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<p>John Snow is heralded as a pioneering figure in epidemiology and spatial analysis, notably through his innovative approach to mapping the 1854 cholera outbreak in London. This study seeks to merge the domains of historical epidemiological research and established spatial statistical techniques by utilizing the Moran’s coefficient (a very popular measure of spatial autocorrelation), on the original cholera dataset collected by John Snow. Despite the Moran coefficient celebrated ability to identify spatial autocorrelation patterns, its application to Snow’s dataset dramatically fails in detecting the outbreak of the cholera epidemic, primarily due to the presence of outliers in the dataset. This paper discusses the implications of these evidences in health applications of the standard spatial statistical methods and suggests some robust alternative to tackle the problem.</p>

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A robust spatial correlation analysis for a better understanding of John Snow ghost map

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月4日

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  • Giuseppe Arbia,
  • Vincenzo Nardelli

摘要

John Snow is heralded as a pioneering figure in epidemiology and spatial analysis, notably through his innovative approach to mapping the 1854 cholera outbreak in London. This study seeks to merge the domains of historical epidemiological research and established spatial statistical techniques by utilizing the Moran’s coefficient (a very popular measure of spatial autocorrelation), on the original cholera dataset collected by John Snow. Despite the Moran coefficient celebrated ability to identify spatial autocorrelation patterns, its application to Snow’s dataset dramatically fails in detecting the outbreak of the cholera epidemic, primarily due to the presence of outliers in the dataset. This paper discusses the implications of these evidences in health applications of the standard spatial statistical methods and suggests some robust alternative to tackle the problem.