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
    3.284
  • CiteScore value: 5.1 CiteScore
    5.1
  • 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
Preprints
https://doi.org/10.5194/nhess-2020-213
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-213
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2020

10 Jul 2020

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

A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest

Sebastian Scheuer1, Dagmar Haase1,3, Annegret Haase2, Manuel Wolff1, and Thilo Wellmann1,3 Sebastian Scheuer et al.
  • 1Landscape Ecology Lab, Geography Department, Humboldt-Universität zu Berlin, Berlin, 10099, Germany
  • 2Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, 04318, Germany
  • 3Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318 Leipzig, Germany

Abstract. Disaster risk is conceived as the interaction of hazard, exposure, and vulnerability. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the residence of urban dwellers. This case study for the city of Leipzig, Germany, proposes an indirect, machine learning-based approach for the prediction of residential choice behaviour to explore how exposure and vulnerabilities are shaped by the residential location choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.

Sebastian Scheuer et al.

Interactive discussion

Status: open (until 13 Sep 2020)
Status: open (until 13 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

Sebastian Scheuer et al.

Sebastian Scheuer et al.

Viewed

Total article views: 84 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
52 23 9 84 10 13
  • HTML: 52
  • PDF: 23
  • XML: 9
  • Total: 84
  • BibTeX: 10
  • EndNote: 13
Views and downloads (calculated since 10 Jul 2020)
Cumulative views and downloads (calculated since 10 Jul 2020)

Viewed (geographical distribution)

Total article views: 192 (including HTML, PDF, and XML) Thereof 192 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 14 Aug 2020
Publications Copernicus
Download
Short summary
The choice of residential location is one of the drivers shaping risks in cities. We model likely outcomes of this decision-making process for distinct socioeconomic groups in the city of Leipzig, Germany, using random forests and geostatistical methods. In so doing, we uncover hot spots and cold spots that may indicate spatial patterns and trends in exposure and vulnerabilities of urban population, to shed a light on how residential location choice affects these risk components as a process.
The choice of residential location is one of the drivers shaping risks in cities. We model...
Citation