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

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.

Keywords

Risk perception Flooding Cognitive maps Fuzzy Kappa 

Notes

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.

References

  1. 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 93:335–354CrossRefGoogle Scholar
  2. 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):156–168Google Scholar
  3. 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 2:65–83CrossRefGoogle Scholar
  4. Appleyard D (1970) Styles and methods of structuring a city. Environ Behav 2:100–117CrossRefGoogle Scholar
  5. Barredo JI (2009) Normalised flood losses in Europe: 1970–2006. Nat Hazards Earth Sys Sci 9:97–104CrossRefGoogle Scholar
  6. 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 81:71–95CrossRefGoogle Scholar
  7. Bertrand M, Mullainathan S (2001) Do people mean what they say? Implications for subject survey data. Am Econ Rev 91:67–72CrossRefGoogle Scholar
  8. Blades M (1990) The reliability of data collected from sketch maps. J Environ Psychol 10:327–339CrossRefGoogle Scholar
  9. Bonnes M, Uzzell D, Carrus G, Kelay T (2007) Inhabitants’ and experts’ assessments of environmental quality for urban sustainability. J Soc Issues 63:59–78CrossRefGoogle Scholar
  10. Bosschaart A, Kuiper W, van der Schee J, Schoonenboom J (2013) The role of knowledge in students’ flood-risk perception. Nat Hazards 69:1661–1680CrossRefGoogle Scholar
  11. Botzen WJW, Aerts JCJH, van den Bergh JCJM (2009) Dependence of flood risk perceptions on socioeconomic and objective factors. Water Resour Res 45:W10440Google Scholar
  12. 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 20:463–471CrossRefGoogle Scholar
  13. Braun B, Aßheuer T (2011) Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Nat Hazards 58:771–787CrossRefGoogle Scholar
  14. Bray Town Council, OPW, Mitchell and Associates, McGill Planning (2007) River dargle (Bray) flood defense scheme environmental impact statement. Bray Co., WicklowGoogle Scholar
  15. Brilly M, Polic M (2005) Public perception of flood risks, flood forecasting and mitigation. Nat Hazards Earth Sys Sci 5:345–355CrossRefGoogle Scholar
  16. Brody S, Blessing R, Sebastian A, Bedient P (2012) Delineating the reality of flood risk and loss in Southeast Texas. Nat Hazards Rev 14:89–97CrossRefGoogle Scholar
  17. Bubeck P, Botzen WJW, Aerts JCJH (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32:1481–1495CrossRefGoogle Scholar
  18. 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 31:461–490CrossRefGoogle Scholar
  19. Carletta J (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22:249–254Google Scholar
  20. 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)Google Scholar
  21. Chaudhuri G, Clarke KC (2013) The SLEUTH land use change model: a review. Int J Environ Res Res 1:88–104Google Scholar
  22. 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 12:699–714CrossRefGoogle Scholar
  23. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:37–46CrossRefGoogle Scholar
  24. Correia FN, Fordham M, Saraiva MDG, Bernardo F (1998) Flood hazard assessment and management: interface with the public. Water Resour Manage 12:202–227Google Scholar
  25. Coulton CJ, Korbin J, Chan T, Su M (2001) Mapping residents’ perceptions of neighborhood boundaries: a methodological note. Am J Community Psychol 29:371–383CrossRefGoogle Scholar
  26. 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 19:10–28CrossRefGoogle Scholar
  27. 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 51:140–150CrossRefGoogle Scholar
  28. 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 39:175–186CrossRefGoogle Scholar
  29. 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 41:251–271CrossRefGoogle Scholar
  30. 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 90:713–737CrossRefGoogle Scholar
  31. DeChano LM, Butler DR (2001) Analysis of public perception of debris flow hazard. Disaster Prev Manag 10:261–269CrossRefGoogle Scholar
  32. 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 48:100–107CrossRefGoogle Scholar
  33. Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11:29–45CrossRefGoogle Scholar
  34. 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 UnitGoogle Scholar
  35. 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 27:899–907Google Scholar
  36. Downs RM (1970) Geographic space perception: past approaches and future prospects. Prog Geogr 2:65–108Google Scholar
  37. Downs R, Stea D (1973) Image and environment. Aldine Publishing Company, ChicagoGoogle Scholar
  38. European Environmental Agency (2010) Mapping the impacts of natural hazards and technological accidents in Europe: An overview of the last decade. EEA Technical report, 13Google Scholar
  39. Feyen L, Dankers R, Bódis K, Salamon P, Barredo J (2012) Fluvial flood risk in Europe in present and future climates. Clim Change 112:47–62CrossRefGoogle Scholar
  40. Fogarty M (2011) 25 years on, Hurricane Charlie is remembered: still no funding for flood protection scheme. Bray People 24 August 2011Google Scholar
  41. Gaillard JC (2008) Alternative paradigms of volcanic risk perception: the case of Mt. Pinatubo in the Philippines. J Volcanol Geoth Res 172:315–328CrossRefGoogle Scholar
  42. 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 7:209–221Google Scholar
  43. Galea S, Tracy M (2007) Participation Rates in Epidemiologic Studies. Ann Epidemiol 17:643–653CrossRefGoogle Scholar
  44. Garling T (1989) The role of cognitive maps in spatial decisions. J Environ Psychol 9:268–278Google Scholar
  45. Golledge RG (2002) The nature of geographic knowledge. Ann Assoc Am Geogr 92:1–14CrossRefGoogle Scholar
  46. Golledge RG (2007) Behavioral geography and the theoretical/quantitative revolution. Geogr Anal 40:239–257CrossRefGoogle Scholar
  47. Golledge RG, Stimpson RJ (1997) Spatial behavior: a geographic perspective. The Guilford Press, New YorkGoogle Scholar
  48. Gould P, White R (1973) Mental maps. Penguin Books, HarmondsworthGoogle Scholar
  49. Grothmann T, Reusswig F (2006) People at risk of flooding: why some residents take precautionary action while others do not. Nat Hazards 38:101–120CrossRefGoogle Scholar
  50. Hackeloeer A, Klasing K, Krisp JM (2014) Georeferencing: a review of methods and applications. Ann GIS 20:61–69CrossRefGoogle Scholar
  51. 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 9:563–574CrossRefGoogle Scholar
  52. Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geogr Inf Sci 17:235–249CrossRefGoogle Scholar
  53. Hagen-Zanker A (2009) An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. Int J Geogr Inf Sci 23:61–73CrossRefGoogle Scholar
  54. Hagoort M, Geertman S, Ottens H (2008) Spatial externalities, neighbourhood rules and CA land-use modelling. Ann Reg Sci 42:39–56CrossRefGoogle Scholar
  55. 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 277–294Google Scholar
  56. 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 97–119CrossRefGoogle Scholar
  57. Haynes K, Barclay J, Pidgeon N (2007) Volcanic hazard communication using maps: an evaluation of their effectiveness. Bull Volc 70:123–138CrossRefGoogle Scholar
  58. Huber PB (1979) Anggor floods: reflections on ethnogeography and mental maps. Geogr Rev 60:127–139CrossRefGoogle Scholar
  59. Irish Meteorological Service (1986) August 1986 Monthly Weather Bulletin. Retrieved 2008 Oct 25Google Scholar
  60. Iverson LR, Prasad AM (2007) Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra. Landsc Ecol 22:323–331CrossRefGoogle Scholar
  61. 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 31:251–272CrossRefGoogle Scholar
  62. Johnson CL, Priest SJ (2008) Flood risk management in England: a changing landscape of risk responsibility? Int J Water Resour Dev 24:513–525CrossRefGoogle Scholar
  63. 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 105–115CrossRefGoogle Scholar
  64. Kellens W, Terpstra T, De Maeyer P (2013) Perception and communication of flood risks: a systematic review of empirical research. Risk Anal 33:24–49CrossRefGoogle Scholar
  65. Kelly NM (2000) Spatial accuracy assessment of wetland permit data. Cartogr Geogr Inf Sci 27:117–127CrossRefGoogle Scholar
  66. 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 8:223–228CrossRefGoogle Scholar
  67. Kinsella S (2012) Is Ireland really the role model for austerity? Camb J Econ 36:223–235CrossRefGoogle Scholar
  68. Kitchin RM (1994) Cognitive maps: what are they and why study them? J Environ Psychol 14:1–19CrossRefGoogle Scholar
  69. Kumar DS, Arya DS, Vojinovic Z (2013) Modeling of urban growth dynamics and its impact on surface runoff characteristics. Comput Environ Urban Syst 41:124–135CrossRefGoogle Scholar
  70. Lennon M, Scott M, O’Neill E (2014) Urban design and adapting to flood risk: the role of green infrastructure. J Urban Des 19:745–758CrossRefGoogle Scholar
  71. 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 186:186–194CrossRefGoogle Scholar
  72. Linden M, Sheehy N (2004) Comparison of a verbal questionnaire and map in eliciting environmental perceptions. Environ Behav 36:32–40CrossRefGoogle Scholar
  73. Lindell MK, Hwang SN (2008) Households’ perceived personal risk and responses in a multihazard environment. Risk Anal 28:539–556Google Scholar
  74. Lohmann A, McMurran G (2009) Resident-defined neighbourhood mapping: using GIS to analyze phenomenological neighbourhoods. J Prev Interv Commun 37:66–81CrossRefGoogle Scholar
  75. 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 72:33–55CrossRefGoogle Scholar
  76. Lynch K (1960) The image of the city. MIT Press, CambridgeGoogle Scholar
  77. 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 32:26–48Google Scholar
  78. Mark DM, Freksa C, Hirtle SC, Lloyd R, Tversky B (1999) Cognitive models of geographical space. Int J Geogr Inf Sci 13:747–774CrossRefGoogle Scholar
  79. 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 72:301–323CrossRefGoogle Scholar
  80. 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 28:429–463CrossRefGoogle Scholar
  81. McEwen L, Jones O (2012) Building local/lay flood knowledges into community flood resilience planning after the July 2007 floods, Gloucestershire, UK. Hydrol Res 43:675–688CrossRefGoogle Scholar
  82. Merz B, Kreibich H, Schwarze R, Thieken A (2010) Assessment of economic flood damage. Nat Hazards Earth Sys Sci 10:1697–1724CrossRefGoogle Scholar
  83. Meyer V, Scheuer S, Haase D (2009) A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. Nat Hazards 48:17–39CrossRefGoogle Scholar
  84. Meyfroidt P, Lambin EF (2008) Forest transition in Vietnam and its environmental impacts. Glob Change Biol 14:1319–1336CrossRefGoogle Scholar
  85. Minnery J, Knight J, Byrne J, Spencer J (2009) Bounding neighbourhoods: how do residents do it? Plan Pract Res 24:471–493CrossRefGoogle Scholar
  86. Montello DR (2009) Cognitive research in GIScience: recent achievements and future prospects. Geogr Compass 3:1824–1840CrossRefGoogle Scholar
  87. 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 3–24Google Scholar
  88. 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 121–134Google Scholar
  89. Novak V, Perfilieva I, Močkoř J (1999) Mathematical principles of fuzzy logic. Kluwer Academic Publishers, NorwellCrossRefGoogle Scholar
  90. 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 12:2271–2282CrossRefGoogle Scholar
  91. 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 58:269–287CrossRefGoogle Scholar
  92. Pocock DCD (1976) Some characteristics of mental maps: an empirical study. Trans Inst Br Geogr New Series 1:493–512CrossRefGoogle Scholar
  93. 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 14:7–33CrossRefGoogle Scholar
  94. Reichel C, Fromming UU (2014) Participatory mapping of local disaster risk reduction knowledge: an example from Switzerland. Int J Disaster Risk Sci 5:41–54CrossRefGoogle Scholar
  95. Ripple WJ, Bradshaw GA, Spies TA (1991) Measuring landscape pattern in the cascade range of Oregon, YSA. Biol Conserv 57:73–88CrossRefGoogle Scholar
  96. Ruin I, Gaillard J-C, Lutoff C (2007) How to get there? Assessing motorists’ flash flood risk perception on daily itineraries. Environ Hazards 7:235–244CrossRefGoogle Scholar
  97. 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 149–167CrossRefGoogle Scholar
  98. 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 58:731–751CrossRefGoogle Scholar
  99. 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 09/03Google Scholar
  100. Siegrist M, Gutscher H (2006) Flooding risks: a comparison of lay people’s perceptions and expert’s assessments in Switzerland. Risk Anal 26:971–979CrossRefGoogle Scholar
  101. Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26:525–552CrossRefGoogle Scholar
  102. Slovic P (1987) Perception of Risk. Science 236:280–285CrossRefGoogle Scholar
  103. 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 228–53Google Scholar
  104. Superquinn Ltd. v. Bray U.D.C. [1998] IEHC 28; [1998] 3 IR 542 (18th February, 1998)Google Scholar
  105. 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 29:250–261CrossRefGoogle Scholar
  106. Visser H, de Nijs T (2006) The map comparison kit. Environ Model Softw 21:346–358CrossRefGoogle Scholar
  107. Wagner K (2007) Mental models of flash floods and landslides. Risk Anal 27:671–682CrossRefGoogle Scholar
  108. Wealands SR, Grayson RB, Walker JP (2005) Quantitative comparison of spatial fields for hydrological model assessment–some promising approaches. Adv Water Resour 28:15–32CrossRefGoogle Scholar
  109. Wolsink M (2010) Contested environmental policy infrastructure: socio-political acceptance of renewable energy, water, and waste facilities. Environ Impact Assess Rev 30:302–311CrossRefGoogle Scholar
  110. 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 14:221–230CrossRefGoogle Scholar
  111. 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 44–49Google Scholar
  112. Xiao N, Armstrong MP (2012) Towards a multiobjective view of cartographic design. Cartogarphy Geogr Inf Sci 39:76–87CrossRefGoogle Scholar
  113. 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 75:69–80Google Scholar
  114. 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 23:1775–1798Google Scholar

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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