Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Geographically Weighted Regression-Modelling Spatial Non-Stationarity

Chris Brunsdon, Stewart Fotheringham and Martin Charlton
Journal of the Royal Statistical Society. Series D (The Statistician)
Vol. 47, No. 3 (1998), pp. 431-443
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2988625
Page Count: 13
  • Read Online (Free)
  • Download ($29.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Geographically Weighted Regression-Modelling Spatial Non-Stationarity
Preview not available

Abstract

In regression models where the cases are geographical locations, sometimes regression coefficients do not remain fixed over space. A technique for exploring this phenomenon, geographically weighted regression is introduced. A related Monte Carlo significance test for spatial non-stationarity is also considered. Finally, an example of the method is given, using limiting long-term illness data from the 1991 UK census.

Page Thumbnails

  • Thumbnail: Page 
[431]
    [431]
  • Thumbnail: Page 
432
    432
  • Thumbnail: Page 
433
    433
  • Thumbnail: Page 
434
    434
  • Thumbnail: Page 
435
    435
  • Thumbnail: Page 
436
    436
  • Thumbnail: Page 
437
    437
  • Thumbnail: Page 
438
    438
  • Thumbnail: Page 
439
    439
  • Thumbnail: Page 
440
    440
  • Thumbnail: Page 
441
    441
  • Thumbnail: Page 
442
    442
  • Thumbnail: Page 
443
    443