Christopher Lloyd (Archaeology School of Geography and Palaeoecolo

Spatial Data Analysis

An Introduction for GIS users

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Oxford University Press
• Seitenzahl: 224
• Erscheinungstermin: 3. Dezember 2009
• Englisch
• Abmessung: 246mm x 190mm x 19mm
• Gewicht: 452g
• ISBN-13: 9780199554324
• ISBN-10: 0199554323
• Artikelnr.: 29425037

Autorenporträt

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

Inhaltsangabe

* 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