We propose a novel diabetes risk index (DRI-GLUCoSE) derived from socioeconomic status and satellite data based greenspace. We mapped index scores in two Canadian cities (Vancouver and Hamilton) and validated the index using fully adjusted logistic regression models to predict individual diabetes status. The final models achieved a predictive accuracy of 75%, the highest among environmental risk models to date. Our combined index of local greenspace and socioeconomic factors demonstrates that the environmental component of diabetes risk is not sufficiently explained by diet and physical activity, and that increasing urban greenspace may be a suitable means of reducing the burden of diabetes at the community scale.
A fast implementation of the IDW algorithm using Rcpp. I compare the results to the well established gstat R package.
Exposure to residential greenness or green spaces such as parks or gardens are beneficial for multiple measures of health. One type of greenspace exposure is visibility, referring to the visual perception of greenness. In this post I will demonstrate, how to conduct a viewshed based visibility analysis.
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COVID-19 Bewegungsradius Stand: 15.1.2020 Die 15-Kilomenter Regel, nachdem sich Bewohner mit einer 7-Tage Inzidenz von über 200 nur noch 15 km um ihren Wohnort bewegen dürfen, sorgt für Verwirrung.
As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.