Geographically weighted local statistics applied to binary data

Chris Brunsdon, Stewart Fotheringham, Martin Charlton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper considers the application of geographically weighting to summary statistics for binary data. We argue that geographical smoothing techniques that are applied to descriptive statistics for ratio and interval scale data may also be applied to descriptive statistics for binary categorical data. Here we outline how this may be done, focussing attention on the odds ratio statistic used for summarising the linkage between a pair of binary variables. An example of this is applied to data relating to house sales, based on over 30,000 houses in the United Kingdom. The method is used to demonstrate that time trends in the building of detached houses vary throughout the country.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages38-50
Number of pages13
Volume2478
ISBN (Print)9783540442530
StatePublished - 2002
Externally publishedYes
Event2nd International Conference on Geographic Information Science, GIScience 2002 - Boulder, United States
Duration: Sep 25 2002Sep 28 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2478
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Geographic Information Science, GIScience 2002
CountryUnited States
CityBoulder
Period9/25/029/28/02

Fingerprint

Binary Data
Statistics
Smoothing Techniques
Binary Variables
Nominal or categorical data
Odds Ratio
Linkage
Weighting
Statistic
Vary
Sales
Interval
Demonstrate

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Brunsdon, C., Fotheringham, S., & Charlton, M. (2002). Geographically weighted local statistics applied to binary data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2478, pp. 38-50). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2478). Springer Verlag.

Geographically weighted local statistics applied to binary data. / Brunsdon, Chris; Fotheringham, Stewart; Charlton, Martin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2478 Springer Verlag, 2002. p. 38-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2478).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Brunsdon, C, Fotheringham, S & Charlton, M 2002, Geographically weighted local statistics applied to binary data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2478, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2478, Springer Verlag, pp. 38-50, 2nd International Conference on Geographic Information Science, GIScience 2002, Boulder, United States, 9/25/02.
Brunsdon C, Fotheringham S, Charlton M. Geographically weighted local statistics applied to binary data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2478. Springer Verlag. 2002. p. 38-50. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Brunsdon, Chris ; Fotheringham, Stewart ; Charlton, Martin. / Geographically weighted local statistics applied to binary data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2478 Springer Verlag, 2002. pp. 38-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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