• Front Matter
    • What is this book and who is it for?
    • Allied resources
    • Contributing
      • 0.0.1 License
  • Preface
    • Spatial Analysis and Spatial Statistics
    • Why this Text?
    • Plan
    • Audience
    • Requisites
    • Words of Appreciation
    • Versioning
  • PART I: Getting to know the technology
  • 1 Preliminaries: Installing R and RStudio
    • 1.1 Introduction
    • 1.2 Learning Objectives
    • 1.3 R: The Open Statistical Computing Project
      • 1.3.1 What is R?
      • 1.3.2 The RStudio IDE
    • 1.4 Packages in R
  • 2 Basic Operations and Data Structures in R
    • 2.1 Learning Objectives
    • 2.2 RStudio IDE
    • 2.3 Some Basic Operations
    • 2.4 Data Classes in R
    • 2.5 Data Types in R
    • 2.6 Indexing and Data Transformations
    • 2.7 Visualization
    • 2.8 Creating a Simple Map
    • 2.9 Examples of digital cartography in R
    • 2.10 References
  • Part II: Statistics and Maps
  • 3 Introduction to Mapping in R
    • 3.1 Learning Objectives
    • 3.2 Suggested Readings
    • 3.3 Preliminaries
    • 3.4 Packages
    • 3.5 Exploring Dataframes and a Simple Proportional Symbols Map
    • 3.6 Improving on the Proportional Symbols Map
    • 3.7 Some Simple Spatial Analysis
    • 3.8 Other Resources
  • 4 Activity: Statistical Maps I
    • 4.1 Housekeeping Questions
    • 4.2 Learning Objectives
    • 4.3 Preliminaries
    • 4.4 Creating a simple thematic map
    • 4.5 Activity
  • 5 Mapping in R: Continued
    • 5.1 Learning Objectives
    • 5.2 Suggested Readings
    • 5.3 Preliminaries
    • 5.4 Summarizing a Dataframe
    • 5.5 Factors
    • 5.6 Subsetting Data
    • 5.7 Pipe Operator
    • 5.8 More on Visualization
  • 6 Activity 2: Statistical Maps II
    • 6.1 Housekeeping Questions
    • 6.2 Learning objectives
    • 6.3 Suggested reading
    • 6.4 Preliminaries
    • 6.5 Activity
  • 7 Maps as Processes: Null Landscapes, Spatial Processes, and Statistical Maps
    • 7.1 Learning Objectives
    • 7.2 Suggested Readings
    • 7.3 Preliminaries
    • 7.4 Random Numbers
    • 7.5 Null Landscapes
    • 7.6 Stochastic Processes
    • 7.7 Simulating Spatial Processes
    • 7.8 Processes and Patterns
  • 8 Activity 3: Maps as Processes
    • 8.1 Practice Questions
    • 8.2 Learning Objectives
    • 8.3 Suggested Reading
    • 8.4 Preliminaries
    • 8.5 Activity
  • Part III: Analysis of Point Patterns
  • 9 Point Pattern Analysis I
    • 9.1 Learning Objectives
    • 9.2 Suggested Readings
    • 9.3 Preliminaries
    • 9.4 Point Patterns
    • 9.5 Processes and Point Patterns
    • 9.6 Intensity and Density
    • 9.7 Quadrats and Density Maps
    • 9.8 Defining the Region for Analysis
  • 10 Activity 4: Point Pattern Analysis I
    • 10.1 Practice questions
    • 10.2 Learning objectives
    • 10.3 Suggested reading
    • 10.4 Preliminaries
    • 10.5 Activity
  • 11 Point Pattern Analysis II
    • 11.1 Learning Objectives
    • 11.2 Suggested Readings
    • 11.3 Preliminaries
    • 11.4 A Quadrat-based Test for Spatial Independence
    • 11.5 Limitations of Quadrat Analysis: Size and Number of Quadrats
    • 11.6 Limitations of Quadrat Analysis: Relative Position of Events
    • 11.7 Kernel Density
  • 12 Activity 5: Point Pattern Analysis II
    • 12.1 Practice questions
    • 12.2 Learning objectives
    • 12.3 Suggested reading
    • 12.4 Preliminaries
    • 12.5 Activity
  • 13 Point Pattern Analysis III
    • 13.1 Learning Objectives
    • 13.2 Suggested Readings
    • 13.3 Preliminaries
    • 13.4 Motivation
    • 13.5 Nearest Neighbors
    • 13.6 \(G\)-function
  • 14 Activity 6: Point Pattern Analysis III
    • 14.1 Practice questions
    • 14.2 Learning objectives
    • 14.3 Suggested reading
    • 14.4 Preliminaries
    • 14.5 Activity
  • 15 Point Pattern Analysis IV
    • 15.1 Learning Objectives
    • 15.2 Suggested Readings
    • 15.3 Preliminaries
    • 15.4 Motivation
    • 15.5 F-function
    • 15.6 \(\hat{K}\)-function
  • 16 Activity 7: Point Pattern Analysis IV
    • 16.1 Practice questions
    • 16.2 Learning objectives
    • 16.3 Suggested reading
    • 16.4 Preliminaries
    • 16.5 Activity
  • 17 Point Pattern Analysis V
    • 17.1 Learning Objectives
    • 17.2 Suggested Readings
    • 17.3 Preliminaries
    • 17.4 Motivation: Hypothesis Testing
    • 17.5 Null Landscapes Revisited
    • 17.6 Simulation Envelopes
    • 17.7 Things to Keep in Mind!
      • 17.7.1 Definition of a Region
      • 17.7.2 Edge Effects
      • 17.7.3 Sampled Point Patterns
  • 18 Activity 8: Point Pattern Analysis V
    • 18.1 Practice questions
    • 18.2 Learning objectives
    • 18.3 Suggested reading
    • 18.4 Preliminaries
    • 18.5 Activity
  • Part IV: Data in Areal Units
  • 19 Area Data I
    • 19.1 Learning Objectives
    • 19.2 Suggested Readings
    • 19.3 Preliminaries
    • 19.4 Area Data
    • 19.5 Processes and Area Data
    • 19.6 Visualizing Area Data: Choropleth Maps
    • 19.7 Visualizing Area Data: Cartograms
  • 20 Activity 9: Area Data I
    • 20.1 Practice questions
    • 20.2 Learning objectives
    • 20.3 Suggested reading
    • 20.4 Preliminaries
    • 20.5 Activity
  • 21 Area Data II
    • 21.1 Learning Objectives
    • 21.2 Suggested Readings
    • 21.3 Preliminaries
    • 21.4 Proximity in Area Data
    • 21.5 Spatial Weights Matrices
    • 21.6 Creating Spatial Weights Matrices in R
    • 21.7 Spatial Moving Averages
    • 21.8 Other Criteria for Coding Proximity
  • 22 Activity 10: Area Data II
    • 22.1 Practice questions
    • 22.2 Learning objectives
    • 22.3 Suggested reading
    • 22.4 Preliminaries
    • 22.5 Activity
  • 23 Area Data III
    • 23.1 Learning Objectives
    • 23.2 Suggested Readings
    • 23.3 Preliminaries
    • 23.4 Spatial Moving Averages and Simulation
    • 23.5 The Spatial Moving Average as a Smoother
    • 23.6 Spatial Moving Average Scatterplots
    • 23.7 Spatial Autocorrelation and Moran’s \(I\) coefficient
    • 23.8 Moran’s \(I\) and Moran’s Scatterplot
    • 23.9 Hypothesis Testing for Spatial Autocorrelation
  • 24 Activity 11: Area Data III
    • 24.1 Practice questions
    • 24.2 Learning objectives
    • 24.3 Suggested reading
    • 24.4 Preliminaries
    • 24.5 Activity
  • 25 Area Data IV
    • 25.1 Learning objectives
    • 25.2 Suggested readings
    • 25.3 Preliminaries
    • 25.4 Decomposing Moran’s \(I\)
    • 25.5 Local Moran’s \(I\) and Mapping
    • 25.6 A Quick Note on Functions
    • 25.7 A Concentration approach for Local Analysis of Spatial Association
    • 25.8 A Short Note on Hypothesis Testing
    • 25.9 Detection of Hot and Cold Spots
    • 25.10 Other Resources
  • 26 Activity 12: Area Data IV
    • 26.1 Practice questions
    • 26.2 Learning objectives
    • 26.3 Suggested reading
    • 26.4 Preliminaries
    • 26.5 Activity
  • 27 Area Data V
    • 27.1 Learning Objectives
    • 27.2 Suggested Readings
    • 27.3 Preliminaries
    • 27.4 Regression Analysis in R
    • 27.5 Autocorrelation as a Model Diagnostic
    • 27.6 Variable Transformations
    • 27.7 A Note about Spatial Autocorrelation in Regression Analysis
  • 28 Activity 13: Area Data V
    • 28.1 Practice questions
    • 28.2 Learning objectives
    • 28.3 Suggested reading
    • 28.4 Preliminaries
    • 28.5 Activity
  • 29 Area Data VI
    • 29.1 Learning Objectives
    • 29.2 Suggested Readings
    • 29.3 Preliminaries
    • 29.4 Residual spatial autocorrelation revisited
      • 29.4.1 Incorrect Functional Form
      • 29.4.2 Omitted Variables
    • 29.5 Remedial Action
    • 29.6 Flexible Functional Forms and Models with Spatially-varying Coefficients
      • 29.6.1 Trend Surface Analysis
      • 29.6.2 Models with Spatially-varying Coefficients
    • 29.7 Spatial Error Model (SEM)
  • 30 Activity 14: Area Data VI
    • 30.1 Practice questions
    • 30.2 Learning objectives
    • 30.3 Suggested reading
    • 30.4 Preliminaries
      • 30.4.1 New York leukemia data
      • 30.4.2 Pennsylvania lung cancer
    • 30.5 Activity
  • Part V: Analysis and Prediction of Fields
  • 31 Spatially Continuous Data I
    • 31.1 Learning objectives
    • 31.2 Suggested readings
    • 31.3 Preliminaries
    • 31.4 Spatially continuous (field) data
    • 31.5 Exploratory visualization
    • 31.6 Tile-based methods
    • 31.7 Inverse distance weighting (IDW)
    • 31.8 \(k\)-point means
  • 32 Activity 15: Spatially Continuous Data I
    • 32.1 Practice questions
    • 32.2 Learning objectives
    • 32.3 Suggested reading
    • 32.4 Preliminaries
    • 32.5 Activity
  • 33 Spatially Continuous Data II
    • 33.1 Learning objectives
    • 33.2 Suggested readings
    • 33.3 Preliminaries
    • 33.4 Uncertainty in the predictions
    • 33.5 Trend surface analysis
    • 33.6 Accuracy and precision
  • 34 Activity 16: Spatially Continuous Data II
    • 34.1 Practice questions
    • 34.2 Learning objectives
    • 34.3 Suggested reading
    • 34.4 Preliminaries
    • 34.5 Activity
  • 35 Spatially Continuous Data III
    • 35.1 Learning objectives
    • 35.2 Suggested reading
    • 35.3 Preliminaries
    • 35.4 Residual spatial pattern
    • 35.5 Measuring spatial dependence in spatially continuous data
    • 35.6 Variographic analyisis
  • 36 Activity 17: Spatially Continuous Data III
    • 36.1 Practice questions
    • 36.2 Learning objectives
    • 36.3 Suggested reading
    • 36.4 Preliminaries
    • 36.5 Activity
  • 37 Spatially Continuous Data IV
    • 37.1 Learning objectives
    • 37.2 Suggested reading
    • 37.3 Preliminaries
    • 37.4 Using residual spatial pattern to estimate prediction errors
    • 37.5 Kriging: a method for optimal prediction.
  • 38 Activity 18: Spatially Continuous Data IV
    • 38.1 Practice questions
    • 38.2 Learning objectives
    • 38.3 Suggested reading
    • 38.4 Preliminaries
    • 38.5 Activity

An Introduction to Spatial Data Analysis and Statistics: A Course in R

An Introduction to Spatial Data Analysis and Statistics: A Course in R

Antonio Paez

2025-01-08