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.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.

BACKGROUND: Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. METHODS: Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. RESULTS: We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. CONCLUSIONS: The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.