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