## 5 Results
### Urban Illuminance Mapping  
Points have been imported and georeferenced in a GIS (QGIS) and have been used for the development of the data analysis algorithm. For this purpose, a polygon grid 10m \(\times\) 10m has been developed considering an extension equal to the maximum extent of the coordinated points representing the measured light intensity. The first insight of where the measurement characterized by higher light intensity are localized in the selected area is shown in Figure 19. Data have been categorized according to 6 classes of light intensity (shown in the legend of Figure 19), considering equal intervals. The last class shows values characterized by a very high illuminance values despite it is composed by only 2 values, recorded under direct lights (a private parking lot and the very bright sign of a commercial activity).  
Figure 17: Definition and differences between (**a**) illuminance in lux (lm/m\({}^{2}\)) and (**b**) luminance in cd/m\({}^{2}\) (lm/sr \(\times\) m\({}^{2}\)).  
Figure 18: **(a) “Zebra effect” on the pavement (horizontal bands with different lighting levels); (b) safety perception of a pedestrian related to lighting levels and degradation signs on the built environment (lack of maintenance and vandalism on the facades); (c) difference of pavements in the city and increased danger for bikes and motorbikes when raining; (d) shading of the urban lighting provided by the trees distributed in parking areas and urban residual parks decreasing visual capability and safety perception for pedestrians.**  
It is possible to note an unevenness of distribution of the illuminance values: the most illuminated locations are those where interesting activities are spotted (the university main fronts, some leisure locations such as pub and restaurants and the crossing of some bigger urban streets). By contrast, a large part of the measurements fall in the low level of illumination range, between 0 and 539 lx. Thus, unequally spaced intervals are considered (shown in the legend of Figure 20) in order to better analyze the visual effect of brightness and darkness that can be perceived by citizens. The raw light measurement data are spatially merged in a grid of 10 m \(\times\) 10 m squares (an area of 20 m\({}^{2}\) each), in order to reduce the number of points to analyze on the GIS. In addition, as discussed in the previous section, the measurement set-up is affected by a variability contribution which depend by the mechanical system used to support light sensors during the measurement campaign through the city. Although such variability is well below the accuracy required by the analysis performed in this paper, the light data in each square of the grid are arithmetically averaged, to reduce the measurement variability. An SQL (structured query language) script allows to compute the arithmetical average light intensity, maximum and minimum light intensity values and the number of points in each of 10 m \(\times\) 10 m polygonal grid. Figure 20 represents the outcomes of this data analysis. The visualization allows us to identify areas in the test bed neighborhood with different illuminance levels. The lower ranges of luminosity correspond to locations where it could be useful to intensify the illumination level and to provide new systems to increase safety and security in a developing area of the city subjected to regeneration processes.  
Figure 19: Results of the measurement campaign. The dimension of the circles is proportional to light intensity. Six light intervals (equally spaced) are considered in the figure.