For the analysis of the hotspots, the representation of the raw data and the smoothed data are already a solid base. The percentages of the foreigners living in the cells are already spatially correlated, before smoothing with SRS. Two hotspots with a share of more than 50% foreigners per cell can be detected, both with the raw and the smoothed data, as seen in Figure 4 (ONLY SMOOTHED DATA IN FIG 4!!!). After applying the SRS, the two hotspots are clearly separated from the other cells. This clustering of the nationalities in urban regions has already been investigated in other reports and this fact also adapts to Swiss cities \cite{Adelman_2016}
For the interventions divided by the population some hotspots are established, although not as clear as for the percentage of foreigners. On the other hand, the raw data would not show clusters in any quantile. For this reason, the hotspots of the smoothed data need to be handled with a critical mind. The literature also states a clustering of social interventions(SOURCE?). For this purpose, our study area is certainly not large enough. Another aspect is the period of time, the data of the interventions is representing. A time series over a longer time period could improve the result.
The boxplots in Figure 5 and 6 do not clearly show a correlation between the variables, neither for the raw data nor for the smoothed. Also when playing with the selection of the cells, no clear correlation can be stated. When normalizing the data, the slopes do not show the expected slope. In addition, the Rdo never exceed 0.1, which makes the regression not reliable, with a huge error that can not be explained by the regression curve. Thus, with a statistical analysis of the connection between interventions and percentage of foreigners no clear correlation can be detected.
For the further investigation, a spatial analysis was adapted using the Local Morans I. When comparing the clustering of the Local Morans I in Figure 7, clear tendencies exist in building two hotspots for the High-High correlation and also one for the Low-Low connection. Also in this case the smoothing effect of the SRS method must be taken into account, which helps to build clusters from the variables. With only 28% of the area, resulting in 113 cells showing a significant relation between the two variables, the effect of the variables on each other are not that clear. The clusters of the High-High connection are in those areas, where it was to be expected according to the hotspots of the smoothed data in Figure 4. The spatial distribution of interventions and foreigner population would be expected as more correlated, according to secondary literature(SOURCE). The same reasons as for the hotspots can be held responsible, as the area is not large enough and the data not that comprising \cite{switzerland}. Also, the interventions due to social reasons do not necessarily correlate with crimes in general. 
The two important living areas in Vernier, Lignon and Avanchets, do not specially appear in neither of the analysis. The reason could be, that the interventions are divided by the population living in the cell. But also in the analysis of the percentage of foreigners per cell are the areas not showing special properties regarding the variable. The region of Lignon has become a popular living area and therefore, the prices do not specially attract foreigners or other social classes. 
When looking at the results overall, it can clearly be said, that the correlation between the percentage of foreigners living in an area and the social interventions by the police is rather weak. Other properties of the society such as education, wealth, stress etc. must have a more important influence in this region and could be investigated in a further study with more input data regarding those topics.