Spatial cluster detection on detailed data without constraint of continuousness

Akihito Ujiie, Junya Fukumoto

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

Abstract

Spatial cluster is the set of geographical units where concentration of events is observed. Spatial clusters provide useful information for under-standing mechanism and characteristic of socioeconomic activities. Sever-al methods have been proposed for cluster detection. However, there is no existing method relaxes a constraint on adjacency of geographical units that compose clusters. Constraint that requires exact adjacency may have significant impact on detected clusters, especially in the case of detailed data. In this study, we propose a new cluster detection method relaxes con-straints on shape and adjacency. Along the lines of model-based clustering, we assume spatial data arise through a probabilistic model. Employing Potts model on the probabilistic model, we can embed constraints on shape in the probabilistic model and relax constraints on geometric shape. The applicability of the proposed method is tested on case studies using mesh data of Japanese economic census.

Original languageEnglish
Title of host publicationCUPUM 2015 - 14th International Conference on Computers in Urban Planning and Urban Management
PublisherCUPUM
ISBN (Electronic)9780692474341
Publication statusPublished - 2015 Jan 1
Event14th International Conference on Computers in Urban Planning and Urban Management, CUPUM 2015 - Cambridge, United States
Duration: 2015 Jul 72015 Jul 10

Publication series

NameCUPUM 2015 - 14th International Conference on Computers in Urban Planning and Urban Management

Other

Other14th International Conference on Computers in Urban Planning and Urban Management, CUPUM 2015
CountryUnited States
CityCambridge
Period15/7/715/7/10

ASJC Scopus subject areas

  • Environmental Engineering
  • Geography, Planning and Development
  • Urban Studies
  • Ecology
  • Civil and Structural Engineering

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