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.

Assessing optical remote sensing for grave detection.

The application of an effective and ready-to-use tool for discovering clandestine graves is crucial for solving a number of cases where disappearance of people is involved. This is the case in Mexico, where the government drug war has resulted in a large number of missing people that has been estimated to be over 40,000 since the year 2006. In this article, we report results from an experimental study on simulated animal graves detection using several techniques from optical remote sensing. Results showed that several spectral indices from hyperspectral and/or multispectral sensors may be used to detect N-enriched vegetation. Thermal imagery was also effective to detect underground voids through differential thermography, although this was only effective for detecting large graves with bare terrain. Lastly, while dense pointclouds reconstructed from oblique aerial photography was able to detect vegetation regrowth over the pits, the terrain subsidence was not sufficiently large to be detected with this technique, even in the case of mechanical removal of vegetation.