Dynamic maps of human exposure to floods based on mobile phone data
Autor: | Matteo Balistrocchi, Roberto Ranzi, Maurizio Carpita, Rodolfo Metulini |
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Rok vydání: | 2020 |
Předmět: |
0208 environmental biotechnology
Vulnerability 02 engineering and technology Land cover Crowdsourcing 01 natural sciences lcsh:TD1-1066 010104 statistics & probability lcsh:Environmental technology. Sanitary engineering 0101 mathematics lcsh:Environmental sciences lcsh:GE1-350 Emergency management Flood myth business.industry lcsh:QE1-996.5 Environmental resource management lcsh:Geography. Anthropology. Recreation Crowding Hazard 020801 environmental engineering lcsh:Geology Geography lcsh:G Mobile phone General Earth and Planetary Sciences business Settore SECS-S/02 - Statistica per La Ricerca Sperimentale e Tecnologica |
Zdroj: | Natural Hazards and Earth System Sciences, Vol 20, Pp 3485-3500 (2020) |
ISSN: | 1684-9981 |
Popis: | Floods are acknowledged as one of the most serious threats to people's lives and properties worldwide. To mitigate the flood risk, it is possible to act separately on its components: hazard, vulnerability, exposure. Emergency management plans can actually provide effective non-structural practices to decrease both human exposure and vulnerability. Crowding maps depending on characteristic time patterns, herein referred to as dynamic exposure maps, represent a valuable tool to enhance the flood risk management plans. In this paper, the suitability of mobile phone data to derive crowding maps is discussed. A test case is provided by a strongly urbanized area subject to frequent flooding located on the western outskirts of Brescia (northern Italy). Characteristic exposure spatiotemporal patterns and their uncertainties were detected with regard to land cover and calendar period. This novel methodology still deserves verification during real-world flood episodes, even though it appears to be more reliable than crowdsourcing strategies, and seems to have potential to better address real-time rescues and relief supplies. |
Databáze: | OpenAIRE |
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