Identifying Residential Areas Based on Open Source Data: A Multi-Criteria Holistic Indicator to Optimize Resource Allocation During a Pandemic

COVID-19 has changed the very way we live our lives, from how we learn and work to how we interact. It has also brought a number of challenges including the management of building utilities under such conditions. In fact, during a lockdown, it makes sense to allocate the available resources based on the density of population, e.g., preferring residential areas over commercial or financial districts. The identification of residential areas is relevant also to prioritize emergency activities in the presence of major natural disaster and it can be the basis to identify which area should be supplied of electricity and gas in case of scarcity of energetic resources. However, given the complexity of the urban landscape, pinpointing residential areas might be difficult. In this paper, based on open source intelligence and multi-criteria decision-making, we aim to develop a holistic indicator to quantify the likelihood that a zone is residential, in order to orient the optimization of the distribution of resources such as power, gas or water. In order to show the effectiveness of the proposed approach, the paper is complemented by a case study set in Nicosia, Cyprus.