Spatial Data Analysis

Description

What is the shortest route between one point and another in a road network? Where is the incidence of disease the highest? How does rainfall correlate with altitude? How does the concentration of a pollutant vary in space, and where do high concentrations correlate with densely populated areas?

Geographical or spatial data play a vital role in many parts of daily life. We are dependent on information about where things are located and about the attributes of those things, either directly, as in the use of a map for navigating around a city, or indirectly, where we use resources like water or gas.

Spatial Data Analysis: An Introduction for GIS Users introduces students to key principles about spatial data, the methods used to explore such data, and the kinds of problems that can be tackled using widely available analytical tools. Taking a gradual, systematic approach, the text opens with coverage of core concepts; these ideas are illustrated and reinforced with careful explanations, numerous worked examples, and case studies throughout the book.

Accessible to students who are new to the field, Spatial Data Analysis focuses on education rather than simple training; it not only shows students how to apply data analysis tools but also demonstrates how those tools work. A Companion Website provides resources for both students and instructors.

Table of Contents

Chapter 1. Introduction
1.1. Spatial data analysis
1.2. Purpose of the book
1.3. Key concepts
1.4. Structure of the book
1.5. Further reading
Chapter 2. Key concepts 1: GISystems
2.1. Introduction
2.1. Data and data models
2.2.1. Raster data
2.2.2. Vector data
2.2.3. Topology
2.3. Databases
2.3.1. Database management
2.4. Referencing systems and projections
2.5. Geocoding
2.6. Spatial data collection
2.6.1. Secondary sources
2.6.2. Remote sensing
2.6.3. Ground survey
2.7. Sources of data error
2.8. Visualising spatial data
2.9. Querying data
2.9.1. Boolean logic
2.10. Summary
2.11. Further reading
Chapter 3. Key concepts 2: statistics
3.1. Introduction
3.2. Univariate statistics
3.3. Multivariate statistics
3.4. Inferential statistics
3.5. Statistics and spatial data
3.6. Summary
3.7. Further reading
Chapter 4. Key concepts 3: spatial data analysis
4.1. Introduction
4.2. Distances
4.3. Measuring lengths and perimeters
4.3.1. Length of vector features
4.4. Measuring areas
4.4.1. Areas of polygons
4.5. Distances from objects: buffers
4.5.1. Vector buffers
4.5.2. Raster proximity
4.6. Spatial dependence and spatial autocorrelation
4.7. Moving windows: basic statistics in sub-regions
4.7. Geographical weights
4.9. Spatial scale
4.10. The ecological fallacy and the modifiable areal unit problem (MAUP)
4.11. Merging polygons
4.12. Uncertainty in spatial data analysis
4.13. Geographic data mining
4.14. Summary
4.15. Further reading
Chapter 5. Combining data layers
5.1. Introduction
5.2. Multiple features: overlays
5.2.1. Line intersection
5.2.2. Point in polygon
5.2.3. Overlay operators
5.2.4. 'Cookie cutter' operations: erase and clip
5.2.5. Applications and problems
5.3. Multicriteria decision analysis
5.4. Case study
5.5. Summary
5.6. Further reading
Chapter 6. Network analysis
6.1. Introduction
6.2. Networks
6.3. Network connectivity
6.4. Summaries of network characteristics
6.5. Identifying shortest paths
6.6. Location-allocation problems
6.7. Other problems and approaches
6.8. Case study
6.9. Summary
6.10. Further reading
Chapter 7. Exploring spatial point patterns
7.1. Introduction
7.2. Basic measures
7.3. Exploring spatial variations in point intensity
7.3.1. Quadrats
7.3.2. Kernel estimation
7.4. Distance based measures
7.4.1. Nearest neighbour methods
7.4.2. K function
7.5. Applications and other issues
7.6. Case study
7.7. Summary
7.8. Further reading
Chapter 8. Exploring spatial patterning in data values
8.1. Introduction
8.2. Spatial autocorrelation
8.3. Local statistics
8.4. Local univariate measures
8.4.1. Local spatial autocorrelation
8.5. Regression and correlation
8.5.1. Spatial regression
8.5.2. Moving window regression (MWR)
8.5.3. Geographically weighted regression (GWR)
8.6. Other approaches
8.7. Case studies
8.7.1. Spatial autocorrelation analysis
8.7.2. GWR
8.8. Summary
8.9. Further reading
Chapter 9. Spatial interpolation
9.1. Introduction
9.2. Spatial interpolation
9.3. Triangulated irregular networks
9.4. Regression for prediction
9.5. Inverse distance weighting
9.6. Thin plate splines
9.7. Ordinary kriging
9.7.1. Variogram
9.7.2. Kriging
9.8. Other approaches and other issues
9.9. Areal interpolation
9.10. Case studies
9.10.1. Variogram estimation
9.10.2. Spatial interpolation
9.11. Summary
9.12. Further reading
Chapter 10. Analysis of grids and surfaces
10.1. Introduction
10.2. Map algebra
10.3. Image processing
10.4. Spatial filters
10.5. Derivatives of altitude
10.6. Other products derived from surfaces
10.7. Case study
10.8. Summary
10.9. Further reading
Chapter 11. Summary
11.1. Review of key concepts
11.2. Approaches
11.3. Other issues
11.4. Problems
11.5. Where next?
11.6. Summary and conclusions
References
Appendix A. Matrix multiplication
Appendix B. Ordinary kriging system
Appendix C. Problems and solutions