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Spatial Data Analysis

An Introduction for GIS users

Christopher Lloyd

Publication Date - February 2010

ISBN: 9780199554324

224 pages

In Stock

Retail Price to Students: $68.95


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.

About the Author(s)

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


"It has long been this reviewer's 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

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
    Appendix A. Matrix multiplication
    Appendix B. Ordinary kriging system
    Appendix C. Problems and solutions