The investigations concerning the correlation between road accidents and poverty were done using the QGIS and GeoDa softwares, as indicated by the  "QGIS User Guide" (Sherman et al., 2004) and the  "GeoDa User Guide" (Anselin, 2003) respectively. 

The address point data vector file was imported on QGIS and intersected with the municipality borders in order to keep the information relevant to Vernier only. The excel data sheets containing the addresses of the households receiving allowances (housing assistance and malady insurance subsides) were imported as tables in QGIS. Using a common identifier ( IDPADR ) between the allowance tables and the address vector file joints were performed in order to have the geopraphical locations of the people needing financial help. Once this was done a count of the number of people receiving malady insurance subsides per cell of inhabited area (thus for 100 m x 100 m regions) was performed. The sum of the people receiving housing assistance was done in a similar way. Finally, centroids of the population grid containing the allowance data were created.

The vector file containing the accident points was then imported on QGIS. A distance matrix analysis was thereupon performed, seeking for the minimal distance between the recently created centroids and the accident points. This allowed to get a table characterizing the economical status of the population per cell (poorer regions being characterized as having higher number of persons needing allowances) and their minimal distance to the closest accident. 

This joined table was then imported on GeoDa, were several spatial analyses could be performed. Scatter plots between the accident and  housing assistance and malady insurance subside variables  were primarily executed. A simple multivariate linear regression followed, using the allowance variables as independent variables in order to characterize the dependent accident variable. Finally, a multivariate regression with dependent spatially weighted variables was implemented, using a Queen contiguity of order 1 for the creation of the weighting file.

The predicted values obtained by the linear and spatial regressions were then imported back in QGIS, where the minimal predicted distances to the nearest accident could be mapped. The number of classes chosen as to depict these estimated values was chosen with the help of the Huntsberger Index: \(Nbr\ of\ classes\ =\ 1\ +\ 3.3\cdot\log\left(nbr\ of\ spatial\ units\right)=1+3.3\cdot\log\left(404\right)\)     

Scatter plots were done with the minimal distance to an accident as dependent variable, and the number of people needing housing aids and health insurance subsides as independent variables. The obtained results were poor, with R² values of 0.032 and 0.067 for the housing aid and insurance subsides respectively. Such low values indicate that no valuable correlation can be made between these variables. The Moran's I values, which are represented by the regression slopes of the standardized data, were both negative (I = -0.179 for the housing aids and I = -0.260 for the health insurance subsides).