Assessing geospatial models to explain the occurrence of clandestine graves in Mexico.

We present an assessment of several geospatial layers proposed as models for detecting clandestine graves in Mexico. The analyses were based on adapting the classical ROC curves to geospatial data (gROC) using the fraction of the predicted area instead of the false positive rate. Grave locations were obtained for ten Mexican states that represent the most conflicting regions in Mexico, and 30 layers were computed to represent geospatial models for grave detection. The gROC analysis confirmed that the travel time from urban streets to grave locations was the most critical variable for detecting graves, followed by nighttime light brightness and population density, whereas, contrary to the rationale, a previously proposed visibility index is less correlated with grave locations. We were also able to deduce which variables are most relevant in each state and to determine optimal thresholds for the selected variables.

Snow's case revisited: new tool in geographic profiling of epidemiology

Abstract Geographic Profiling technique is used to find the origin of a series of crimes. The method was recently extended to other fields. One of the best renowned data in epidemiology is that by John Snow during an outburst of cholera in London. We wrote Python scripts to perform the analyses to apply the Geographic Profiling for individuating the starting origin of an infection by using the old Snow's data set. We modified the method by applying a weight to each point of the map where cases of cholera were reported. The weight was proportional to the number of cases in a given location.This modification of the Geographic Profiling method allowed to individuate in the map an area of maximum probability of the infection source, which was a few meters wide and including the historically known source of cholera, that is the "classical" water pump at Broad Street.The method appears to be a useful complement in order to individuate the source of epidemics when available data about the cases of the infections can be summarized on a map.