Spatial autocorrelation mixtures in geospatial disease data: An important global epidemiologic/public health assessment ingredient?

Geographic-oriented public health has a time-honored history, beginning with such classic assessments as John Snow's cholera deaths vis-à-vis London's Broad Street water pump. His constructed map illustrates how gathering locational information about diseases and mapping its static as well as diffusion map patterns benefit society in the long run. Spatial autocorrelation (SA)—a habitual manifestation of geospatial data locational tagging/indexing characterizing their nonrandom mixture of attribute values across a geographic landscape—latent in georeferenced disease data, is a key feature of such assessment instruments. The objective of this article is to highlight the presence of positive–negative SA mixtures, rather than solely positive SA, in global epidemiologic/public health data, regardless of the place on Earth that houses them, and consequential impacts on the assessment of such data.