Spatially clustered associations in health related geospatial data

Didier Leibovici, Lucy Bastin, Suchith Anand, Gobe Hobona, Mike Jackson

Research output: Contribution to journalArticle

Abstract

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources an dWeb services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.
Original languageEnglish
Pages (from-to)347-364
Number of pages18
JournalTransactions in GIS
Volume15
Issue number3
DOIs
Publication statusPublished - Jul 2011

Keywords

  • overlaying maps using
  • desktop GIS
  • multivariate spatial analysis
  • data sources
  • Web services
  • Internet
  • availablility
  • standards
  • OGC
  • geospatial mashups
  • thematic data
  • spatial clusters
  • correlation of outcomes
  • cluster detection
  • multivariate associations
  • background populations
  • public health
  • epidemiological mapping
  • statistic framework
  • statistical map methodologies

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