Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.102 IF 3.102
  • IF 5-year value: 3.284 IF 5-year
  • CiteScore value: 5.1 CiteScore
  • SNIP value: 1.37 SNIP 1.37
  • IPP value: 3.21 IPP 3.21
  • SJR value: 1.005 SJR 1.005
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 90 Scimago H
    index 90
  • h5-index value: 42 h5-index 42
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 Jul 2020

02 Jul 2020

Review status
This preprint is currently under review for the journal NHESS.

Dynamic maps of people exposure to floods based on mobile phone data

Matteo Balistrocchi1, Rodolfo Metulini2, Maurizio Carpita3, and Roberto Ranzi1 Matteo Balistrocchi et al.
  • 1Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, Brescia (BS), 25123, Italy
  • 2Department of Economics and Statistics, University of Salerno, Fisciano (SA), 84084, Italy
  • 3Department of Economics and Management, University of Brescia, Brescia (BS), 25122, Italy

Abstract. Floods are acknowledged as one of the most serious threats to human 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 people exposure and vulnerability. Crowding maps depending on characteristic time patterns, herein referred to as dynamic exposure maps, provide 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 floodings located in the western outskirt of Brescia town (northern Italy). Characteristic exposure spatio-temporal patterns and their uncertainties were detected, with regard to land cover and calendar period. This novel methodology appears to be more reliable than crowdsourcing strategies, and has potentials to better address real-time rescues and reliefs supply.

Matteo Balistrocchi et al.

Interactive discussion

Status: open (until 07 Sep 2020)
Status: open (until 07 Sep 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Matteo Balistrocchi et al.

Matteo Balistrocchi et al.


Total article views: 249 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
184 53 12 249 11 12
  • HTML: 184
  • PDF: 53
  • XML: 12
  • Total: 249
  • BibTeX: 11
  • EndNote: 12
Views and downloads (calculated since 02 Jul 2020)
Cumulative views and downloads (calculated since 02 Jul 2020)

Viewed (geographical distribution)

Total article views: 141 (including HTML, PDF, and XML) Thereof 141 with geography defined and 0 with unknown origin.
Country # Views %
  • 1



No saved metrics found.


No discussed metrics found.
Latest update: 14 Aug 2020
Publications Copernicus
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 estimate of the spatio-temporal variability of 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....