{"title":"Delineating Health Enumeration Areas in Florida","authors":"Bertram L. Melix, Christopher K. Uejio","doi":"10.1111/gean.70048","DOIUrl":"https://doi.org/10.1111/gean.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>Consideration of the spatial dimensions of health is often necessary for small area health investigations. This study introduces a novel approach to neighborhood delineation by designing geographic units defined by similarity in both behavioral and social determinants of health alongside criteria to ensure the robustness of health metrics (≥ 1000 individuals, ≥ 50 deaths). To create the Health Enumeration Areas (HEA), we first aggregate individual death records to census blocks and calculate drive time from each block's centroid to the nearest hospital. The number of deaths and decedents' smoking status and drive time to nearest hospital serve as constraints in the enriched constraints Max-P algorithm (EMP) regionalization technique. Across Florida counties, HEA generally outperformed census tracts, as evidenced by higher path silhouette scores associated with decedent tobacco use patterns and reduced within-unit heterogeneity. HEA also reduced unstable life expectancy estimates from ∼14% to ∼4% and accounted for a larger share of the population in those estimates, with improvements ranging from 20% to over 70% in many counties. The algorithm produced geographic units, similar in size to census tracts, that prioritized homogeneity and more accurately represents the conditions impacting health by addressing the uncertainty inherent in traditional areal units.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esther Jose, Nastaran Oladzad, David Sudit, Moises Sudit, Rajan Batta
{"title":"Kriging-Informed Coverage Sampling: An Integer Programming Approach to Optimizing Information Collection","authors":"Esther Jose, Nastaran Oladzad, David Sudit, Moises Sudit, Rajan Batta","doi":"10.1111/gean.70044","DOIUrl":"https://doi.org/10.1111/gean.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately mapping spatial phenomena with limited observations hinges on selecting sampling sites that minimize predictive uncertainty. We model this task by quantifying unsampled-location uncertainty with ordinary Kriging and framing site selection as an optimization problem. Because the resulting Kriging prediction-variance objective is nonlinear, we derive an integer program approximation called Kriging-informed coverage sampling that bounds the Kriging variance with a set of linear constraints. We prove that Kriging-informed coverage sampling is isomorphic to the classical Maximal Coverage Location Problem, thereby linking geostatistical uncertainty reduction to a well-studied family of location problems and enabling the use of well-established solution techniques. Computational experiments on synthetic landscapes and a remote-sensing case study show that Kriging-informed coverage sampling attains <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≥</mo>\u0000 </mrow>\u0000 <annotation>$$ ge $$</annotation>\u0000 </semantics></math>90% of the information gain achieved by exact non-linear solution methods while reducing solution times by up to two orders of magnitude.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esther Jose, Nastaran Oladzad, David Sudit, Moises Sudit, Rajan Batta
{"title":"Kriging-Informed Coverage Sampling: An Integer Programming Approach to Optimizing Information Collection","authors":"Esther Jose, Nastaran Oladzad, David Sudit, Moises Sudit, Rajan Batta","doi":"10.1111/gean.70044","DOIUrl":"https://doi.org/10.1111/gean.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately mapping spatial phenomena with limited observations hinges on selecting sampling sites that minimize predictive uncertainty. We model this task by quantifying unsampled-location uncertainty with ordinary Kriging and framing site selection as an optimization problem. Because the resulting Kriging prediction-variance objective is nonlinear, we derive an integer program approximation called Kriging-informed coverage sampling that bounds the Kriging variance with a set of linear constraints. We prove that Kriging-informed coverage sampling is isomorphic to the classical Maximal Coverage Location Problem, thereby linking geostatistical uncertainty reduction to a well-studied family of location problems and enabling the use of well-established solution techniques. Computational experiments on synthetic landscapes and a remote-sensing case study show that Kriging-informed coverage sampling attains <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≥</mo>\u0000 </mrow>\u0000 <annotation>$$ ge $$</annotation>\u0000 </semantics></math>90% of the information gain achieved by exact non-linear solution methods while reducing solution times by up to two orders of magnitude.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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