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

Abstract

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.

Understanding Spatial Autocorrelation: An Everyday Metaphor and Additional New Interpretations

“…Haining [27] highlights this SA nonindependence feature as being instrumental to geography's contribution to spatial statistics, commenting that SA relates to both scale and resolution of geographic data. Cliff and Ord [14] acknowledge that mis-specified regression models can create spurious residual SA, a theme discussed in detail by McMillen [29], and in terms of omitted variable beckoning by Griffith and Chun [34], can introduce omitted variable bias, especially in the presence of disregarded negative SA [33]. In these two latter multivariate contexts, a response variable's mean varies, rather than being a constant (e.g., only an intercept term); SA contained in a response variable is a function of either that latent in related covariates, or spatial lag terms appearing in spatial autoregressive model specifications (e.g., conditional autoregressive (CAR), simultaneous autoregressive (SAR), and autoregressive response (AR) versions being the most popular) that attempt to usurp missing variable effects.…”

“…In turn, improved SA comprehension can contribute to such endeavors as "develop[ing] and offer[ing] new strategies, visions and proposals on the role of sustainability and resilience related to urban and rural contexts" [1], such as spatially adjusted analytical techniques, or "help[ing] policy-makers to manage the new chances set up by a particularly complex and dynamic socioeconomic scenario worldwide" [1], such as furnishing appropriate tools for monitoring and evaluating sustainability progress. To these ends, this paper makes the following two contributions: (1) establishing the jigsaw puzzle metaphor for explaining in relatively simple and intelligible terms the concept of SA; and, (2) the additional interpretation of SA as frequently being a mixture of positive and negative local geographic relationships (supplementing [32,33]). Within the confines of this second knowledge advancement, this paper presents a Perhaps the most crucial and profound revelation the jigsaw puzzle metaphor motivates is construction of positive and negative SA Moran scatterplot trendline pairs, which serendipitously demonstrates graphically for the first time that the negative SA slopes typically are much shallower than their positive SA accompaniments in these mixtures; negative SA almost always is weaker and therefore tends to be much less salient.…”

“…The objective of this paper is to augment this body of literature by explaining the concept of SA utilizing a common everyday object as a metaphor. Doing so contributes to knowledge particularly by establishing a better comprehension of negative SA, one of the most neglected concepts in spatial statistics/econometrics [31], as well as mixtures of positive and negative SA [32,33].…”