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Description  |  Agenda  |  Instructors & Participants  |  Travel & Accommodations

Geographically Weighted Regression and Associated Statistics
Santa Barbara, CA - August 4-8, 2003
The application deadline was March 31, 2003.

Host institution
CSISS, University of California, Santa Barbara

A. Stewart Fotheringham (coordinator), Chris Brunsdon, and Martin Charlton (all of The University of Newcastle, UK)

The Workshop
The standard procedure in the vast majority of empirical analyses of spatial data is either to calculate a global statistic or to calibrate a global model. The term ‘global’ implies that all the spatial data are used to compute a single statistic that is essentially an average of the conditions that exist throughout the study area in which the data have been measured. Such a procedure is flawed when the relationships being measured vary over space. Geographically Weighted Regression (GWR) is a statistical technique that allows variations in relationships over space to be measured within a single modelling framework. The output from GWR is a set of surfaces, each surface depicting the spatial variation of a relationship.

The technique is based on regular regression modelling but can be extended in many different ways. It provides a great richness in the results obtained for any spatial data set and should be useful across all disciplines in which spatial data are used.

The workshop will be based around a textbook: Fotheringham A. S., C. Brunsdon, and M. Charlton, Geographically Weighted Regression: the analysis of spatially varying relationships (Wiley 2002), written by the presenters of the workshop. Participants will be supplied with a copy of the text as part of the course. The authors have also written windows-based, user-friendly software for GWR, which will also be supplied to participants.

Topics Covered
The workshop will be a mix of lectures and practical, computer-based sessions. Topics to be covered include local statistics and local models, the basics of GWR with examples, statistical inference and GWR, GWR and spatial autocorrelation, extensions to the basic GWR framework and concept, applications of specialized GWR software, and visualizing the output in ArcView 3.3.

Exercises will be provided to participants but they will also be expected to bring their own spatial data set for experimentation with GWR. Participants will present the results of their GWR analyses on their own data sets at the conclusion of the course.

There are no registration fees associated with CSISS workshops. Eligibility for attendance is determined through a competitive application process. Additionally, successful applicants may receive a $500 scholarship to help offset travel and lodging expenses. Full details will be outlined in letters of acceptance.


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