Abstract
This paper illustrates some of the methods used by geographers to specify appropriate models of the space-time patterns found in geocoded disease data. Such data are of two basic forms: point or area-based. Two data sets are analyzed. For point patterns, we use the classic data collected in 1854 by Dr. John Snow which give the geographical location of deaths from cholera in the Golden Square (Soho) district of London, England. Simple mapping methods based upon measures of geographical centrality (the spatial mean, median and mode) and Thiessen polygon techniques point to one particular water pump as the source of the cholera outbreak. The results of these methods are compared with those from nearest neighbour analysis as developed by Ripley (1976) and Diggle (1983). For area-based data, we use the monthly reported number of measles cases per million population in the United States between 1962 and 1995 at different spatial scales. The techniques of spatial autocorrelation analysis, disease centroids, and multidimensional scaling are used to unravel the distinctive geography of measles transmission into and within the United States that resulted from the systematic vaccination programmes articulated from 1967 by the Centers for Disease Control (CDC).
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© 1998 B. G. Teubner Verlagsgesellschaft Leipzig
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Cliff, A.D., Haggett, P., Smallman-Raynor, M.R. (1998). Detecting Space—Time Patterns in Geocoded Disease Data. Cholera in London, 1854 Measles in the United States, 1962–95. In: Gierl, L., Cliff, A.D., Valleron, AJ., Farrington, P., Bull, M. (eds) Geomed ’97. Informatik und Unternehmensführung. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-95397-1_1
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DOI: https://doi.org/10.1007/978-3-322-95397-1_1
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