Natural Hazards

, Volume 76, Issue 3, pp 1573–1601 | Cite as

Exploring a spatial statistical approach to quantify flood risk perception using cognitive maps

  • Eoin O’Neill
  • Michael Brennan
  • Finbarr Brereton
  • Harutyun Shahumyan
Original Paper


Modern flood risk management strategies have evolved from flood resistance to a holistic approach incorporating prevention, protection and preparedness with the aim of reducing the likelihood and/or impact of flooding. This evolution has been driven by a trend of increasingly damaging and frequent flood events due to climate change. Populations at risk are required to be an active participant within modern flood risk management plans, resulting in management plan effectiveness being partially dependent on the relevant population’s flood risk perception. Thus, understanding how at-risk populations perceive their own flood risk, and how this compares to the reality of the situation, is a significant component of flood risk management. This paper compares subjective risk perception to an objective measure of risk within a specific case study area, where 305 residents were surveyed on their perception of flood risk. As part of the survey, respondents were asked to delineate the areas of the study area that they perceived would be at risk of inundation during a severe flood event. Using spatial statistical indicators, including Fuzzy Kappa comparison, it was possible to quantify the divergence between subjective and objective measures of risk extent, enabling an assessment of the ‘correctness’ of subjective perceived risk. This novel approach identified significant deviations between risk perception and objective risk measures at an individual level. The paper concludes by considering potential policy implications.


Risk perception Flooding Cognitive maps Fuzzy Kappa 



This paper is an output of The FloodPAP Project: an examination of issues relating to Flood-risk Perception, Awareness and Policy. This paper could not have been accomplished without the extensive manual and digital processing input of Richard Geoghegan, Sean Judge and Ilda Dreoni. The authors are very grateful for their assistance. The authors also wish to thank the two anonymous referees for helpful comments provided. Finally, the authors would like to thank the School of Geography, Planning and Environmental Policy, University College Dublin, for providing the funding necessary to initiate this project, and also the Irish Research Council for funding knowledge exchange activities associated with this project.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Eoin O’Neill
    • 1
    • 2
  • Michael Brennan
    • 1
  • Finbarr Brereton
    • 1
    • 2
  • Harutyun Shahumyan
    • 3
  1. 1.School of Geography, Planning and Environmental PolicyUniversity College DublinBelfield, Dublin 4Ireland
  2. 2.UCD Earth InstituteUniversity College DublinBelfield, Dublin 4Ireland
  3. 3.National Center for Smart Growth Research and EducationUniversity of MarylandCollege ParkUSA

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