Spatial Data Analysis

Description

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.

• A focused introduction to the key ideas and methods from which readers can build a firm foundation in spatial data analysis.
• Assumes only limited prior knowledge of GIS and starts from first principles, making it ideal for anyone new to the field.
• Worked examples and case studies demonstrate all of the key methods introduced, to put principles into an applied context.
• The Online Resource Centre features the synthetic data and worked examples needed to enable readers to experiment with the methods detailed.

About the Author(s)

Christopher Lloyd, School of Geography, Archaeology and Palaeoecology, Queens University, Belfast

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: GIS
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
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: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
Chapter 5. Combining data layers
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
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:The travelling salesperson problem
6.7:Location-allocation problems
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: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
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: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
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:Other issues
11.3:Problems
11.4:Where next?
11.5:Summary and conclusions
References
Appendix A. Matrix multiplication
Appendix B. The exponential function
Appendix C. The inverse tangent Appendix D. Line Intersection Appendix E. Ordinary least squares Appendix F. Ordinary kriging system Appendix G. Problems and solutions

Reviews

"It has long been this reviewers contention that if a student is taught the fundamentals and theory of geographic information systems, then all one has to ask is how does a particular software package do what I need? With this textbook Lloyd has achieved what he stated and provides a great resource for understanding advanced topics in spatial data analysis." - Joe Aufmuth, University of Florida in Journal of Spatial Science

"The authors approach in this text definitely strikes a chord not simply as a source for the geography user but also for wider clients of geographic information science (GISc). In favouring an education approach (rather than training), the book captures a progression of knowledge for the GISc undergraduate.The author has clearly faced the tribulations of constructing GISc/GIS courses and the focus reflected in this books approach and content are well worth consideration." - Geography, Autumn 2011