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

  • Authors:

  • Terhi Ahola;Kirsi Virrantaus;Jukka Matthias Krisp;Gary J. Hunter

  • Affiliations:

  • Helsinki University of Technology, Department of Surveying, Laboratory of Geoinformation and Positioning Technology, FIN-02015 HUT, Finland;Helsinki University of Technology, Department of Surveying, Laboratory of Geoinformation and Positioning Technology, FIN-02015 HUT, Finland;Helsinki University of Technology, Department of Surveying, Laboratory of Geoinformation and Positioning Technology, FIN-02015 HUT, Finland;University of Melbourne, Department of Geomatics, Parkville, VIC 3010, Australia

  • Venue:
  • International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
  • Year:
  • 2007

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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.