Geographically Weighted Regression - The Analysis Of Spatially Varying Relationships

Geographical Weighted Regression (GWR) is a new local modelling technique for analysing spatial analysis. This technique allows local as opposed to global models of relationships to be measured and mapped. This is the first and only book on this technique, offering comprehensive coverage on this new 'hot' topic in spatial analysis.


* Provides step-by-step examples of how to use the GWR model using data sets and examples on issues such as house price determinants, educational attainment levels and school performance statistics
* Contains a broad discussion of and basic concepts on GWR through to ideas on statistical inference for GWR models
* uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details).

A. Stewart Fotheringham, Professor of Quantitative Geography, University of Newcastle. Chris Brunsdon, Senior Lecturer in Spatial Analysis, University of Newcastle. Martin Charlton, Lecturer in Geographical Information Systems, University of Newcastle.

Acknowledgements.

Local Statistics and Local Models for Spatial Data.

Geographically Weighted Regression: The Basics.

Extensions to the Basic GWR Model.

Statistical Inference and Geographically Weighted Regression.

GWR and Spatial Autocorrelation.

Scale Issues and Geographically Weighted Regression.

Geographically Weighted Local Statistics.

Extensions of Geographically Weighting.

Software for Geographically Weighted Regression.

Epilogue.

Bibliography.

Index.