Discussion

Both hypotheses were proved considering the smoothed data of the SRS analysis. Nevertheless, it is important to point out, that the smoothing effect of the SRS is quite high and a loss in precision of the data can not be excluded. Therefore, the new information varies from the provided data and the statistical check is less high than with the raw data. The strong smoothing effect has its source in the Queen's 2 contiguity. We have chosen the creation of the weight file like that, so a clear contrast between the raw data and the smoothed one is visible. Like this, we were able to point out the effect of spatial statistics and analysis compared to usual statistics. With a Queen's file of a lower order, a compromise between the smoothing and loss of data could be achieved. 
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 5. 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{Adelman2016}.
For the interventions divided by the population, some hotspots are established, although not as clear as for the percentage of foreigners as to be seen in Figure 4. On the other hand, the raw data would not show clusters in any quantile, which warns us, that the smoothed data should be handled with a critical mind. The literature also states a clustering of social interventions, although the exact distinction between common crime due to social problems is not clear \cite{Massey_1991}. To further proof this fact, our study area is certainly not large enough. Another aspect is the restricted period of time that the data of the interventions is representing. A time series over a longer time period could improve the result.
The boxplots in Figure 6 and 7 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 of 1, but much smaller values. 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 Moran's I. When comparing the clustering of the Local Moran's I in Figure 8, 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 rather weak. The clusters of the High-High connection lie in those areas where it was to be expected according to the hotspots of the smoothed data in Figure 5. The spatial distribution of interventions and foreigner population would be expected as more correlated, according to general speaking \cite{2017}. Although scientifically seen, this connection is not clearly proofed \cite{Entorf2000} and Adelman et al. even states the opposite\cite{Adelman2016}. The reason for the thrown hypothesis in this case can also be the same as for the hotspots, as the area is not large enough and the data not that comprising. 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, the areas are 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 social properties, 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.