Vectorial and raster collected data sets have been gathered into one single shapefile using Quantum GIS (QGIS ,référence) software. Larger scaled data sets (domestic violence on women and neo-mortality rate) have been resampled to the Second-Order Administrative Division (SOAD) scale, or equivalently, the district scale. To do this, they were exported as a raster image with resolution of XX m2 with the Rasterize (référence?) module of QGIS, and finally polygonized at the SOAD scale using the Spatial Statistics plugin (référence?) of QGIS. They has been polygonized by taking the majority value contained inside each SOAD polygon, in order to avoid edge effects between two provinces. Neo-mortality rate have finally been multiplied by the population of women per SOAD, district, which is  contained in the census data set, and multiplied by 1000 to obtain an estimation of the number of new born mortality per district.

Travel time to closest facility has been estimated at the district scale by taking the mean of the values contained inside the district polygons.

Datasets have been explored using GeoDa software. Regressions were produced with the regression tool of GeoDa, supposing that neo-mortality GeoDa. The multivariate linear regression was done according to the following formula:

The dependent variable, neo_mort,  is a dependant variable the mortality rate per 1000  during the first 28 days  of life. The independent or explicative variables are: health_dist, which is proportional to the  distance in minutes that it takes  to reach the nearest  health facilities, electricity coverage, household center; elec, that represents the percentage of households with access to electricity; household_size, that represents the average  size and domestic in family members for each district; vio_tot, that represents the rate of domestic  violence on women.