Abstract
The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.
Highlights
COVID-19 was declared, by March 11, 2020, a global pandemic [1]
Infection rates are increasing in many low- and middle-income countries (LMICs), as well as mortality rates, which are high in deprived communities [2,3], pointing to high vulnerabilities of the urban poor in LMICs
It is worth mentioning that the most severe impacts of the epidemic occur in other groups such as indigenous people, and these should be included in the risk group designation [5,6,7,8]
Summary
COVID-19 was declared, by March 11, 2020, a global pandemic [1]. Recently, infection rates are increasing in many low- and middle-income countries (LMICs), as well as mortality rates, which are high in deprived communities [2,3], pointing to high vulnerabilities of the urban poor in LMICs. It is worth mentioning that the most severe impacts of the epidemic occur in other groups such as indigenous people, and these should be included in the risk group designation [5,6,7,8]. These indigenous groups, located in- or outside of cities, are often recognized as hard-to-access communities and prone to underestimation of the actual situation. Spatial tools are highly demanded for the identification of hotspots and applying spatial measurements [9]
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