380
Views
2
CrossRef citations to date
0
Altmetric
Articles

Small-Area Estimations from Survey Data for High-Resolution Maps of Urban Flood Risk Perception and Evacuation Behavior

ORCID Icon & ORCID Icon
Pages 425-447 | Received 04 Dec 2021, Accepted 27 Jun 2022, Published online: 03 Oct 2022

References

  • Aerts, J. C. J. H., W. J. Botzen, K. C. Clarke, S. L. Cutter, J. W. Hall, B. Merz, E. Michel-Kerjan, J. Mysiak, S. Surminski, and H. Kunreuther. 2018. Integrating human behaviour dynamics into flood disaster risk assessment. Nature Climate Change 8 (3):193–99. doi: 10.1038/s41558-018-0085-1.
  • Allan, J. N., J. T. Ripberger, W. Wehde, M. Krocak, C. L. Silva, and H. C. Jenkins‐Smith. 2020. Geographic distributions of extreme weather risk perceptions in the United States. Risk Analysis: An Official Publication of the Society for Risk Analysis 40 (12):2498–2508. doi: 10.1111/risa.13569.
  • Bamberg, S., T. Masson, K. Brewitt, and N. Nemetschek. 2017. Threat, coping and flood prevention—A meta-analysis. Journal of Environmental Psychology 54:116–26. doi: 10.1016/j.jenvp.2017.08.001.
  • Bates, D., M. Maechler, B. Bolker, S. Walker, R. H. B. Christensen, H. Singmann, and B. Dai. 2014. lme4: Linear mixed-effects models using Eigen and S4 (1.1-7) [Computer software]. http://cran.r-project.org/web/packages/lme4/index.html.
  • Baubion, C. 2015. Losing memory—The risk of a major flood in the Paris region: Improving prevention policies. Water Policy 17 (Suppl. 1):156–79. doi: 10.2166/wp.2015.008.
  • Beccari, B. 2016. A comparative analysis of disaster risk, vulnerability and resilience composite indicators. PLOS Currents Disasters 8. doi: 10.1371/currents.dis.453df025e34b682e9737f95070f9b970.
  • Begg, C., M. Ueberham, T. Masson, and C. Kuhlicke. 2017. Interactions between citizen responsibilization, flood experience and household resilience: Insights from the 2013 flood in Germany. International Journal of Water Resources Development 33 (4):591–608. doi: 10.1080/07900627.2016.1200961.
  • Birkholz, S., M. Muro, P. Jeffrey, and H. M. Smith. 2014. Rethinking the relationship between flood risk perception and flood management. The Science of the Total Environment 478:12–20. doi: 10.1016/j.scitotenv.2014.01.061.
  • Blöschl, G., J. Hall, A. Viglione, R. A. Perdigão, J. Parajka, B. Merz, and M. Bohãč. 2019. Changing climate both increases and decreases European river floods. Nature 573 (7772):108–11. doi: 10.1038/s41586-019-1495-6.
  • Botzen, W., H. Kunreuther, and E. Michel-Kerjan. 2015. Divergence between individual perceptions and objective indicators of tail risks: Evidence from floodplain residents in New York City. Judgment and Decision Making 10 (4):365–85. doi: http://journal.sjdm.org/15/15415/jdm15415.html.
  • Buttice, M. K., and B. Highton. 2013. How does multilevel regression and poststratification perform with conventional national surveys? Political Analysis 21 (4):449–67. doi: 10.1093/pan/mpt017.
  • Caughey, D., and C. Warshaw. 2019. Public opinion in subnational politics. The Journal of Politics 81 (1):352–63. doi: 10.1086/700723.
  • Coronese, M., F. Lamperti, K. Keller, F. Chiaromonte, and A. Roventini. 2019. Evidence for sharp increase in the economic damages of extreme natural disasters. Proceedings of the National Academy of Sciences of the United States of America 116 (43): 21450–55. doi: 10.1073/pnas.1907826116.
  • Cutter, S. L., B. J. Boruff, and W. L. Shirley. 2003. Social vulnerability to environmental hazards. Social Science Quarterly 84 (2):242–61. doi: 10.1111/1540-6237.8402002.
  • Debionne, S., I. Ruin, S. Shabou, C. Lutoff, and J.-D. Creutin. 2016. Assessment of commuters’ daily exposure to flash flooding over the roads of the Gard region, France. Journal of Hydrology 541:636–48. doi: 10.1016/j.jhydrol.2016.01.064.
  • De Dominicis, S., F. Fornara, U. Ganucci Cancellieri, C. Twigger-Ross, and M. Bonaiuto. 2015. We are at risk, and so what? Place attachment, environmental risk perceptions and preventive coping behaviours. Journal of Environmental Psychology 43:66–78. doi: 10.1016/j.jenvp.2015.05.010.
  • Demuth, J. L., R. E. Morss, J. K. Lazo, and C. Trumbo. 2016. The effects of past hurricane experiences on evacuation intentions through risk perception and efficacy beliefs: A mediation analysis. Weather, Climate, and Society 8 (4):327–44. doi: 10.1175/WCAS-D-15-0074.1.
  • de Sherbinin, A., A. Bukvic, G. Rohat, M. Gall, B. McCusker, B. Preston, A. Apotsos, C. Fish, S. Kienberger, P. Muhonda, et al. 2019. Climate vulnerability mapping: A systematic review and future prospects. WIREs Climate Change 10 (5):e600. doi: 10.1002/wcc.600.
  • Downes, M., L. C. Gurrin, D. R. English, J. Pirkis, D. Currier, M. J. Spittal, and J. B. Carlin. 2018. Multilevel regression and poststratification: A modeling approach to estimating population quantities from highly selected survey samples. American Journal of Epidemiology 187 (8):1780–90. doi: 10.1093/aje/kwy070.
  • Fekete, A., and F. Fiedrich, eds. 2018. Urban disaster resilience and security: Addressing risks in societies. New York: Springer International. doi: 10.1007/978-3-319-68606-6
  • Fowler, L. 2016. The states of public opinion on the environment. Environmental Politics 25 (2):315–37. doi: 10.1080/09644016.2015.1102351.
  • Fuchs, S., and T. Thaler, eds. 2018. Vulnerability and resilience to natural hazards. Cambridge, UK: Cambridge University Press.
  • Ge, Y., G. Yang, X. Wang, W. Dou, X. Lu, and J. Mao. 2021. Understanding risk perception from floods: A case study from China. Natural Hazards 105 (3):3119–40. doi: 10.1007/s11069-020-04458-y.
  • Groves, R. M., F. J. Fowler, M. P. Couper, J. M. Lepkowski, E. Singer, and R. Tourangeau. 2004. Survey methodology. Hoboken, NJ: Wiley.
  • Haer, T., W. W. Botzen, H. de Moel, and J. C. Aerts. 2017. Integrating household risk mitigation behavior in flood risk analysis: An agent‐based model approach. Risk Analysis 37 (10): 1977–92. doi: 10.1111/risa.12740.
  • Hamilton, K., D. Demant, A. E. Peden, and M. S. Hagger. 2020. A systematic review of human behaviour in and around floodwater. International Journal of Disaster Risk Reduction 47:101561. doi: 10.1016/j.ijdrr.2020.101561.
  • Hamilton, L. C., J. Hartter, and T. G. Safford. 2015. Validity of county-level estimates of climate change beliefs. Nature Climate Change 5 (8):704. doi: 10.1038/nclimate2720.
  • Hartmann, T., and P. Driessen. 2017. The flood risk management plan: Towards spatial water governance. Journal of Flood Risk Management 10 (2):145–54. doi: 10.1111/jfr3.12077.
  • Höppner, C., R. Whittle, M. Bründl, and M. Buchecker. 2012. Linking social capacities and risk communication in Europe: A gap between theory and practice? Natural Hazards 64 (2):1753–78. doi: 10.1007/s11069-012-0356-5.
  • Howe, P. D. 2018. Modeling geographic variation in household disaster preparedness across U.S. states and metropolitan areas. The Professional Geographer 70 (3):491–503. doi: 10.1080/00330124.2017.1416301.
  • Howe, P. D., J. R. Marlon, X. Wang, and A. Leiserowitz. 2019. Public perceptions of the health risks of extreme heat across US states, counties, and neighborhoods. Proceedings of the National Academy of Sciences of the United States of America 116 (14):6743–48. doi: 10.1073/pnas.1813145116.
  • Howe, P. D., M. Mildenberger, J. R. Marlon, and A. Leiserowitz. 2015. Geographic variation in opinions on climate change at state and local scales in the USA. Nature Climate Change 5 (6):596–603. doi: 10.1038/nclimate2583.
  • Hudson, P., and W. J. W. Botzen. 2019. Cost–benefit analysis of flood‐zoning policies: A review of current practice. WIREs Water 6 (6):e1387. doi: 10.1002/wat2.1387.
  • Institut National de la Statistique et des Etudes Economiques (INSEE). 2015. Données Carroyées 200m Filosofi 2015. Fichier Localisé Social et Fiscal [Filosofi 2015 200m grid data. Localized social and fiscal database]. Institut National de la Statistique et des Etudes Economiques, France. Accessed May 5, 2018. https://www.insee.fr/fr/statistiques/4176305.
  • Institut National de la Statistique et des Etudes Economiques (INSEE). 2017a. Recensement de la Population 2017. Base Infracommunale IRIS [2017 population census: Infra-municipal IRIS database]. Institut National de la Statistique et des Etudes Economiques, France. Accessed April 21, 2021. https://www.insee.fr/fr/statistiques/4799309.
  • Institut National de la Statistique et des Etudes Economiques (INSEE). 2017b. Revenus, pauvreté et niveau de vie en 2017. Base Infracommunale Filosofi [Income, poverty and living standards in 2017: Infra-municipal Filosofi database]. Institut National de la Statistique et des Etudes Economiques, France. Accessed April 21, 2021. https://www.insee.fr/fr/statistiques/4479212.
  • Institution Interdépartementale des Barrages Réservoirs du Bassin de la Seine (IIBRBS). 2013. Crues de reference et zones inondables [Reference floods and flood zones]. Institution Interdépartementale des Barrages Réservoirs du Bassin de la Seine, Hydratec, France. Accessed May 5, 2018. https://www.data.gouv.fr/fr/datasets/crues-de-references-idf/.
  • Intergovernmental Panel on Climate Change (IPCC). 2021. AR6 Climate Change 2021: The physical science basis. Cambridge, UK: Cambridge University Press. Accessed December 9, 2021. https://www.ipcc.ch/.
  • International Federation of Red Cross and Red Crescent Societies (IFRC). 2018. World Disasters Report 2018. Leaving no one behind. Accessed January 19, 2021. https://media.ifrc.org/ifrc/world-disaster-report-2018/
  • Kellens, W., T. Terpstra, and P. De Maeyer. 2013. Perception and communication of flood risks: A systematic review of empirical research. Risk Analysis: An Official Publication of the Society for Risk Analysis 33 (1):24–49. doi: 10.1111/j.1539-6924.2012.01844.x.
  • Kim, J., and S. S. Oh. 2015. Confidence, knowledge, and compliance with emergency evacuation. Journal of Risk Research 18 (1):111–26. doi: 10.1080/13669877.2014.880728.
  • Kreibich, H., M. Müller, K. Schröter, and A. H. Thieken. 2017. New insights into flood warning reception and emergency response by affected parties. Natural Hazards and Earth System Sciences 17 (12):2075–92. doi: 10.5194/nhess-17-2075-2017.
  • Kuhlicke, C. 2019. Risk and resilience in the management and governance of natural hazards. In Oxford research encyclopedia of natural hazard science, ed. C. Kuhlicke. New York: Oxford University Press. Accessed April 21, 2021. doi: 10.1093/acrefore/9780199389407.013.299.
  • Kuhlicke, C., S. Seebauer, P. Hudson, C. Begg, P. Bubeck, C. Dittmer, T. Grothmann, A. Heidenreich, H. Kreibich, D. F. Lorenz, et al. 2020. The behavioral turn in flood risk management, its assumptions and potential implications. WIREs Water 7 (3):e1418. doi: 10.1002/wat2.1418.
  • Kwan, M. P. 2018. The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health 15 (9):1841. doi: 10.3390/ijerph15091841.
  • Lax, J. R., and J. H. Phillips. 2009. How should we estimate public opinion in the states? American Journal of Political Science 53 (1):107–21. doi: 10.1111/j.1540-5907.2008.00360.x.
  • Lazrus, H., R. E. Morss, J. L. Demuth, J. K. Lazo, and A. Bostrom. 2016. “Know what to do if you encounter a flash flood”: Mental models analysis for improving flash flood risk communication and public decision making. Risk Analysis: An Official Publication of the Society for Risk Analysis 36 (2):411–27. doi: 10.1111/risa.12480.
  • Lechowska, E. 2018. What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements. Natural Hazards 94 (3):1341–66. doi: 10.1007/s11069-018-3480-z.
  • Lee, T. M., E. M. Markowitz, P. D. Howe, C.-Y. Ko, and A. A. Leiserowitz. 2015. Predictors of public climate change awareness and risk perception around the world. Nature Climate Change 5 (11):1014–20. doi: 10.1038/nclimate2728.
  • Leemann, L., and F. Wasserfallen. 2017. Extending the use and prediction precision of subnational public opinion estimation: Extending use and precision of MRP. American Journal of Political Science 61 (4):1003–22. doi: 10.1111/ajps.12319.
  • Lindell, M. K., J. E. Kang, and C. S. Prater. 2011. The logistics of household hurricane evacuation. Natural Hazards 58 (3):1093–1109. doi: 10.1007/s11069-011-9715-x.
  • Lindell, M. K., P. M. Murray-Tuite, M. K. Lindell, P. B. Wolshon, and E. J. Baker. 2019. Large-scale evacuation: The analysis, modeling, and management of emergency relocation from hazardous areas. London and New York: Routledge/Taylor & Francis Group.
  • Marlon, J., P. D. Howe, M. Mildenberger, A. Leiserowitz, and X. Wang. 2020. Yale climate opinion maps—U.S. 2020. New Haven, CT: Yale Program on Climate Change Communication. Accessed April 21, 2021. http://climatecommunication.yale.edu/visualizations-data/ycom-us/.
  • Mendelsohn, J., G. Johnson, K. Klima, R. Steratore, S. Cohen, G. Kirkwood, L. Dixon, J. L. Hastings, and P. S. Steinberg. 2021. Developing metrics and scoring procedures to support mitigation grant program decisionmaking. Santa Monica, CA: RAND Corporation. Accessed December 9, 2021. https://doi.org/10.7249/RRA377-1
  • Michel-Kerjan, E. 2015. We must build resilience into our communities. Nature 524 (7566):389. doi: 10.1038/524389a.
  • Mildenberger, M., P. Howe, E. Lachapelle, L. Stokes, J. Marlon, and T. Gravelle. 2016. The distribution of climate change public opinion in Canada. PLoS ONE 11 (8):e0159774. doi: 10.1371/journal.pone.0159774.
  • O’Neill, E., M. Brennan, F. Brereton, and H. Shahumyan. 2015. Exploring a spatial statistical approach to quantify flood risk perception using cognitive maps. Natural Hazards 76 (3):1573–1601. doi: 10.1007/s11069-014-1559-8.
  • Organisation for Economic Co-operation and Development (OECD). 2018. Preventing the flooding of the Seine in the Paris–Ile de France region: Progress made and future challenges. Paris: OECD. https://www.oecd.org/governance/risk/preventing-the-flooding-of-the-seine-2018.pdf.
  • Pacheco, J. 2011. Using national surveys to measure dynamic US state public opinion: A guideline for scholars and an application. State Politics & Policy Quarterly 11 (4):415–39. doi: 10.1177/1532440011419287.
  • Park, D. K., A. Gelman, and J. Bafumi. 2004. Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Political Analysis 12 (4):375–85. doi: 10.1093/pan/mph024.
  • Poussin, J. K., W. J. W. Botzen, and J. C. J. H. Aerts. 2014. Factors of influence on flood damage mitigation behaviour by households. Environmental Science & Policy 40:69–77. doi: 10.1016/j.envsci.2014.01.013.
  • Poussin, J. K., W. J. Wouter Botzen, and J. C. J. H. Aerts. 2015. Effectiveness of flood damage mitigation measures: Empirical evidence from French flood disasters. Global Environmental Change 31:74–84. doi: 10.1016/j.gloenvcha.2014.12.007.
  • Reghezza-Zitt, M. 2019. Crisis management, uncertainty and the unthinkable. How to anticipate and prepare for a systemic crisis in case of major flooding of the Parisian metropolitan area. Annales de Geographie 726 (2):5–30. doi: 10.3917/ag.726.0005.
  • Reghezza-Zitt, M., and S. Rufat. 2015. Resilience imperative: Uncertainty, risks and disasters. London: Elsevier.
  • Reghezza-Zitt, M., and S. Rufat. 2019. Disentangling the range of responses to threats, hazards and disasters. Vulnerability, resilience and adaptation in question. Cybergeo: art. 916. doi: 10.4000/cybergeo.32917.
  • Richert, C., K. Erdlenbruch, and C. Figuières. 2017. The determinants of households’ flood mitigation decisions in France—On the possibility of feedback effects from past investments. Ecological Economics 131:342–52. doi: 10.1016/j.ecolecon.2016.09.014.
  • Rufat, S. 2013. Spectroscopy of urban vulnerability. Annals of the Association of American Geographers 103 (3):505–25. doi: 10.1080/00045608.2012.702485.
  • Rufat, S. 2015. Towards a social and spatial risk perception framework. Cybergeo: art. 725. doi: 10.4000/cybergeo.27010.
  • Rufat, S., and W. W. Botzen. 2022. Drivers and dimensions of flood risk perceptions: Revealing an implicit selection bias and lessons for communication policies. Global Environmental Change 73:102465. doi: 10.1016/j.gloenvcha.2022.102465.
  • Rufat, S., A. Fekete, I. Armaş, T. Hartmann, C. Kuhlicke, T. Prior, T. Thaler, and B. Wisner. 2020. Swimming alone? Why linking flood risk perception and behavior requires more than “it’s the individual, stupid.” WIREs Water 7 (5):e1462. doi: 10.1002/wat2.1462.
  • Rufat, S., E. Tate, C. G. Burton, and A. S. Maroof. 2015. Social vulnerability to floods: Review of case studies and implications for measurement. International Journal of Disaster Risk Reduction 14:470–86. doi: 10.1016/j.ijdrr.2015.09.013.
  • Rufat, S., E. Tate, C. T. Emrich, and F. Antolini. 2019. How valid are social vulnerability models? Annals of the American Association of Geographers 109 (4):1131–53. doi: 10.1080/24694452.2018.1535887.
  • Ruin, I., J. C. Gaillard, and C. Lutoff. 2007. How to get there? Assessing motorists’ flash flood risk perception on daily itineraries. Environmental Hazards 7 (3):235–44. doi: 10.1016/j.envhaz.2007.07.005.
  • Siegrist, M., and J. Árvai. 2020. Risk perception: Reflections on 40 years of research. Risk Analysis: An Official Publication of the Society for Risk Analysis 40 (Suppl. 1):2191–2206. doi: 10.1111/risa.13599.
  • Slavikova, L. 2018. Effects of government flood expenditures: The problem of crowding-out. Journal of Flood Risk Management 11:95–104. doi: 10.1111/jfr3.12265.
  • Tausanovitch, C., and C. Warshaw. 2013. Measuring constituent policy preferences in congress, state legislatures, and cities. The Journal of Politics 75 (2):330–42. doi: 10.1017/S0022381613000042.
  • Wachinger, G., O. Renn, C. Begg, and C. Kuhlicke. 2013. The risk perception paradox—Implications for governance and communication of natural hazards. Risk Analysis: An Official Publication of the Society for Risk Analysis 33 (6):1049–65. doi: 10.1111/j.1539-6924.2012.01942.x.
  • Wang, W., D. Rothschild, S. Goel, and A. Gelman. 2015. Forecasting elections with non-representative polls. International Journal of Forecasting 31 (3):980–91. doi: 10.1016/j.ijforecast.2014.06.001.
  • Wang, Y., M. Kyriakidis, and V. N. Dang. 2021. Incorporating human factors in emergency evacuation—An overview of behavioral factors and models. International Journal of Disaster Risk Reduction 60:102254. doi: 10.1016/j.ijdrr.2021.102254.
  • Wang, Z., N. S. N. Lam, N. Obradovich, and X. Ye. 2019. Are vulnerable communities digitally left behind in social responses to natural disasters? Evidence from Hurricane Sandy with Twitter data. Applied Geography 108:1–8. doi: 10.1016/j.apgeog.2019.05.001.
  • Ward, P. J., V. Blauhut, N. Bloemendaal, J. E. Daniell, M. C. de Ruiter, M. J. Duncan, R. Emberson, S. F. Jenkins, D. Kirschbaum, M. Kunz, et al. 2020. Review article: Natural hazard risk assessments at the global scale. Natural Hazards and Earth System Sciences 20 (4):1069–96. doi: 10.5194/nhess-20-1069-2020.
  • Warshaw, C., and J. Rodden. 2012. How should we measure district-level public opinion on individual issues? The Journal of Politics 74 (1):203–19. doi: 10.1017/S0022381611001204.
  • Zhang, X., J. B. Holt, H. Lu, A. G. Wheaton, E. S. Ford, K. J. Greenlund, and J. B. Croft. 2014. Multilevel regression and poststratification for small-area estimation of population health outcomes: A case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system. American Journal of Epidemiology 179 (8):1025–33. doi: 10.1093/aje/kwu018.
  • Zhang, X., J. B. Holt, S. Yun, H. Lu, K. J. Greenlund, and J. B. Croft. 2015. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system. American Journal of Epidemiology 182 (2):127–37. doi: 10.1093/aje/kwv002.
  • Zou, L., N. S. N. Lam, H. Cai, and Y. Qiang. 2018. Mining Twitter data for improved understanding of disaster resilience. Annals of the American Association of Geographers 108 (5):1422–41. doi: 10.1080/24694452.2017.1421897.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.