Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics

Urška Demšar, A. Stewart Fotheringham, Martin Charlton

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The paper examines the potential for combining a spatial statistical methodology - Geographically Weighted Regression (GWR) - with geovisual analytical exploration to help understand complex spatio-temporal processes. This is done by applying the combined statistical - exploratory methodology to a simulated data set in which the behaviour of regression parameters was controlled across space and time. A variety of complex spatio-temporal processes was captured through space-time (i.e. as spatio-temporal) varying parameters whose values were known. The task was to see if the proposed methodology could uncover these complex processes from the data alone. The results of the experiment confirm that the combined methodology can successfully identify spatio-temporal patterns in the local GWR parameter estimates that correspond to the controlled behaviour of the original parameters.

Original languageEnglish (US)
Pages (from-to)181-197
Number of pages17
JournalInformation Visualization
Volume7
Issue number3-4
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • Geographically Weighted Regression (GWR)
  • Geovisual Analytics
  • Spatio-temporal dynamics
  • Spatio-temporal patterns
  • Spatio-temporal processes
  • Visual data exploration

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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