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Spatial Data Analysis: Models, Methods and Techniques - SpringerBriefs in Regional Science (Paperback)
  • Spatial Data Analysis: Models, Methods and Techniques - SpringerBriefs in Regional Science (Paperback)

Spatial Data Analysis: Models, Methods and Techniques - SpringerBriefs in Regional Science (Paperback)

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Paperback 80 Pages / Published: 05/08/2011
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The availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.

Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN: 9783642217197
Number of pages: 80
Weight: 153 g
Dimensions: 235 x 155 x 4 mm


From the reviews:

"Fischer and Wang detail the models, methods, and techniques that can be employed to describe and explain the pattern and behaviour of variables distributed over geographic space. ... This is a wholly useful text, aimed at quantitative geographers and spatial econometricians who ... want to develop their skills further. It would be ideal for masters-level students in GIS, spatial analysis, and econometrics as well as for the increasing body of researchers who are beginning to see the value of accounting for space in their models." (Daniel Lewis, Environmental and Planning B: Planning and Design, Vol. 39 (4), 2012)

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