At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socioeconomic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.

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Urban Heat Island (UHI) phenomenon concerns the development of higher ambient temperatures in urban districts compared to the surrounding rural areas. Several studies investigated the influence of individual parameters in the UHI phenomenon, on the other hand, an exhaustive study that quantifies the influence of each parameter in the resulting UHI is missing in the related literature. This paper proposes a new index aimed at quantifying the hazard of the absolute maximum UHI intensity in urban districts during the Summer season by taking all the parameters influencing the phenomenon into account. In addition, for the first time, the influence of each parameter has been quantified. City albedo and the presence of greenery represent the most important characteristics with an influence of 29% and 21%. Population density, width of streets, canyon orientation and building height has a medium influence of 12%, 10%, 9% and 8% respectively. The remaining parameters have an overall influence of 11%. These results are achieved by exploiting three synergistically related techniques: the Analytic Hierarchy Processes to analyse the parameters involved in the UHI phenomenon; a state-of-the-art technique to acquire a large set of data; and an optimization procedure involving a involving a Jackknife resampling approach to calibrate the index by exploiting the effective UHI intensity measured in a total of 41 urban districts and 35 European Cities.