Spatial Data Analysis: An Introduction for GIS users

Paperback | December 26, 2009

byChristopher Lloyd

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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 populatedareas?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 wateror 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 theanalysis 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 spatialdata analysis tools should know something about how the approaches work rather than simply how to apply them. 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.

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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 populatedareas?Geographical or spatial data play a vital r...

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

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Format:PaperbackDimensions:272 pages, 9.69 × 7.44 × 0.68 inPublished:December 26, 2009Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199554323

ISBN - 13:9780199554324

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Table of Contents

1. Introduction1.1 Spatial data analysis1.2 Purpose of the book1.3 Key concepts1.4 Structure of the book1.5 Further reading2. Key concepts 1: GISystems2.1 Introduction2.2 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 reading3. Key concepts 2: statistics3.1 Introduction3.2 Univariate statistics3.3 Multivariate statistics3.4 Inferential statistics3.5 Statistics and spatial data3.6 Summary3.7 Further reading4. 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-regions4.8 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 reading5. 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 reading6. 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 reading7. 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 reading8. 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 reading9. 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 reading10. 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 reading11. 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