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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Main Authors: M. Balistrocchi, R. Metulini, M. Carpita, R. Ranzi
Format: Article
Language:English
Published: Copernicus Publications 2020-12-01
Series:Natural Hazards and Earth System Sciences
Online Access:https://nhess.copernicus.org/articles/20/3485/2020/nhess-20-3485-2020.pdf
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spelling 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
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