Dynamic maps of human exposure to floods based on mobile phone data
<p>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-structur...
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2020-12-01
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doaj-f3478119237f4c73b4c48b661646fb1c2020-12-17T08:07:44ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812020-12-01203485350010.5194/nhess-20-3485-2020Dynamic maps of human exposure to floods based on mobile phone dataM. Balistrocchi0R. Metulini1M. Carpita2R. Ranzi3Department of Engineering Enzo Ferrari, University of Modena and Reggio Emilia, Modena (MO), 41125, ItalyDepartment of Economics and Statistics, University of Salerno, Fisciano (SA), 84084, ItalyDepartment of Economics and Management, University of Brescia, Brescia (BS), 25122, ItalyDepartment of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, Brescia (BS), 25123, Italy<p>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.</p>https://nhess.copernicus.org/articles/20/3485/2020/nhess-20-3485-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Balistrocchi R. Metulini M. Carpita R. Ranzi |
spellingShingle |
M. Balistrocchi R. Metulini M. Carpita R. Ranzi Dynamic maps of human exposure to floods based on mobile phone data Natural Hazards and Earth System Sciences |
author_facet |
M. Balistrocchi R. Metulini M. Carpita R. Ranzi |
author_sort |
M. Balistrocchi |
title |
Dynamic maps of human exposure to floods based on mobile phone data |
title_short |
Dynamic maps of human exposure to floods based on mobile phone data |
title_full |
Dynamic maps of human exposure to floods based on mobile phone data |
title_fullStr |
Dynamic maps of human exposure to floods based on mobile phone data |
title_full_unstemmed |
Dynamic maps of human exposure to floods based on mobile phone data |
title_sort |
dynamic maps of human exposure to floods based on mobile phone data |
publisher |
Copernicus Publications |
series |
Natural Hazards and Earth System Sciences |
issn |
1561-8633 1684-9981 |
publishDate |
2020-12-01 |
description |
<p>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.</p> |
url |
https://nhess.copernicus.org/articles/20/3485/2020/nhess-20-3485-2020.pdf |
work_keys_str_mv |
AT mbalistrocchi dynamicmapsofhumanexposuretofloodsbasedonmobilephonedata AT rmetulini dynamicmapsofhumanexposuretofloodsbasedonmobilephonedata AT mcarpita dynamicmapsofhumanexposuretofloodsbasedonmobilephonedata AT rranzi dynamicmapsofhumanexposuretofloodsbasedonmobilephonedata |
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