Aim The purpose of this study was to reconstruct early nineteenth-century vegetation and fire regimes to examine the role of fire, topography, and substrate interactions in shaping landscape and regional vegetation patterns. LocationOur study area was the Current River watershed of the Ozark Highlands in south-central Missouri, USA. MethodsWe combined analysis of early nineteenth-century Public Land Survey (PLS) notes and dendrochronology-based fire histories to reconstruct vegetation and disturbance regimes of pine-oak (Pinus-Quercus) woodlands.Three methods were used to display and analyse PLS data within a Geographic Information System (GIS): (1) simple point distributions for each tree species; (2) section line descriptions of each tree species and other coded features (e.g. 'prairie'); and (3) spatial interpolation of the point-tree data.Vegetation patterns were then related to geological parent material, topography, and mean fire-return intervals from 23 sites using correlation and Canonical Correspondence Analysis (CCA). ResultsThe most striking patterns in the early 1800 s were extensive stands of shortleaf pine (Pinus echinata Mill.) and oak-dominated 'barrens' (savanna) in the frequently burned areas south-west of the Current River, and more mesophytic, fire-sensitive species (red oaks (Quercus rubra L., Q. coccinea Muenchh.), maples (Acer rubrum L., Acer saccharum Marsh), eastern red cedar (Juniperus virginiana L.) in a fire shadow north-east of the river. Several kilometre-wide ecotones of pine-mixed hardwood encompassed the major pineries and barrens.Fire-return intervals and relative dominance of several tree species were strongly correlated at both fine (3-64 km 2 ) and coarse (> 100 km 2 ) spatial scales. At fine scales, relative dominance of shortleaf pine increased with increasing fire frequency during 1701-1820. Relative dominance of black oak (Q. velutina Lam.), and to a lesser extent post oak (Q. stellata Wang.), decreased with increasing fire frequency. Shortleaf pine and these xerophytic oak species occurred on similar bedrock types but were strongly differentiated by fire regimes. Main conclusions Fires exerted strong constraints on vegetation composition and patterns.Historical patterns of Native American occupancy in the region are consistent with the reconstructed vegetation and fire histories and suggest that anthropogenic fire regimes played an overriding role in the development of Ozark vegetation in the 1800s.

Introduction Health disparity affects both urban and rural residents, with evidence showing that rural residents have significantly lower health status than urban residents. Health equity is the commitment to reducing disparities in health and in its determinants, including social determinants. Objective This article evaluates the reach and context of a virtual urgent care (VUC) program on health equity and accessibility with a focus on the rural underserved population. Materials and Methods We studied a total of 5343 patient activation records and 2195 unique encounters collected from a VUC during the first 4 quarters of operation. Zip codes served as the analysis unit and geospatial analysis and informatics quantified the results. Results The reach and context were assessed using a mean accumulated score based on 11 health equity and accessibility determinants calculated for each zip code. Results were compared among VUC users, North Carolina (NC), rural NC, and urban NC averages. Conclusions The study concluded that patients facing inequities from rural areas were enabled better healthcare access by utilizing the VUC. Through geospatial analysis, recommendations are outlined to help improve healthcare access to rural underserved populations.

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With the rapid advance of geospatial technologies, the availability of geospatial data from a wide variety of sources has increased dramatically. It is beneficial to integrate / conflate these multi‐source geospatial datasets, since the integration of multi‐source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi‐source datasets over the same geographical area are often disparate. Accurately integrating geospatial data from different sources is a challenging task. Among the subtasks of integration/conflation, the most crucial one is feature matching, which identifies the features from different datasets as presentations of the same real‐world geographic entity. In this article we present a new relaxation‐based point feature matching approach to match the road intersections from two GIS vector road datasets. The relaxation labeling algorithm utilizes iterated local context updates to achieve a globally consistent result. The contextual constraints (relative distances between points) are incorporated into the compatibility function employed in each iteration's updates. The point‐to‐point matching confidence matrix is initialized using the road connectivity information at each point. Both the traditional proximity‐based approach and our relaxation‐based point matching approach are implemented and experiments are conducted over 18 test sites in rural and suburban areas of Columbia, MO. The test results show that our relaxation labeling approach has much better performance than the proximity matching approach in both simple and complex situations.

Merging geospatial analytics with big data approaches provides a mechanism for leveraging and maximizing uses of traditional survey data to further extant work in meaningful ways. This study examines the income inequality hypothesis, which proposes that ecological (summary-level) income inequality is harmful for population health. However, findings from extant work are inconsistent across health outcomes and levels of geography. We contribute to this debate by applying a big data geospatial approach to create three innovative measures that capture uniformity in income inequality across counties within U.S. states. Using data from the Behavioral Risk Factor Surveillance System and American Community Survey, we evaluate multilevel models of individuals within states to examine the ways that income inequality, operationalized as the Gini coefficient, and three spatial uniformity measures that capture the way income inequality is dispersed across space within states, are associated with several health outcomes. Specifically, the uniformity measures capture the extent to which (1) inequality is uniformly distributed spatially in states regardless of whether the level is high or low, (2) the extent to which states are more uniformly high in inequality across space, and (3) the extent to which they are more uniformly low in inequality. We conclude that state income inequality did not predict worse health across these outcomes (and indeed was associated with lower odds of depression and obesity). However, residents of states that have more uniformly high inequality across space are more likely to report below-average health, cardiovascular disease, difficulty concentrating, and that they have not sought care because it was too expensive. We conclude with a discussion of how a big data geospatial approach can further contribute to research on this and other public health topics where scholars primarily rely on traditional survey data.