Emerging Trends and Issues in Geo-Spatial Environmental Health: A Critical Perspective

Daniel A. Griffith ()
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Daniel A. Griffith: Department of Geospatial Information Sciences, the University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080-3021, USA

IJERPH, 2025, vol. 22, issue 2, 1-17

Abstract: This opinion piece postulates that quantitative environmental research and public health spatial analysts unknowingly tolerate certain spatial statistical model specification errors, whose remedies constitute some of the urgent emerging trends and issues in this subfield (e.g., forecasting disease spreading). Within this context, this paper addresses misspecifications affiliated with omitted variable bias complications arising from ignoring, and hence abandoning, negative spatial autocorrelation latent in georeferenced disease data, and/or being ill-informed about reigning teledependencies (i.e., long-distance spatial correlations). As imperative academic challenges, it advances elegant and convincing arguments to do otherwise. Its two particular themes are positive–negative spatial autocorrelation mixtures, and hierarchical autocorrelation generated by hegemonic urban systems. Comprehensive interpretations and implementations of these two conjectures constitute future research directions. Important conceptualizations for treatments reported in this paper include confounding variables and Moran eigenvector spatial filtering. This paper’s fundamental implication is an advocacy for a prodigious paradigm shift, a marked change in the collective mindsets and applications of spatial epidemiologists when specifying spatial regression equations to describe either environmental health data, or a publicly transparent geographic diffusion of diseases.

Keywords: hierarchy autocorrelation; mixture; omitted variable bias; spatial autocorrelation; urban hierarchy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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