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A spatio-temporal population model to support risk assessment and damage analysis for decision-making 

Authors: Terhi Ahola a;  Kirsi Virrantaus a;  Jukka Matthias Krisp a; Gary J. Hunter b
Affiliations:   a Helsinki University of Technology, Department of Surveying, Laboratory of Geoinformation and Positioning Technology, FIN-02015 HUT, Finland
b University of Melbourne, Department of Geomatics, Parkville, VIC 3010, Australia
DOI: 10.1080/13658810701349078
Publication Frequency: 12 issues per year
Published in: journal International Journal of Geographical Information Science, Volume 21, Issue 8 January 2007 , pages 935 - 953
Formats available: HTML (English) : PDF (English)
Previously published as: International journal of geographical information systems (0269-3798, 1362-3087) until 1996
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Abstract

The aim of this research is to develop and implement a simple spatio-temporal model of population location that might improve risk assessment and damage analysis for decision-making in both the Finnish Fire and Rescue Services and the Finnish Defence Forces. The motivation for the research is that present risk models do not take into account the temporal variation in population location during different times of the day. We use spatio-temporal modelling methods to model the population dynamics, and visualization techniques to represent the model outcomes. In addition, we apply the developed model to a damage-analysis application. The case study site is located in the centre of Helsinki. The model uses a basic population and workplace dataset maintained by the Helsinki Metropolitan Area Council. By means of this model, we intend to advance risk assessment, which considers the consequences of accidents. This model has the potential to help decision-makers evaluate their plans in several application areas—such as achieving better preparedness by having more reliable evacuation plans and resource allocation. In addition to the application-related technological research, a more generic framework about decision-making supported by spatio-temporal knowledge and visualization is presented.
Keywords: Spatio-temporal population modelling; Spatio-temporal knowledge; Risk assessment; Damage analysis; Preparedness planning; Decision-making
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