Abstract:The way in which risk maps are used in decision support processes for spatial planning at local scale is critical to helping decision makers in the definition of a prevention strategy to minimize risks. This paper presents a spatial decision support system that is developed to assist spatial planning by integrating the risk management component at municipal level. For the development of the RiskOTe tool, a semi-quantitative risk assessment model was used that assumes risk management to be comprehensive with respect to the type of hazard, vulnerability and risk minimization measures. In this paper, the components for the development of the spatial decision support system are identified, described and implemented using the municipality of Oeiras, in Portugal, as case study. The use of the system allowed the generation of multiple scenarios and outcomes. The results demonstrate that decision-making on the transformation of land uses by integrating risk analysis can be supported on a solid basis of information obtained from a spatial decision support system.
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.
Due to its novelty, the recent pandemic of the coronavirus disease (COVID-19), which is associated with the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2), triggered the public’s interest in accessing information, demonstrating the importance of obtaining and analyzing credible and updated information from an epidemiological surveillance context. For this purpose, health authorities, international organizations, and university institutions have published online various graphic and cartographic representations of the evolution of the pandemic with daily updates that allow the almost real-time monitoring of the evolutionary behavior of the spread, lethality, and territorial distribution of the disease. The purpose of this article is to describe the technical solution and the main results associated with the publication of the COMPRIME_COMPRI_MOv dashboard for the dissemination of information and multi-scale knowledge of COVID-19. Under two rapidly implementing research projects for innovative solutions to respond to the COVID-19 pandemic, promoted in Portugal by the FCT (Foundation for Science and Technology), a website was created. That website brings together a diverse set of variables and indicators in a dynamic and interactive way that reflects the evolutionary behavior of the pandemic from a multi-scale perspective, in Portugal, constituting itself as a system for monitoring the evolution of the pandemic. In the current situation, this type of exploratory solutions proves to be crucial to guarantee everyone’s access to information while simultaneously emerging as an epidemiological surveillance tool that is capable of assisting decision-making by public authorities with competence in defining control policies and fight the spread of the new coronavirus.
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