Spatial Data Analysis An Introduction for GIS users

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. Making use of spatial data requires a whole set of approaches to extract information from those data and make them useful. Underpinning these approaches is the analysis of data. Spatial Data Analysis introduces key principles about spatial data and provides guidance on methods for their exploration; it provides a set of key ideas or frameworks that will give the reader knowledge of the kinds of problems that can be tackled using the tools that are widely available for the analysis of spatial data. The approach is gradual and systematic; the initial focus is on themes that follow through the rest of the book. These key ideas are introduced, illustrated, and restated to ensure that readers develop a clear understanding of them. Beyond careful explanations, a clear understanding is fostered still further by numerous worked examples and case studies. In short, the stress is on first principles and reinforcement of key ideas throughout - on education rather than simply training, based on the conviction that users of spatial data analysis tools should know something about how the approaches work rather than simply how to apply them. Online Resource Centre The Online Resource Centre to accompany Spatial Data Analysis features For registered adopters of the book: Figures from the book, available to download. For students: Synthetic data and worked examples to enable readers to experiment with the methods described in the book.

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
2.1 - Introduction
2.1 - Data and data models
2.2.1 - Raster data
2.2.2 - Vector data
2.2.3 - Topology
2.2.4 - Rasters and vectors in GIS software
2.3 - Databases
2.3.1 - Database management
2.3.2 - The Geodatabase
2.4 - Referencing systems and projections
2.5 - Georeferencing
2.6 - Geocoding
2.7 - Spatial scale
2.8 - Spatial data collection
2.8.1 - Spatial sampling
2.8.2 - Secondary data analysis
2.8.3 - Remote sensing
2.8.4 - Ground survey
2.9 - Sources of data error
2.9.1 - Uncertainty in spatial data analysis
2.10 - Visualising spatial data
2.11 - Querying data
2.11.1 - Boolean logic
2.12 - Summary
2.13 - Further reading
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
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 - Moving windows: basic statistics in sub-regions
4.7 - Geographical weights
4.8 - Spatial dependence and spatial autocorrelation
4.9 - The ecological fallacy and the modifiable areal unit problem
4.10 - Merging polygons
4.11 - Summary
4.12 - Further reading
5.1 - Introduction
5.2 - Multiple features: overlays
5.2.1 - Point in polygon
5.2.2 - Overlay operators
5.2.3 - 'Cookie cutter' operations: erase and clip
5.2.4 - Applications and problems
5.3 - Multicriteria decision analysis
5.4 - Case study
5.5 - Summary
5.6 - Further reading
6.1 - Introduction
6.2 - Networks
6.3 - Network connectivity
6.4 - Summaries of network characteristics
6.5 - Identifying shortest paths
6.6 - The travelling salesperson problem
6.7 - Location-allocation problems
6.8 - Case study
6.9 - Summary
6.10 - Further reading
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 - measures based on distances between events
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
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
9.1 - Introduction
9.2 - Interpolation
9.3 - Triangulated irregular networks
9.4 - Regression for prediction
9.4.1 - Trend surface analysis
9.5 - Inverse distance weighting
9.6 - Thin plate splines
9.7 - Ordinary kriging
9.7.1 - Variogram
9.7.2 - Kriging
9.7.3 - Cokriging
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
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
11.1 - Review of key concepts
11.2 - Other issues
11.3 - Problems
11.4 - Where next?
11.5 - Summary and conclusions

Chris Lloyd is a Lecturer in Geography (GIS) in the School of Geography, Archaeology, and Paleoecology at Queen's University, Belfast.