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

Giuseppe Arbia () and Vincenzo Nardelli ()
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Giuseppe Arbia: Università Cattolica del Sacro Cuore
Vincenzo Nardelli: Università Cattolica del Sacro Cuore

Letters in Spatial and Resource Sciences, 2025, vol. 18, issue 1, No 15, 11 pages

Abstract: Abstract 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.

Keywords: Spatial econometrics; Spatial autocorrelation; Robust statistics; Epidemiological data analysis (search for similar items in EconPapers)
JEL-codes: C21 C31 I18 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12076-025-00409-y

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