The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

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.

An evaluative model for assessing pandemic resilience at the neighborhood level: The case of Tehran

“… References Indicators Sub- dimensions Dimensions ( Sharifi & Khavarian-Garmsir, 2020 ; Wilkinson, 2020 ) Quality of residential area Built environment characteristics Physical Dimension ( Wilkinson, 2020 ) Average housing area in neighborhoods ( Wilkinson, 2020 ) Building density ( Brito et al., 2020 ; Franch-Pardo et al., 2020 ; Lai et al., 2020 ; Liu et al., 2020 ; Mollalo et al., 2019 ; Sangiorgio & Parisi, 2020 ) Land use mix Land use ( Franch-Pardo et al., 2020 ; Ren et al., 2020 ) Number of neighborhood centers (Supermarkets, Bakery, Grocery, and ….) ( Franch-Pardo et al., 2020 ; Pourghasemi et al., 2020 ; Ren et al., 2020 ) Number of Banks ( Franch-Pardo et al., 2020 ; Ren et al., 2020 ) Number of Chain stores ( Franch-Pardo et al., 2020 ; Liu et al., 2020 ; Sharifi & Khavarian-Garmsir, 2020 ) The ratio of non-built-up areas ( Franch-Pardo et al., 2020 ; Ren et al., 2020 ; Sangiorgio & Parisi, 2020 ) The ratio of the areas of educational, cultural and religious centers ( Franch-Pardo et al., 2020 ; Lak et al., 2020 ; Sangiorgio & Parisi, 2020 ) Number of Drugstores ...…”

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

“…The implementation of PPGIS in Greece (Antoniou, Vassilakis, & Hatzaki, 2020 ) and India (Debnath & Bardhan, 2020 ) during the spring of 2020 was motivated by the need to rapidly acquire data based on location. These studies find that crowdsourcing applications are important tools for real‐time mapping and monitoring to allow health authorities to make decisions and design effective management approaches (Antoniou et al., 2020 ; Brito et al., 2020 ; Desjardins, 2020 ).…”

Spread of COVID-19 in urban neighbourhoods and slums of the developing world

“…Second, we seek to create a network model of infectious spread through an urban system (city) to provide a candidate explanation of the empirically observed variation in caseloads across slums and non-slums. While there has been an emerging body of field-based studies and earth observations on COVID-19 in cities [7][8][9][10], our network modelling approach offers a new and different lens through which to explore the fine-grained spread of infection in urban neighbourhoods. We discuss the results obtained in the context of cities in the developing world.…”