Articles | Volume 20, issue 12
https://doi.org/10.5194/nhess-20-3485-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-3485-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Dec 2020
Research article |
| 17 Dec 2020
| 17 Dec 2020
Dynamic maps of human exposure to floods based on mobile phone data
Matteo Balistrocchi
CORRESPONDING AUTHOR
Department of Engineering Enzo Ferrari, University of Modena and
Reggio Emilia, Modena (MO), 41125, Italy
Rodolfo Metulini
Department of Economics and Statistics, University of Salerno,
Fisciano (SA), 84084, Italy
Maurizio Carpita
Department of Economics and Management, University of Brescia, Brescia
(BS), 25122, Italy
Roberto Ranzi
Department of Civil, Environmental, Architectural Engineering and
Mathematics, University of Brescia, Brescia (BS), 25123, Italy
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Cited
18 citations as recorded by crossref.
- Post‐event flood mapping for road networks using taxiGPSdata X. Kong et al. 10.1111/jfr3.12799
- A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data X. Kong et al. 10.5194/hess-27-3803-2023
- Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China X. Guo et al. 10.1080/19475705.2023.2221772
- Mobile phone data for anticipating displacements: practices, opportunities, and challenges B. Aydoğdu et al. 10.1017/dap.2024.94
- Public risk perception in extreme weather events: topic distribution, spatiotemporal analysis, and sentiment comparison on social media R. Zhang & A. Chen 10.1016/j.ijdrr.2025.105692
- Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China X. Guo et al. 10.3390/ijerph191710697
- Human mobility amplifies compound flood risks in coastal urban areas under climate change Z. Long & H. Duan 10.1038/s43247-025-02406-x
- Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making R. Metulini & M. Carpita 10.1007/s10479-023-05195-8
- Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area S. Perazzini et al. 10.1016/j.seps.2023.101747
- Probabilistic mapping of life loss due to dam-break flooding A. Maranzoni et al. 10.1007/s11069-023-06285-3
- Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities S. Cumbane & G. Gidófalvi 10.3390/ijgi10060421
- Postearthquake situational awareness based on mobile phone signaling data: An example from the 2017 Jiuzhaigou earthquake K. Dai et al. 10.1016/j.ijdrr.2021.102736
- An innovative framework to assess the human-water relationship in complex pluvial flooding system at urban meso-scales C. Ye et al. 10.1016/j.jhydrol.2025.132876
- Economic Management Data Envelopes Based on the Clustering of Incomplete Data S. Dong et al. 10.1155/2021/4312842
- Statistical indicators based on mobile phone and street maps data for risk management in small urban areas S. Perazzini et al. 10.1007/s10260-023-00719-9
- Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China T. Wang et al. 10.1016/j.jenvman.2023.118787
- Traffic flows time series in a flood-prone area: modeling and clustering on extreme values with a spatial constraint M. Carpita et al. 10.1007/s00477-024-02735-x
- Rainstorm Flood Risk Assessment of Urban Metro System in Different Operating Periods: A Case Study of the Central Urban Area of Tianjin, China X. Hou et al. 10.1061/NHREFO.NHENG-2102
18 citations as recorded by crossref.
- Post‐event flood mapping for road networks using taxiGPSdata X. Kong et al. 10.1111/jfr3.12799
- A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data X. Kong et al. 10.5194/hess-27-3803-2023
- Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China X. Guo et al. 10.1080/19475705.2023.2221772
- Mobile phone data for anticipating displacements: practices, opportunities, and challenges B. Aydoğdu et al. 10.1017/dap.2024.94
- Public risk perception in extreme weather events: topic distribution, spatiotemporal analysis, and sentiment comparison on social media R. Zhang & A. Chen 10.1016/j.ijdrr.2025.105692
- Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China X. Guo et al. 10.3390/ijerph191710697
- Human mobility amplifies compound flood risks in coastal urban areas under climate change Z. Long & H. Duan 10.1038/s43247-025-02406-x
- Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making R. Metulini & M. Carpita 10.1007/s10479-023-05195-8
- Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area S. Perazzini et al. 10.1016/j.seps.2023.101747
- Probabilistic mapping of life loss due to dam-break flooding A. Maranzoni et al. 10.1007/s11069-023-06285-3
- Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities S. Cumbane & G. Gidófalvi 10.3390/ijgi10060421
- Postearthquake situational awareness based on mobile phone signaling data: An example from the 2017 Jiuzhaigou earthquake K. Dai et al. 10.1016/j.ijdrr.2021.102736
- An innovative framework to assess the human-water relationship in complex pluvial flooding system at urban meso-scales C. Ye et al. 10.1016/j.jhydrol.2025.132876
- Economic Management Data Envelopes Based on the Clustering of Incomplete Data S. Dong et al. 10.1155/2021/4312842
- Statistical indicators based on mobile phone and street maps data for risk management in small urban areas S. Perazzini et al. 10.1007/s10260-023-00719-9
- Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China T. Wang et al. 10.1016/j.jenvman.2023.118787
- Traffic flows time series in a flood-prone area: modeling and clustering on extreme values with a spatial constraint M. Carpita et al. 10.1007/s00477-024-02735-x
- Rainstorm Flood Risk Assessment of Urban Metro System in Different Operating Periods: A Case Study of the Central Urban Area of Tianjin, China X. Hou et al. 10.1061/NHREFO.NHENG-2102
Latest update: 13 Jul 2025
Short summary
Flood risk is an increasing threat to urban communities and their strategical assets worldwide. Non-structural practices, such as emergency management plans, can be effective in order to decrease the flood risk in strongly urbanized areas. Mobile phone data provide reliable estimates of the spatiotemporal variability in people exposed to flooding, thus enhancing the preparedness of stakeholders involved in flood risk management. Further, practical advantages emerge with respect to crowdsourcing.
Flood risk is an increasing threat to urban communities and their strategical assets worldwide....
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