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.
R package for computing Lacunarity for Spatial Raster
Example workflow for a large study area when using the GVI R package
Thinking critically about parameters before running the code is always important. In this post I will explain how to set important parameters when conducting a Visibility Analysis.
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.