The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil
| Autor: | Monika Kuffer, Julio Cesar Pedrassoli, Jiong Wang, Patrícia Lustosa Brito, Anderson Dias de Freitas, Federico Costa, Mila Koeva |
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| Rok vydání: | 2020 |
| Předmět: |
Earth observation
Coronavirus disease 2019 (COVID-19) Geography Planning and Development Population 0211 other engineering and technologies lcsh:G1-922 02 engineering and technology computer.software_genre urban health 03 medical and health sciences 0302 clinical medicine urban remote sensing Earth and Planetary Sciences (miscellaneous) 030212 general & internal medicine Computers in Earth Sciences Dimension (data warehouse) education Environmental planning education.field_of_study Data collection pandemic 021107 urban & regional planning informal settlements deprived areas Geography Work (electrical) ITC-ISI-JOURNAL-ARTICLE ITC-GOLD computer lcsh:Geography (General) Slum Data integration |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 9, Iss 557, p 557 (2020) |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi9090557 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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