The University of Southampton
University of Southampton Institutional Repository

Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano

Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano
Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano
Population data is commonly available for administrative units referring to the year of the last census. That level of aggregation and the static character of the information pose particular difficulties for spatial analysis in applications such as disaster management or spatial planning, for which much more time-sensitive population distributions are required. In this study, a flexible model to create dynamic gridded population data with a spatial resolution of 100 m is implemented for the mountainous, hazard-prone and highly touristic region of the Autonomous Province of Bolzano, based on the integration of multiple data sources within an explicit spatio-temporal modelling framework. It is argued that dynamic gridded population information provides an improvement to the existing regional datasets. Our study shows that integrating daily and seasonal changes to the distribution of population improves exposure information for risk assessments especially in highly touristic areas.
2212-4209
470-479
Renner, Kathrin
574fb2ab-3cde-45ab-9fb7-03bdc059dc71
Schneiderbauer, Stefan
28fb2f14-9ce9-413b-aa76-e32ce95fa267
Pruß, Fabio
085319a3-382e-4870-900c-b2d6999b4896
Kofler, Christian
600e8b2c-ff0f-43a0-9f1f-25cf0c9e8523
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Renner, Kathrin
574fb2ab-3cde-45ab-9fb7-03bdc059dc71
Schneiderbauer, Stefan
28fb2f14-9ce9-413b-aa76-e32ce95fa267
Pruß, Fabio
085319a3-382e-4870-900c-b2d6999b4896
Kofler, Christian
600e8b2c-ff0f-43a0-9f1f-25cf0c9e8523
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800

Renner, Kathrin, Schneiderbauer, Stefan, Pruß, Fabio, Kofler, Christian, Martin, David and Cockings, Samantha (2018) Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano. International Journal of Disaster Risk Reduction, 27, 470-479. (doi:10.1016/j.ijdrr.2017.11.011).

Record type: Article

Abstract

Population data is commonly available for administrative units referring to the year of the last census. That level of aggregation and the static character of the information pose particular difficulties for spatial analysis in applications such as disaster management or spatial planning, for which much more time-sensitive population distributions are required. In this study, a flexible model to create dynamic gridded population data with a spatial resolution of 100 m is implemented for the mountainous, hazard-prone and highly touristic region of the Autonomous Province of Bolzano, based on the integration of multiple data sources within an explicit spatio-temporal modelling framework. It is argued that dynamic gridded population information provides an improvement to the existing regional datasets. Our study shows that integrating daily and seasonal changes to the distribution of population improves exposure information for risk assessments especially in highly touristic areas.

Text 1-s2.0-S2212420917303345-main - Accepted Manuscript
Restricted to Repository staff only until 27 November 2018.
Request a copy

More information

Accepted/In Press date: 18 November 2017
e-pub ahead of print date: 27 November 2017
Published date: March 2018

Identifiers

Local EPrints ID: 416045
URI: https://eprints.soton.ac.uk/id/eprint/416045
ISSN: 2212-4209
PURE UUID: 0497dde8-5a33-44d5-b558-f7838cb09330
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 30 Nov 2017 17:30
Last modified: 01 Mar 2018 17:31

Export record

Altmetrics

Contributors

Author: Kathrin Renner
Author: Stefan Schneiderbauer
Author: Fabio Pruß
Author: Christian Kofler
Author: David Martin ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×