Spatial Data Analysis

NOTE EDITORE

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. 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 the analysis 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 spatial data analysis tools should know something about how the approaches work rather than simply how to apply them. Online Resource Centre 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.

SOMMARIO

1.1 - Spatial data analysis1.2 - Purpose of the book1.3 - Key concepts1.4 - Structure of the book1.5 - Further reading2.1 - Introduction2.1 - Data and data models2.2.1 - Raster data2.2.2 - Vector data2.2.3 - Topology2.2.4 - Rasters and vectors in GIS software2.3 - Databases2.3.1 - Database management2.3.2 - The Geodatabase2.4 - Referencing systems and projections2.5 - Georeferencing2.6 - Geocoding2.7 - Spatial scale2.8 - Spatial data collection2.8.1 - Spatial sampling2.8.2 - Secondary data analysis2.8.3 - Remote sensing2.8.4 - Ground survey2.9 - Sources of data error2.9.1 - Uncertainty in spatial data analysis2.10 - Visualising spatial data2.11 - Querying data2.11.1 - Boolean logic2.12 - Summary2.13 - Further reading3.1 - Introduction3.2 - Univariate statistics3.3 - Multivariate statistics3.4 - Inferential statistics3.5 - Statistics and spatial data3.6 - Summary3.7 - Further reading4.1 - 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 - Moving windows: basic statistics in sub-regions4.7 - Geographical weights4.8 - Spatial dependence and spatial autocorrelation4.9 - The ecological fallacy and the modifiable areal unit problem4.10 - Merging polygons4.11 - Summary4.12 - Further reading5.1 - Introduction5.2 - Multiple features: overlays5.2.1 - Point in polygon5.2.2 - Overlay operators5.2.3 - 'Cookie cutter' operations: erase and clip5.2.4 - Applications and problems5.3 - Multicriteria decision analysis5.4 - Case study5.5 - Summary5.6 - Further reading6.1 - Introduction6.2 - Networks6.3 - Network connectivity6.4 - Summaries of network characteristics6.5 - Identifying shortest paths6.6 - The travelling salesperson problem6.7 - Location-allocation problems6.8 - Case study6.9 - Summary6.10 - Further reading7.1 - Introduction7.2 - Basic measures7.3 - Exploring spatial variations in point intensity7.3.1 - Quadrats7.3.2 - Kernel estimation7.4 - measures based on distances between events7.4.1 - Nearest neighbour methods7.4.2 - K function7.5 - Applications and other issues7.6 - Case study7.7 - Summary7.8 - Further reading8.1 - 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 reading9.1 - Introduction9.2 - Interpolation9.3 - Triangulated irregular networks9.4 - Regression for prediction9.4.1 - Trend surface analysis9.5 - Inverse distance weighting9.6 - Thin plate splines9.7 - Ordinary kriging9.7.1 - Variogram9.7.2 - Kriging9.7.3 - Cokriging9.8 - Other approaches and other issues9.9 - Areal interpolation9.10 - Case studies9.10.1 - Variogram estimation9.10.2 - Spatial interpolation9.11 - Summary9.12 - Further reading10.1 - 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 reading11.1 - Review of key concepts11.2 - Other issues11.3 - Problems11.4 - Where next?11.5 - Summary and conclusions

AUTORE

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

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780199554324
• Dimensioni: 245 x 12.0 x 188 mm Ø 435 gr
• Formato: Brossura
• Illustration Notes: 85 colour line drawings
• Pagine Arabe: 224