Discussion

Observing the results yielded by the scatter plots, the distance of one's home to the nearest accident is apparently not linked to the economical status of the person living there. However, a negative Moran's I expressing negative autocorrelation, if the R2 values would have been more conclusive one could have already made the assumption that the inhabited areas with less allowances are situated at greater distances to the nearest accidents.  This would indicate that poorer people are more at risk when considering road-traffic accidents.
This initial hypothesis is reinforced when looking at the results of the mutlivariable regressions. Even if the determination coefficient stay pretty low, a clear improvement can be noticed when the neighboring cells are taken into account. A higher value of R2 means indeed that the model is better at approximating the values of the dependent variable thanks to the independent ones.  
The difference between the maps shown in Fig. 5 and Fig. 8 illustrate this improvement. The spatial regression predicted distances illustrated in Fig. 8 do indeed somewhat better represent the actual distances to the road-traffic accident locations. 
Isolated cells and the border of municipality may induce problems for linear regressions, as could be noticed in the standard deviation maps (Fig. 4 and 7). Also, since no lower outliers are present in any of these maps, it seems that the multivariate linear regression tends to rather overestimate the distance to the nearest accident, as does the multivariate spatial regression. This is also clearly shown in the quantile maps (Fig. 5 and 8), which depict some neighborhoods using cold colors even though having accidents within their cell boundaries. 

Conclusions

The analysis that was performed on the municipality of Vernier showed that, when spatiality is taken into account, the distance to road-traffic accidents can be correlated to the wealth of the neighborhood. Indeed, the wealthier the region, the further away the closest accident will be. This confirms the results found in literature. 
Even if corroborating what has been affirmed in other studies , the results found in this paper should be considered carefully. The best determination coefficient found, using multivariate spatial regression, is still quite low and indicates that the precision of the model, which allows to estimate the distance to the nearest accident thanks to the allowances people get at some locations, is still poor. 
The spatial model may be this mediocre because of the limited amount of data that was used, and the restricted zone that was under analysis. The high variability in the population of the municipality of Vernier may also explain these discrepancies. Futures studies might does want to focus on somewhat larger areas.

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 DOI of the datasets used:  10.5072/zenodo.147367  
DOI of the article: 10.22541/au.151173685.50941386