## 6 Discussion  
Several metrics can be used to infer quantitatively the perceived level of security of citizens in the different area of a city: crimes per area, road accidents, personal assault, and many more. There is evidence from the literature that such metrics are directly affected by the quality of street lights: poor lighting is correlated with an increase of the perceived level of insecurity. However, it is not possible to increase indiscriminately the level of lighting in any area of the city, both for economical as well as environmental reasons (energy consumption and light pollution). Thus, it is important to have an accurate map of the quality of lighting in the different areas of the city. Such data should be correlated with other metrics to identify the area which requires an improvement in quality light. Current research work focuses on a methodology able to generate an accurate mapping of lighting quality in urban area. These data are correlated with road accidents information to demonstrate the feasibility of the proposed approach.  
The survey campaign for quality light monitoring has been carried out in the most accurate way possible, despite some drawbacks that can be highlighted. Due to the experimental nature of the proposed procedure, the orthogonality of the sensor to the ground could be improved, ensuring that the detection tools are always horizontal. Moreover, the variation of height from the ground could have been affected for the same reasons, despite always carefully remained within a reasonable range. These issues may cause a bias in the detection of the light intensity data. However, the final evaluations are carried out on average values computed on the 10 m \(\times\) 10 m grid, therefore the single values are not considered, but in intermediate steps of the proposed approach.  
A further improvement of the research concerns the accuracy in the detection of the position of the sensor when recording the light intensity data. For the case study presented in this article a grid 10 m \(\times\) 10 m has been adopted. This allows us to represent the light intensity along the streets and the open spaces of the neighborhood, although not providing enough detail needed to represent and analyses specific technical units of the urban environment (e.g., carriageway, pavement, parking lots  
Figure 20: Measured illuminance (average lux), aggregated within a grid of squares of 10 m \(\times\) 10 m each.  
etc.). This aspect will be addressed and some research insights will be carried out in order to be able to reach a finer granularity in the analysis and visualization of the geolocated data. An oversampling of the data from ambient length sensors could be a possible solution.  
The proposed method has been developed following the guidelines of the EN 13201-4 [82], in particular, the following steps of the research on the orthogonality of the light detecting sensor will be improved as well as the continuity in the height at which the measures are taken. However, the standard should be used when the purpose of characterizing lighting installations considers the photometric quality parameters, an objective that is not completely within the scope of this research, oriented towards the assessment of the security of urban space related to the light intensity.  
Another issue that may have affected the detection of the light data concerns the setup of the Arduino equipped with the sensors. The breadboard has been inserted in a white paperboard box. The light reflecting on the paperboard may have affected the detection of the light intensity, decreasing the accuracy of the values. This issue could be overcome adopting an improved installation setup, or simply employing a black paperboard box.