Brunsdon, Chris and Fotheringham, Stewart and Charlton, Martin
(2008)
Geographically Weighted Regression: A Method
for Exploring Spatial Nonstationarity.
Encyclopedia of Geographic Information Science.
p. 558.
ISSN 9781412913133
Abstract
Spatial nonstationarity is a condition in which a simple ‘global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this
paper, a technique is developed, termed geogra hically weighted regression, model which allows diferent relationships to exist at diferent points in space.
This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigatin the null non-stationa y one and also for testing whether individual regression coeficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U. K. census relating car ownership rates to social class and mule unemployment. The paper concludes by discussing ways in which the technique can be extended.
| Item Type: |
Article
|
| Keywords: |
Geographically Weighted Regression; Spatial Nonstationarity; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG |
| Item ID: |
5895 |
| Depositing User: |
Prof. Chris Brunsdon
|
| Date Deposited: |
20 Feb 2015 15:18 |
| Journal or Publication Title: |
Encyclopedia of Geographic Information Science |
| Publisher: |
SAGE Publications |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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