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|Title:||Spatially clustered associations in health related geospatial data|
|Authors:||LEIBOVICI Didier G.; BASTIN LUCY; ANAND Suchith; HOBONA Gobe; JACKSON Mike|
|Citation:||Transactions in GIS vol. 15 no. 3 p. 347–364|
|Type:||Articles in periodicals and books|
|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 and Web 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 paper 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. Authors: Didier G. Leibovici, Lucy Bastin, Suchith Anand, Gobe Hobona and Mike Jackson|
|JRC Institute:||Sustainable Resources|
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