Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Known as, and referred to locally and most frequently in the Irish media as, Hurricane Charlie rather than the official designated name of Hurricane Charley assigned by the World Meteorological Organisation.

  2. Various events and activities have influenced flood awareness over the intervening period, including: a High Court case which found that the ‘natural’ flood was overwhelmingly the significant cause of the flooding (Superquinn Ltd versus Bray UDC 1998); a major €2billion planned development (yet to proceed) incorporating floodplain lands, currently a golf course; consultation about the flood relief scheme; flood warnings and precautionary evacuation (e.g. September 2008); preparation of an emergency flood plan and subsequent ‘dry run’ flood drill evacuation (April 2009); the emergence of a community flood action group.

  3. The scheme involves, in summary, riverworks within the river channel to make it hydraulically efficient and ‘containment’ within defences built along the riverbanks to contain all the flow from a 1 in 100-year flood event.

  4. The probabilistic terms from which respondents in their study could choose from included: (1) a 100-year flood; (2) a 1 in 100 flood; (3) a flood with a 1 % annual exceedance probability; and, (4) a flood with a 1 in 100 chance of being equalled or exceeded every year.

  5. See area statistics from Census 2011 at: http://census.cso.ie/areaprofiles/areaprofile.aspx?Geog_Type=ST&Geog_Code=35007. It should be noted that the study area does not conform exactly to the area covered by Census 2011.

  6. Georeferening an image means to establish its location in terms of map projections or coordinate systems (Hackeloeer et al. 2014).

  7. Negative Fuzzy Kappa values are possible depending on spatial autocorrelation of the compared maps.

  8. Although counter-intuitive, this negative coefficient is accounted for by the categorical representation of risk perception used in our survey (1 = ‘major risk’; 2 = ‘minor risk’; 3 = ‘no risk’).

References

  • Adger WN, Dessai S, Goulden M, Hulme M, Lorenzoni I, Nelson DR, Naess LO, Wolf J, Wreford A (2009) Are there social limits to adaptation to climate change? Clim Change 93:335–354

    Article  Google Scholar 

  • Akın A, Clarke KC, Berberoglu S (2014) The impact of historical exclusion on the calibration of the SLEUTH urban growth model. Int J Appl Earth Obs Geoinf 27(Part B):156–168

  • Al-Ahmadi K, Heppenstall A, Hogg J, See L (2009) A fuzzy cellular automata urban growth model (FCAUGM) for the city of Riyadh, Saudi Arabia. Part 1: model structure and validation. Appl Spat Anal Policy 2:65–83

    Article  Google Scholar 

  • Appleyard D (1970) Styles and methods of structuring a city. Environ Behav 2:100–117

    Article  Google Scholar 

  • Barredo JI (2009) Normalised flood losses in Europe: 1970–2006. Nat Hazards Earth Sys Sci 9:97–104

    Article  Google Scholar 

  • Beniston M, Stephenson DB, Christensen OB, Ferro CAT, Frei C, Goyette S, Halsnaes K, Holt T, Jylha K, Palutikof J, Scholl R, Semmler T, Woth K (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81:71–95

    Article  Google Scholar 

  • Bertrand M, Mullainathan S (2001) Do people mean what they say? Implications for subject survey data. Am Econ Rev 91:67–72

    Article  Google Scholar 

  • Blades M (1990) The reliability of data collected from sketch maps. J Environ Psychol 10:327–339

    Article  Google Scholar 

  • Bonnes M, Uzzell D, Carrus G, Kelay T (2007) Inhabitants’ and experts’ assessments of environmental quality for urban sustainability. J Soc Issues 63:59–78

    Article  Google Scholar 

  • Bosschaart A, Kuiper W, van der Schee J, Schoonenboom J (2013) The role of knowledge in students’ flood-risk perception. Nat Hazards 69:1661–1680

    Article  Google Scholar 

  • Botzen WJW, Aerts JCJH, van den Bergh JCJM (2009) Dependence of flood risk perceptions on socioeconomic and objective factors. Water Resour Res 45:W10440

    Google Scholar 

  • Bouwer LM, Bubeck P, Aerts JCJH (2010) Changes in future flood risk due to climate and development in a dutch polder area. Glob Environ Change 20:463–471

    Article  Google Scholar 

  • Braun B, Aßheuer T (2011) Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Nat Hazards 58:771–787

    Article  Google Scholar 

  • Bray Town Council, OPW, Mitchell and Associates, McGill Planning (2007) River dargle (Bray) flood defense scheme environmental impact statement. Bray Co., Wicklow

    Google Scholar 

  • Brilly M, Polic M (2005) Public perception of flood risks, flood forecasting and mitigation. Nat Hazards Earth Sys Sci 5:345–355

    Article  Google Scholar 

  • Brody S, Blessing R, Sebastian A, Bedient P (2012) Delineating the reality of flood risk and loss in Southeast Texas. Nat Hazards Rev 14:89–97

    Article  Google Scholar 

  • Bubeck P, Botzen WJW, Aerts JCJH (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32:1481–1495

    Article  Google Scholar 

  • Campbell E, Henly JR, Elliott DS, Irwin K (2009) Subjective constructions of neighborhood boundaries: lessons from a qualitative study of four neighborboods. J Urban Aff 31:461–490

    Article  Google Scholar 

  • Carletta J (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22:249–254

    Google Scholar 

  • CEC (2006) Proposal for a Directive of the European Parliament and of the Council on the assessment and management of floods. COM (2006) 15 final. Brussels: Commission of the European Communities (CEC)

  • Chaudhuri G, Clarke KC (2013) The SLEUTH land use change model: a review. Int J Environ Res Res 1:88–104

    Google Scholar 

  • Clarke KC, Gaydos LJ (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12:699–714

    Article  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:37–46

    Article  Google Scholar 

  • Correia FN, Fordham M, Saraiva MDG, Bernardo F (1998) Flood hazard assessment and management: interface with the public. Water Resour Manage 12:202–227

    Google Scholar 

  • Coulton CJ, Korbin J, Chan T, Su M (2001) Mapping residents’ perceptions of neighborhood boundaries: a methodological note. Am J Community Psychol 29:371–383

    Article  Google Scholar 

  • Coulton C, Chan T, Mikelbank K (2011) Finding places in community change initiatives: using GIS to uncover resident perceptions of their neighbourhoods. J Community Pract 19:10–28

    Article  Google Scholar 

  • Coulton CJ, Jennings MZ, Chan T (2013) How Big is my neighbourhood? Individual and contextual effects on perception of neighbourhood scale. Am J Community Psychol 51:140–150

    Article  Google Scholar 

  • Curtis JW (2012) Integrating sketch maps with GIS to explore fear of crime in the urban environment: a review of the past and prospects for the future. Cartogr Geogr Inf Sci 39:175–186

    Article  Google Scholar 

  • Curtis JW, Shiau E, Lowery B, Sloane D, Hennigan K, Curtis A (2014) The prospects and problems of integrating sketch maps with geographic information systems to understand environmental perception: a case study of mapping youth fear in Los Angeles gang neighbourhoods. Environ Plan 41:251–271

    Article  Google Scholar 

  • Cutter SL, Mitchell JT, Scott MS (2000) Revealing the vulnerability of people and places: a case study of Georgetown county, South Carolina. Ann Assoc Am Geogr 90:713–737

    Article  Google Scholar 

  • DeChano LM, Butler DR (2001) Analysis of public perception of debris flow hazard. Disaster Prev Manag 10:261–269

    Article  Google Scholar 

  • Didelon C, de Ruffray S, Boquet M, Lambert N (2011) A world of interstices: a fuzzy logic approach to the analysis of interpretative maps. Cartogr J 48:100–107

    Article  Google Scholar 

  • Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11:29–45

    Article  Google Scholar 

  • Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M, Agwe J, Buys P, Kjekstad O, Lyon B, Yetman G (2005) Natural disaster hotspots: a global risk analysis. Washington DC: Disaster risk management working paper series No. 5. The World Bank, Hazard Management Unit

  • Ding WJ, Wang RQ, Wu DQ, Liu J (2013) Cellular automata model as an intuitive approach to simulate complex land-use changes: an evaluation of two multi-state land-use models in the yellow river delta. Stoch Environ Res Risk Assess 27:899–907

  • Downs RM (1970) Geographic space perception: past approaches and future prospects. Prog Geogr 2:65–108

    Google Scholar 

  • Downs R, Stea D (1973) Image and environment. Aldine Publishing Company, Chicago

    Google Scholar 

  • European Environmental Agency (2010) Mapping the impacts of natural hazards and technological accidents in Europe: An overview of the last decade. EEA Technical report, 13

  • Feyen L, Dankers R, Bódis K, Salamon P, Barredo J (2012) Fluvial flood risk in Europe in present and future climates. Clim Change 112:47–62

    Article  Google Scholar 

  • Fogarty M (2011) 25 years on, Hurricane Charlie is remembered: still no funding for flood protection scheme. Bray People 24 August 2011

  • Gaillard JC (2008) Alternative paradigms of volcanic risk perception: the case of Mt. Pinatubo in the Philippines. J Volcanol Geoth Res 172:315–328

    Article  Google Scholar 

  • Gaillard JC, D’Ercole R, Leone F (2001) Cartography of population vulnerability to volcanic hazards and lahars of Mount Pinatubo (Philippines): a case study in Pasig-Potrero River basin (province of Pampanga)/Cartographie de la vulnérabilité des populations face aux phénomènes volcaniques et aux lahars du Mont Pinatubo (Philippines): cas du bassin de la rivière Pasig-Potrero (province de Pampanga). Géomorphologie: relief, processus, environnement 7:209–221

  • Galea S, Tracy M (2007) Participation Rates in Epidemiologic Studies. Ann Epidemiol 17:643–653

    Article  Google Scholar 

  • Garling T (1989) The role of cognitive maps in spatial decisions. J Environ Psychol 9:268–278

    Google Scholar 

  • Golledge RG (2002) The nature of geographic knowledge. Ann Assoc Am Geogr 92:1–14

    Article  Google Scholar 

  • Golledge RG (2007) Behavioral geography and the theoretical/quantitative revolution. Geogr Anal 40:239–257

    Article  Google Scholar 

  • Golledge RG, Stimpson RJ (1997) Spatial behavior: a geographic perspective. The Guilford Press, New York

    Google Scholar 

  • Gould P, White R (1973) Mental maps. Penguin Books, Harmondsworth

    Google Scholar 

  • Grothmann T, Reusswig F (2006) People at risk of flooding: why some residents take precautionary action while others do not. Nat Hazards 38:101–120

    Article  Google Scholar 

  • Hackeloeer A, Klasing K, Krisp JM (2014) Georeferencing: a review of methods and applications. Ann GIS 20:61–69

    Article  Google Scholar 

  • Hagemeier-Klose M, Wagner K (2009) Evaluation of flood hazard maps in print and web mapping services as information tools in flood risk communication. Nat Hazards Earth Sys Sci 9:563–574

    Article  Google Scholar 

  • Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geogr Inf Sci 17:235–249

    Article  Google Scholar 

  • Hagen-Zanker A (2009) An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. Int J Geogr Inf Sci 23:61–73

    Article  Google Scholar 

  • Hagoort M, Geertman S, Ottens H (2008) Spatial externalities, neighbourhood rules and CA land-use modelling. Ann Reg Sci 42:39–56

    Article  Google Scholar 

  • Hart RA, Conn MK (1991) Developmental perspectives on decision making and action in environments. In: Garling T, Evans GW (eds) Environment, cognition and action: an integrated approach. Plenum Press, New York, pp 277–294

    Google Scholar 

  • Hassanzadeh R, Nedovic-Budic Z (2013) Identification of earthquake disaster hot spots with crowd sourced data. In: Zlatanova S, Peters R, Dilo A, Scholten H (eds) Intelligent systems for crisis management. Springer, Berlin Heidelberg, pp 97–119

    Chapter  Google Scholar 

  • Haynes K, Barclay J, Pidgeon N (2007) Volcanic hazard communication using maps: an evaluation of their effectiveness. Bull Volc 70:123–138

    Article  Google Scholar 

  • Huber PB (1979) Anggor floods: reflections on ethnogeography and mental maps. Geogr Rev 60:127–139

    Article  Google Scholar 

  • Irish Meteorological Service (1986) August 1986 Monthly Weather Bulletin. Retrieved 2008 Oct 25

  • Iverson LR, Prasad AM (2007) Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra. Landsc Ecol 22:323–331

    Article  Google Scholar 

  • Jantz CA, Goetz SJ, Shelley MK (2004) Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plan B 31:251–272

    Article  Google Scholar 

  • Johnson CL, Priest SJ (2008) Flood risk management in England: a changing landscape of risk responsibility? Int J Water Resour Dev 24:513–525

    Article  Google Scholar 

  • Joo Y, Jun C, Park S (2010) Design of a dynamic land-use change probability model using spatio-temporal transition matrix. In: Taniar D, Gervasi O, Murgante B, Pardede E, Apduhan BO (eds) Computational science and its applications–ICCSA 2010. Springer, Berlin Heidelberg, pp 105–115

    Chapter  Google Scholar 

  • Kellens W, Terpstra T, De Maeyer P (2013) Perception and communication of flood risks: a systematic review of empirical research. Risk Anal 33:24–49

    Article  Google Scholar 

  • Kelly NM (2000) Spatial accuracy assessment of wetland permit data. Cartogr Geogr Inf Sci 27:117–127

    Article  Google Scholar 

  • King D (2000) You’re on your own: community vulnerability and the need for awareness and education for predictable natural disasters. J Contingencies Crisis Manag 8:223–228

    Article  Google Scholar 

  • Kinsella S (2012) Is Ireland really the role model for austerity? Camb J Econ 36:223–235

    Article  Google Scholar 

  • Kitchin RM (1994) Cognitive maps: what are they and why study them? J Environ Psychol 14:1–19

    Article  Google Scholar 

  • Kumar DS, Arya DS, Vojinovic Z (2013) Modeling of urban growth dynamics and its impact on surface runoff characteristics. Comput Environ Urban Syst 41:124–135

    Article  Google Scholar 

  • Lennon M, Scott M, O’Neill E (2014) Urban design and adapting to flood risk: the role of green infrastructure. J Urban Des 19:745–758

    Article  Google Scholar 

  • Leone F, Lesales T (2009) The interest of cartography for a better perception and management of volcanic risk: from scientific to social representations: the case of Mt. Pelée Volcano, Martinique (Lesser Antilles). J Volcanol Geoth Res 186:186–194

    Article  Google Scholar 

  • Linden M, Sheehy N (2004) Comparison of a verbal questionnaire and map in eliciting environmental perceptions. Environ Behav 36:32–40

    Article  Google Scholar 

  • Lindell MK, Hwang SN (2008) Households’ perceived personal risk and responses in a multihazard environment. Risk Anal 28:539–556

  • Lohmann A, McMurran G (2009) Resident-defined neighbourhood mapping: using GIS to analyze phenomenological neighbourhoods. J Prev Interv Commun 37:66–81

    Article  Google Scholar 

  • Lopez N, Lukinbeal C (2010) Comparing police and residents’ perceptions of crime in a Phoenix neighbourhood using mental maps in GIS. Yearb Assoc Pac Coast Geogr 72:33–55

    Article  Google Scholar 

  • Lynch K (1960) The image of the city. MIT Press, Cambridge

    Google Scholar 

  • Macharis C, De Witte A, Steenberghen T, Van de Walle S, Lannoy P, Polain C (2006) Impact and assessment of “free” public transport measures: lessons from the case study of Brussels. Eur Transp Trasp Eur 32:26–48

    Google Scholar 

  • Mark DM, Freksa C, Hirtle SC, Lloyd R, Tversky B (1999) Cognitive models of geographical space. Int J Geogr Inf Sci 13:747–774

    Article  Google Scholar 

  • Matei SA, Ball-Rokeach SJ (2005) Watts, the 1965 Los Angeles riots, and the communicative construction of the fear epicenter of Los Angeles. Commun Monogr 72:301–323

    Article  Google Scholar 

  • Matei S, Ball-Rokeach SJ, Qiu JL (2001) Fear and misperception of Los Angeles urban space a spatial-statistical study of communication-shaped mental maps. Commun Res 28:429–463

    Article  Google Scholar 

  • McEwen L, Jones O (2012) Building local/lay flood knowledges into community flood resilience planning after the July 2007 floods, Gloucestershire, UK. Hydrol Res 43:675–688

    Article  Google Scholar 

  • Merz B, Kreibich H, Schwarze R, Thieken A (2010) Assessment of economic flood damage. Nat Hazards Earth Sys Sci 10:1697–1724

    Article  Google Scholar 

  • Meyer V, Scheuer S, Haase D (2009) A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. Nat Hazards 48:17–39

    Article  Google Scholar 

  • Meyfroidt P, Lambin EF (2008) Forest transition in Vietnam and its environmental impacts. Glob Change Biol 14:1319–1336

    Article  Google Scholar 

  • Minnery J, Knight J, Byrne J, Spencer J (2009) Bounding neighbourhoods: how do residents do it? Plan Pract Res 24:471–493

    Article  Google Scholar 

  • Montello DR (2009) Cognitive research in GIScience: recent achievements and future prospects. Geogr Compass 3:1824–1840

    Article  Google Scholar 

  • Moore GT, Golledge RG (1976) Environmental knowing: concepts and theories. In: GT Moore, RG Golledge (Eds.) Environmental knowing: theories, research ad methods. Stroudsburg, Pa: Dowden, Hutchinson and Ross, pp 3–24

  • Motoyoshi T (2006) Public perception of flood risk and community-based disaster preparedness. In: Ikeda S, Fukuzono T, Sato T (eds) A bettter integrated management of disaster risks: Toward resilient society to emerging disaster risks in mega-cities. Tokyo, Terrapub and Nied, pp 121–134

    Google Scholar 

  • Novak V, Perfilieva I, Močkoř J (1999) Mathematical principles of fuzzy logic. Kluwer Academic Publishers, Norwell

    Book  Google Scholar 

  • O’Sullivan JJ, Bradford RA, Bonaiuto M, De Dominicis S, Rotko P, Aaltonen J, Waylen K, Langan SJ (2012) Enhancing flood resilience through improved risk communications. Nat Hazards Earth Sys Sci 12:2271–2282

    Article  Google Scholar 

  • Pagneux E, Gísladóttir G, Jónsdóttir S (2011) Public perception of flood hazard and flood risk in Iceland: a case study in a watershed prone to ice-jam floods. Nat Hazards 58:269–287

    Article  Google Scholar 

  • Pocock DCD (1976) Some characteristics of mental maps: an empirical study. Trans Inst Br Geogr New Series 1:493–512

    Article  Google Scholar 

  • Prabhakar SVRK, Srinivasan A, Shaw R (2009) Climate change and local level disaster risk reduction planning: need, opportunities and challenges. Mitig Adapt Strat Glob Change 14:7–33

    Article  Google Scholar 

  • Reichel C, Fromming UU (2014) Participatory mapping of local disaster risk reduction knowledge: an example from Switzerland. Int J Disaster Risk Sci 5:41–54

    Article  Google Scholar 

  • Ripple WJ, Bradshaw GA, Spies TA (1991) Measuring landscape pattern in the cascade range of Oregon, YSA. Biol Conserv 57:73–88

    Article  Google Scholar 

  • Ruin I, Gaillard J-C, Lutoff C (2007) How to get there? Assessing motorists’ flash flood risk perception on daily itineraries. Environ Hazards 7:235–244

    Article  Google Scholar 

  • Schanze J (2006) Flood risk management-a basic framework. In: Schanze J, Zeman E, Marsalek J (eds) Flood risk management-hazards, vulnerability and mitigation measures. Springer, Berlin Heidelberg, pp 149–167

    Chapter  Google Scholar 

  • Scheuer S, Haase D, Meyer V (2011) Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnerability. Nat Hazards 58:731–751

    Article  Google Scholar 

  • Shahumyan H, White R, Twumasi B, Convery S, Williams B, Critchley M, Carty J, Walsh C, Brennan M (2009) The MOLAND model calibration and validation for greater Dublin region. urban institute ireland working paper series 09/03

  • Siegrist M, Gutscher H (2006) Flooding risks: a comparison of lay people’s perceptions and expert’s assessments in Switzerland. Risk Anal 26:971–979

    Article  Google Scholar 

  • Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26:525–552

    Article  Google Scholar 

  • Slovic P (1987) Perception of Risk. Science 236:280–285

    Article  Google Scholar 

  • Stea D (1969) The measurement of mental maps: An experimental model of studying conceptual spaces. In: KR Cox, RG Golledge (Eds.) Behavioral Problems in Geography: A Symposium. Northwestern University Studies in Geography No. 17, Evanston, Il: Northwestern University Press, pp 228–53

  • Superquinn Ltd. v. Bray U.D.C. [1998] IEHC 28; [1998] 3 IR 542 (18th February, 1998)

  • Tung-Wen Sun M, Tsai YT, Shih MC, Lin JYW (2009) Public participation and the concept of space in environmental governance: an application of PPGIS. Public Adm Dev 29:250–261

    Article  Google Scholar 

  • Visser H, de Nijs T (2006) The map comparison kit. Environ Model Softw 21:346–358

    Article  Google Scholar 

  • Wagner K (2007) Mental models of flash floods and landslides. Risk Anal 27:671–682

    Article  Google Scholar 

  • Wealands SR, Grayson RB, Walker JP (2005) Quantitative comparison of spatial fields for hydrological model assessment–some promising approaches. Adv Water Resour 28:15–32

    Article  Google Scholar 

  • Wolsink M (2010) Contested environmental policy infrastructure: socio-political acceptance of renewable energy, water, and waste facilities. Environ Impact Assess Rev 30:302–311

    Article  Google Scholar 

  • Wu X, Hu Y, He HS, Bu R, Onsted J, Xi F (2009) Performance evaluation of the SLEUTH model in the Shenyang metropolitan area of northeastern China. Environ Model Assess 14:221–230

    Article  Google Scholar 

  • Wynne-Evans E, Jones L, Caldin H, Murray V (2011) Mapping of European flooding events 2000–2009. Centre for Radiation, Chemical and Environmental Hazards September 2011 Issue 20, pp 44–49

  • Xiao N, Armstrong MP (2012) Towards a multiobjective view of cartographic design. Cartogarphy Geogr Inf Sci 39:76–87

    Article  Google Scholar 

  • Xiao J, Shen Y, Ge J, Tateishi R, Tang C, Liang Y, Huang Z (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75:69–80

  • Yang X, Lo CP (2002) Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int J Remote Sens 23:1775–1798

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. School of Geography, Planning and Environmental Policy, University College Dublin, Belfield, Dublin 4, Ireland

    Eoin O’Neill, Michael Brennan & Finbarr Brereton

  2. UCD Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland

    Eoin O’Neill & Finbarr Brereton

  3. National Center for Smart Growth Research and Education, University of Maryland, College Park, MD, 20742, USA

    Harutyun Shahumyan

Authors
  1. Eoin O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Brennan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Finbarr Brereton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Harutyun Shahumyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eoin O’Neill.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Neill, E., Brennan, M., Brereton, F. et al. Exploring a spatial statistical approach to quantify flood risk perception using cognitive maps. Nat Hazards 76, 1573–1601 (2015). https://doi.org/10.1007/s11069-014-1559-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-014-1559-8

Keywords

Search

Navigation