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
PART I: Getting to know the technology
