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

Fingerprint

risk factor
spatial analysis
limiting factor
public health
GIS
methodology
health
method
test
statistics
services

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

Cite this

Leibovici, Didier ; Bastin, Lucy ; Anand, Suchith ; Hobona, Gobe ; Jackson, Mike. / Spatially clustered associations in health related geospatial data. In: Transactions in GIS. 2011 ; Vol. 15, No. 3. pp. 347-364.
@article{13654e680de7498790c4d545f042781d,
title = "Spatially clustered associations in health related geospatial data",
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.",
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",
author = "Didier Leibovici and Lucy Bastin and Suchith Anand and Gobe Hobona and Mike Jackson",
year = "2011",
month = "7",
doi = "10.1111/j.1467-9671.2011.01252.x",
language = "English",
volume = "15",
pages = "347--364",
journal = "Transactions in GIS",
issn = "1361-1682",
publisher = "Wiley-Blackwell",
number = "3",

}

Spatially clustered associations in health related geospatial data. / Leibovici, Didier; Bastin, Lucy; Anand, Suchith; Hobona, Gobe; Jackson, Mike.

In: Transactions in GIS, Vol. 15, No. 3, 07.2011, p. 347-364.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Spatially clustered associations in health related geospatial data

AU - Leibovici, Didier

AU - Bastin, Lucy

AU - Anand, Suchith

AU - Hobona, Gobe

AU - Jackson, Mike

PY - 2011/7

Y1 - 2011/7

N2 - 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.

AB - 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.

KW - overlaying maps using

KW - desktop GIS

KW - multivariate spatial analysis

KW - data sources

KW - Web services

KW - Internet

KW - availablility

KW - standards

KW - OGC

KW - geospatial mashups

KW - thematic data

KW - spatial clusters

KW - correlation of outcomes

KW - cluster detection

KW - multivariate associations

KW - background populations

KW - public health

KW - epidemiological mapping

KW - statistic framework

KW - statistical map methodologies

UR - http://www.scopus.com/inward/record.url?scp=79958272614&partnerID=8YFLogxK

UR - http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9671.2011.01252.x/abstract

U2 - 10.1111/j.1467-9671.2011.01252.x

DO - 10.1111/j.1467-9671.2011.01252.x

M3 - Article

VL - 15

SP - 347

EP - 364

JO - Transactions in GIS

JF - Transactions in GIS

SN - 1361-1682

IS - 3

ER -