Risk maps for cities: Incorporating streets into geostatistical models

We describe a methodology to transform spatial point data to incorporate permeable barriers, by distorting the map to widen streets, with one additional parameter. We use Gaussian Field models to estimate this additional parameter, and develop risk maps incorporating streets as permeable barriers. We demonstrate our method on simulated datasets and apply it to data on Triatoma infestans, a vector of Chagas disease in Arequipa, Peru. We found that the transformed landscape that best fit the observed pattern of Triatoma infestans infestation, approximately doubled the true Euclidean distance between neighboring houses on different city blocks. Our findings may better guide control of re-emergent insect populations.
Source: Spatial and Spatio-temporal Epidemiology - Category: Epidemiology Source Type: research