Spatial Data Analysis: An Introduction for GIS users

by Christopher Lloyd

Overview

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.

Product Details

ISBN-13:	9780199554324
Publisher:	Oxford University Press
Publication date:	02/01/2010
Edition description:	New Edition
Pages:	224
Product dimensions:	7.40(w) x 9.60(h) x 0.60(d)

About the Author

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

Table of Contents

Chapter 1. Introduction1.1. Spatial data analysis1.2. Purpose of the book1.3. Key concepts1.4. Structure of the book1.5. Further readingChapter 2. Key concepts 1: GISystems2.1. Introduction2.1. Data and data models2.2.1. Raster data2.2.2. Vector data2.2.3. Topology2.3. Databases2.3.1. Database management2.4. Referencing systems and projections2.5. Geocoding2.6. Spatial data collection2.6.1. Secondary sources2.6.2. Remote sensing2.6.3. Ground survey2.7. Sources of data error2.8. Visualising spatial data2.9. Querying data2.9.1. Boolean logic2.10. Summary2.11. Further readingChapter 3. Key concepts 2: statistics3.1. Introduction3.2. Univariate statistics3.3. Multivariate statistics3.4. Inferential statistics3.5. Statistics and spatial data3.6. Summary3.7. Further readingChapter 4. Key concepts 3: spatial data analysis4.1. Introduction4.2. Distances4.3. Measuring lengths and perimeters4.3.1. Length of vector features4.4. Measuring areas4.4.1. Areas of polygons4.5. Distances from objects: buffers4.5.1. Vector buffers4.5.2. Raster proximity4.6. Spatial dependence and spatial autocorrelation4.7. Moving windows: basic statistics in sub-regions4.7. Geographical weights4.9. Spatial scale4.10. The ecological fallacy and the modifiable areal unit problem (MAUP)4.11. Merging polygons4.12. Uncertainty in spatial data analysis4.13. Geographic data mining4.14. Summary4.15. Further readingChapter 5. Combining data layers5.1. Introduction5.2. Multiple features: overlays5.2.1. Line intersection5.2.2. Point in polygon5.2.3. Overlay operators5.2.4. 'Cookie cutter' operations: erase and clip5.2.5. Applications and problems5.3. Multicriteria decision analysis5.4. Case study5.5. Summary5.6. Further readingChapter 6. Network analysis6.1. Introduction6.2. Networks6.3. Network connectivity6.4. Summaries of network characteristics6.5. Identifying shortest paths6.6. Location-allocation problems6.7. Other problems and approaches6.8. Case study6.9. Summary6.10. Further readingChapter 7. Exploring spatial point patterns7.1. Introduction7.2. Basic measures7.3. Exploring spatial variations in point intensity7.3.1. Quadrats7.3.2. Kernel estimation7.4. Distance based measures7.4.1. Nearest neighbour methods7.4.2. K function7.5. Applications and other issues7.6. Case study7.7. Summary7.8. Further readingChapter 8. Exploring spatial patterning in data values8.1. Introduction8.2. Spatial autocorrelation8.3. Local statistics8.4. Local univariate measures8.4.1. Local spatial autocorrelation8.5. Regression and correlation8.5.1. Spatial regression8.5.2. Moving window regression (MWR)8.5.3. Geographically weighted regression (GWR)8.6. Other approaches8.7. Case studies8.7.1. Spatial autocorrelation analysis8.7.2. GWR8.8. Summary8.9. Further readingChapter 9. Spatial interpolation9.1. Introduction9.2. Spatial interpolation9.3. Triangulated irregular networks9.4. Regression for prediction9.5. Inverse distance weighting9.6. Thin plate splines9.7. Ordinary kriging9.7.1. Variogram9.7.2. Kriging9.8. Other approaches and other issues9.9. Areal interpolation9.10. Case studies9.10.1. Variogram estimation9.10.2. Spatial interpolation9.11. Summary9.12. Further readingChapter 10. Analysis of grids and surfaces10.1. Introduction10.2. Map algebra10.3. Image processing10.4. Spatial filters10.5. Derivatives of altitude10.6. Other products derived from surfaces10.7. Case study10.8. Summary10.9. Further readingChapter 11. Summary11.1. Review of key concepts11.2. Approaches11.3. Other issues11.4. Problems11.5. Where next? 11.6. Summary and conclusionsReferencesAppendix A. Matrix multiplicationAppendix B. Ordinary kriging systemAppendix C. Problems and solutions

Editorial Reviews

"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

From the Publisher