## 1 Introduction
Datasets exist globally at a coarse spatial (\(\sim\)3 km) and spectral resolution, allowing us to understand the broad variations in urban lighting [37] but sub-city patterning cannot be explored effectively [38; 39]; however, important correlations can be found between population, road density and lengths, and people's incomes. Numerous satellite images are available from the International Space Station with a spatial resolution of up to 60 m. Although these images are starting to be used to detect demographic patterns within urban areas, and in principle, such a mosaic image could also be used to automatically identify and classify the different lights of the city, in practice, individual lamps still cannot be identified [40] and there are some difficulties associated with imaging light pollution: large dynamic range, rapid motion of the sensors, need to sample a large area in a short period of time (requiring a large field of view, FOV). Finer spatial resolution data do exist, but typically have a limited spatial extent [41] along a landscape modification gradient where the three following phenomena have been recognized [42]: (1) patch density increases exponentially; (2) the regularity of patch shape increases; (3) landscape connectivity decreases [43; 44]. These patterns have been constantly supported for urbanizing landscapes throughout the world, however this hinders the development of a robust evidence base to sustain urban lighting policies, as cities can be highly heterogeneous even at fine spatial scales [44]. For example, little is known about how lighting varies with urban land use [45] or alongside built density gradients. Upgraded baseline urban lighting maps are similarly required in order to apply the outcomes of published lighting research [46], to implement existing planning guidance on urban lighting zones, [47], to implement planning agreements and legislation standards related to lighting nuisance [48] and to monitor changes over time. Consequently, we need confident lighting datasets at the city scale; and with a spatial and spectral resolution that should be sufficient to advance lighting research and the development of planning and governance of urban lighting. The aim of the current research work is to remedy to the current lack of lighting data in urban areas with a proper spatial resolution. Lighting data are missing because measurement campaigns are generally expensive, and this is because they involve several technicians moving around different area of cities (and in particular the most unsafe parts of them) during the night with proper measurement devices and the logging of this data in databases. The proposed approach tries to automate this process thanks to the use of a geographic information system (GIS)-based informative system, integrated with lighting data generated by mobile Internet of Things (IoT) sensors The data generated by the IoT sensors can be automatically inserted in a GIS system. Such an approach allows data to be correlated about lighting quality with respect to the perceived level of security and with quantitative data about car accidents and security events. The integration of IoT sensors with the remote GIS system is performed using state of the art IoT protocols [49]. At the present, a measurement prototype based on the Arduino microcontroller has been designed. The IoT sensor is mounted on a mobile mechanical structure which allows easy movement of the sensors in urban areas. The IoT sensor is equipped with off-the-shelf light sensors based on a photodetector. The use of an appropriate sensor calibrated using a V-lambda filter and using cosine correction response input optics, as required by the international norm CIE-194:2011 [50], will provide more accurate light measurement but is out of the scope of the current research work. In fact, as mentioned above, the main contribution of the current research lies in the definition of a GIS-based methodology to analyze and correlate data, more than in the proposal of a new measurement of light sensors.