of Key Terms ……………………………………………………………………….30
References …………………………………………………………………………………………. 31
2. Making Scientific Observations and Measurements in Spatial
Analysis …………………………………………………………………………………………….35
Learning Objectives ……………………………………………………………………………35
Introduction ……………………………………………………………………………………….35
Scales of Measurement ……………………………………………………………………….36
Nominal Scale ………………………………………………………………………………..36
Ordinal Scale …………………………………………………………………………………36
Interval Scale………………………………………………………………………………….38
Ratio Scale …………………………………………………………………………………….. 39
Two Main Approaches for Data Collection That Involve
Deductive and Inductive Reasoning ………………………………………….40
Population and Sample ………………………………………………………………………43
Spatial Sampling ……………………………………………………………………………44
Conclusion …………………………………………………………………………………………58
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ………………………………………………………………………………………58
Step I. View Data Structure ……………………………………………………………58
Step II. Basic Data Summaries ………………………………………………………..59
Step III. Exploring the Spatial Data ……………………………………………….. 59
Stay One Step Ahead with Challenge Assignments …………………………..60
Review and Study Questions …………………………………………………………….. 62
Glossary of Key Terms ………………………………………………………………………. 62
References ………………………………………………………………………………………….63
3. Using Statistical Measures to Analyze Data Distributions ……………..65
Learning Objectives ……………………………………………………………………………65
Introduction ……………………………………………………………………………………….65
Descriptive Statistics ………………………………………………………………………….66
Measures of Central Tendency ………………………………………………………. 67
Deriving a Weighted Mean Using the Frequency
Distributions in a Set of Observations ……………………………………….68
Measures of Dispersion …………………………………………………………………. 69
Spatial Statistics: Measures for Describing Basic Characteristics
of Spatial Data ……………………………………………………………………………………72
Spatial Measures of Central Tendency …………………………………………… 76
Spatial Measures of Dispersion ……………………………………………………… 78
Random Variables and Probability Distribution ………………………………… 81
Random Variable ……………………………………………………………………………82
Probability and Theoretical Data Distributions ……………………………..82
Concepts and Applications ………………………………………………………..82
Binomial Distribution ………………………………………………………………..84
Poisson Distribution ………………………………………………………………….85
Normal Distribution …………………………………………………………………. 87
Conclusion …………………………………………………………………………………………95
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ………………………………………………………………………………………95
Exploring Z-Score to Assess the Relative Position in Data
Distributions Using R …………………………………………………………………….95
Stay One Step Ahead with Challenge Assignments …………………………..97
Review and Study Questions …………………………………………………………… 102
Glossary of Key Terms …………………………………………………………………….. 103
References ……………………………………………………………………………………….. 103
4. Engaging in Exploratory Data Analysis, Visualization, and
Hypothesis Testing …………………………………………………………………………. 105
Learning Objectives …………………………………………………………………………. 105
Introduction …………………………………………………………………………………….. 105
Exploratory Data Analysis, Geovisualization, and Data
Visualization Methods …………………………………………………………………….. 106
Data Visualization …………………………………………………………………………… 107
Geographic Visualization ………………………………………………………………… 107
New Stunning Visualization Tools and Infographics ………………………. 109
Exploratory Approaches for Visualizing Spatial Datasets ……………….. 109
Visualizing Multidimensional Datasets: An Illustration Based on
U.S. Educational Achievements Rates, 1970–2012 …………………………….. 120
Hypothesis Testing, Confidence Intervals, and p-Values………………….. 126
Computation ……………………………………………………………………………………. 129
Statistical Conclusion ………………………………………………………………………. 129
Conclusion ………………………………………………………………………………………. 131
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ……………………………………………………………………………………. 132
Generating Graphical Data Summaries ……………………………………….. 132
Stay One Step Ahead with Challenge Assignments ………………………… 134
Review and Study Questions …………………………………………………………… 139
Glossary of Key Terms …………………………………………………………………….. 139
References ……………………………………………………………………………………….. 140
5. Understanding Spatial Statistical Relationships …………………………… 143
Learning Objectives …………………………………………………………………………. 143
Engaging in Correlation Analysis ……………………………………………………. 143
Ordinary Least Squares and Geographically Weighted Regression
Methods …………………………………………………………………………………………… 148
Procedures in Developing a Spatial Regression Model ……………………. 151
Examining Relationships between Regression Variables …………………. 153
Examining the Strength of Association and Direction of All
Paired Variables Using a Scatterplot Matrix …………………………………….. 153
Fitting the Ordinary Least Squares Regression Model ………………… 153
Primary Model………………………………………………………………………… 153
Examining Variance Inflation Factor Results ………………………………. 155
Reduced Model ……………………………………………………………………….. 156
Best Model ………………………………………………………………………………. 158
Examining Residual Changes in Ordinary Least Squares
Regression Models ………………………………………………………………………. 158
Fitting the Geographically Weighted Regression Model ……………… 161
Examining Residual Change and Effects of Predictor Variables
on Local Areas …………………………………………………………………………….. 161
Summary of Modeling Result ………………………………………………………….. 163
Conclusion ………………………………………………………………………………………. 164
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ……………………………………………………………………………………. 165
Stay One Step Ahead with Challenge Assignments ………………………… 166
Review and Study Questions …………………………………………………………… 173
Glossary of Key Terms …………………………………………………………………….. 173
References ……………………………………………………………………………………….. 175
6. Engaging in Point Pattern Analysis ……………………………………………….. 177
Learning Objectives …………………………………………………………………………. 177
Introduction …………………………………………………………………………………….. 177
Rationale for Studying Point Patterns and Distributions …………………. 179
Exploring Patterns, Distributions, and Trends Associated with
Point Features ………………………………………………………………………………….. 179
Quadrat Count…………………………………………………………………………………. 180
Nearest Neighbor Approach ……………………………………………………………. 185
K-Function Approach ………………………………………………………………………. 188
Kernel Estimation Approach ……………………………………………………………. 193
Constructing a Voronoi Map from Point Features ……………………………. 195
Exploring Space-Time Patterns ………………………………………………………… 197
Conclusions ………………………………………………………………………………………200
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ……………………………………………………………………………………. 201
Explore Potential Path Area and Activity Space Concepts …………… 201
Stay One Step Ahead with Challenge Assignments ………………………… 210
Review and Study Questions …………………………………………………………… 214
Glossary of Key Terms …………………………………………………………………….. 214
References ……………………………………………………………………………………….. 215
7. Engaging in Areal Pattern Analysis Using Global and Local
Statistics ………………………………………………………………………………………….. 217
Learning Objectives …………………………………………………………………………. 217
Rationale for Studying Areal Patterns ……………………………………………… 217
The Notion of Spatial Relationships…………………………………………………. 218
Quantifying Spatial Autocorrelation Effects in Areal Patterns ………… 219
Join Count Statistics …………………………………………………………………………. 221
Interpreting the Join Count Statistics and Methodological Flaws …225
Global Moran’s I Coefficient of Spatial Autocorrelation ……………………226
Interpreting Moran’s I and Methodological Flaws ……………………….229
Global Geary’s C Coefficient of Spatial Autocorrelation ……………………229
Interpreting Geary’s C and Methodological Flaws ………………………. 231
Getis-Ord G Statistics ………………………………………………………………………. 231
Interpretation of Getis-Ord G and Methodological Flaws…………….234
Local Moran’s I …………………………………………………………………………………234
Local G-Statistic ……………………………………………………………………………….238
Local Geary ……………………………………………………………………………………… 241
Using Scatterplots to Synthesize and Interpret Local Indicators of
Spatial Association Statistics ……………………………………………………………. 244
Conclusions ……………………………………………………………………………………… 247
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ……………………………………………………………………………………. 249
Quiz ………………………………………………………………………………………………… 251
Review and Study Questions …………………………………………………………… 252
Glossary of Key Terms ……………………………………………………………………..253
References ………………………………………………………………………………………..254
8. Engaging in Geostatistical Analysis ……………………………………………… 257
Learning Objectives …………………………………………………………………………. 257
Introduction …………………………………………………………………………………….. 257
Rationale for Using Geostatistics to Study Complex
Spatial Patterns …………………………………………………………………………………258
Basic Interpolation Equations …………………………………………………………..260
Spatial Structure Functions for Regionalized Variables …………………… 261
Kriging Method and Its Theoretical Framework ………………………………264
Simple Kriging …………………………………………………………………………………265
Ordinary Kriging ……………………………………………………………………………..265
Universal Kriging ……………………………………………………………………………. 270
Indicator Kriging …………………………………………………………………………….. 270
Key Points to Note about the Geostatistical Estimation Using
Kriging ………………………………………………………………………………………… 271
Exploratory Data Analysis …………………………………………………………… 272
Spatial Prediction and Modeling …………………………………………………. 273
Uncertainty Analysis …………………………………………………………………… 276
Conditional Geostatistical Simulation ………………………………………………280
Inverse Distance Weighting …………………………………………………………….. 281
Conclusions ……………………………………………………………………………………… 282
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments …………………………………………………………………………………….284
Review and Study Questions …………………………………………………………… 292
Glossary of Key Terms …………………………………………………………………….. 293
References ……………………………………………………………………………………….. 294
9. Data Science: Understanding Computing Systems and
Analytics for Big Data ……………………………………………………………………. 297
Learning Objectives …………………………………………………………………………. 297
Introduction to Data Science ……………………………………………………………. 297
Rationale for a Big Geospatial Data Framework ………………………………. 298
Data Management …………………………………………………………………………….300
Data Warehousing ………………………………………………………………………. 301
Data Sources, Processing Tools, and the Extract-Transform-
Load Process ………………………………………………………………………………..302
Data Integration and Storage ……………………………………………………303
Data-Mining Algorithms for Big Geospatial Data ……………………303
Tools, Algorithms, and Methods for Data Mining and
Actionable Knowledge …………………………………………………………….304
Business Intelligence, Spatial Online Analytical Processing,
and Analytics …………………………………………………………………………..305
Analytics and Strategies for Big Geospatial Data …………………………….. 310
Spatiotemporal Data Analytics ………………………………………………………… 312
Classification Algorithms for Detecting Clusters in Big
Geospatial Data ……………………………………………………………………………….. 313
Embedding Solutions/Algorithm with Topological Considerations … 315
Graph and Text Analytics ………………………………………………………………… 315
Conclusions ……………………………………………………………………………………… 317
Worked Examples in R and Stay One Step Ahead with Challenge
Assignments ……………………………………………………………………………………. 317
Review and Study Questions …………………………………………………………… 321
Glossary of Key Terms …………………………………………………………………….. 321
References ……………………………………………………………………………………….. 322
Index ………………………………………………………………………………………………………325


Preface
This book fosters a problem-based learning collaborative approach among students and stresses the mastering of spatial analysis knowledge and skills through a combined use of fundamental theories, concepts, and the practical application of geospatial data tools, techniques, and strategies in environmental studies. Since the publication of the first edition, many new developments have taken shape, such as the rigorous implementation of new tools and methods for spatial analysis using R, growth and expansion of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and increased interdisciplinary use of spatial analysis. Other developments are in citizen science and the development of new analytical strategies, concepts and algorithms, methods, and cloud-based platforms and tools to serve numerous mobile applications with spatial perspectives.

Currently, we see a deepening of understanding around big data, locational analytics, and its increased use across a variety of contexts. Spatial data now drives the world of goods and services, human and animal movements, unmanned systems (ground and aerial vehicles, drones, aircraft, and spacecraft), health, real estate, business, indoor and outdoor emergency response and planning, and many other applications.

A new wave of increased and renewed interest for simpler ways to use, visualize, describe, and present spatial data, find relationships in spatial data, and discover patterns in spatial data is further inspiring work in spatial analysis. We can accomplish these spatial tasks easily by ensuring that we have the most accurate, robust, and unbiased data to work with. Spatial techniques therefore assure us with the production of actionable spatial data, information, and knowledge to support decisions.

The overarching objectives of this book are (1) to offer readers a theoretical/methodological foundation in spatial analysis using traditional, contemporary, and emerging computational approaches; and (2) to encourage readers to apply the critical knowledge and skills to appropriately analyze and interpret geographic data. To achieve these objectives, the second edition draws from traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms, with the primary goal of supporting the growing field of geographic information science and training the next generation of geospatial analysts and data scientists. Spatial analytical concepts are introduced together with a series of active learning activities to enable readers to better understand, analyze, and synthesize spatial patterns, distributions, and relationships.

 

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