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Testing for Clustering of Health Events within a Geographical Information System Framework

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  • S P Kingham

    (Department of Epidemiology and Public Health, University of Newcastle upon Tyne, NE2 4HH, England)

  • A C Gatrell
  • B Rowlingson

Abstract

An approach to analysing data for spatial clustering is outlined, with special reference to environmental epidemiology. The method is based on recent developments in spatial point-process modelling; specifically, on the use of so-called ‘second-order’ analysis of bivariate point patterns. This continuous-space approach offers some advantages over analytical methods that aggregate health events to areal units. The method is implemented within the framework of a proprietary geographical information system, ARC/INFO, and is illustrated with reference to health data from a questionnaire survey of children in Preston (Lancashire). The nature of the data gained from the questionnaire means that variables which may affect the health of the children studied can be accounted for within the analysis.

Suggested Citation

  • S P Kingham & A C Gatrell & B Rowlingson, 1995. "Testing for Clustering of Health Events within a Geographical Information System Framework," Environment and Planning A, , vol. 27(5), pages 809-821, May.
    • Handle: RePEc:sae:envira:v:27:y:1995:i:5:p:809-821
    DOI: 10.1068/a270809
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    References listed on IDEAS

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    1. Peter J. Diggle, 1990. "A Point Process Modelling Approach to Raised Incidence of a Rare Phenomenon in the Vicinity of a Prespecified Point," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 349-362, May.
    2. Peter J. Diggle & Barry S. Rowlingson, 1994. "A Conditional Approach to Point Process Modelling of Elevated Risk," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 433-440, May.
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    Cited by:

    1. Eric Marcon & Florence Puech, 2009. "Generalizing Ripley's K function to inhomogeneous populations," Working Papers halshs-00372631, HAL.

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