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Kyne–Donner Model of Authority’s Recommendation and Hurricane Evacuation Decisions: A Study of Hypothetical Hurricane Event in the Rio Grande Valley, Texas

  • Dean Kyne
  • William Donner
Article

Abstract

Many previous studies identified factors influencing hurricane evacuation decisions by testing the protective action decision model (PADM). This study further examines factors that affect the trust in authority’s recommendation and evacuation decision-making in a proposed Kyne–Donner Model. The model provides an understanding of the predictive factors influencing evacuation decision-making through the mediating factor of trust in authority’s recommendation. This study takes advantage of the structural equation modeling method to simultaneously test multi-stages of the model. There are factors, namely, age, gender, education, household size, decision maker, risk area, house materials, hurricane evacuation experience, information seeking frequency, information seeking behavior, and information sources which influence trust in authority’s recommendation which, together with hurricane evacuation impediments, influence the hurricane evacuation decision. The study’s findings are consistent with the PADM and demonstrate the importance of trust in authority’s recommendation and hurricane evacuation decision-making.

Keywords

Evacuation Kyne–Donner model Protective action decision model Rio Grande Valley (RGV) Authority’s recommendation 

References

  1. Baker, E. J. (1991). Hurricane Evacuation Behavior. International Journal of Mass Emergencies and Disasters, 9(2), 24. http://www.ijmed.org/articles/412/.
  2. Baker, E. J. (1995). Public response to hurricane probability forecasts. Professional Geographer, 47(2), 137–147.  https://doi.org/10.1111/j.0033-0124.1995.00137.x.CrossRefGoogle Scholar
  3. Bateman, J. M. B., & Edwards, B. (2002). Gender and evacuation: A closer look at why women are more likely to evacuate for hurricanes. Natural Hazards Review, 3(3), 11.  https://doi.org/10.1061/(ASCE)1527-6988(2002)3:3(107).CrossRefGoogle Scholar
  4. Brackenridge, S., Zottarelli, L. K., Rider, E., & Carlsen-Landy, B. (2012). Dimensions of the human-animal bond and evacuation decisions among pet owners during hurricane Ike. Anthrozoos, 25(2), 229–238.  https://doi.org/10.2752/175303712x13316289505503.CrossRefGoogle Scholar
  5. Burnside, R., Miller, D. S., & Rivera, J. D. (2007). The impact of information and risk perception on the hurricane evacuation decision-making of greater new orleans residents. Sociological Spectrum, 27(6), 727–740.  https://doi.org/10.1080/02732170701534226.CrossRefGoogle Scholar
  6. Cahyanto, I., Pennington-Gray, L., Thapa, B., Srinivasan, S., Villegas, J., Matyas, C., et al. (2014). An empirical evaluation of the determinants of tourist’s hurricane evacuation decision making. Journal of Destination Marketing & Management, 2(4), 253–265.  https://doi.org/10.1016/j.jdmm.2013.10.003.CrossRefGoogle Scholar
  7. Czajkowski, J. (2011). Is it time to go yet? Understanding household hurricane evacuation decisions from a dynamic perspective. Natural Hazards Review, 12(2), 72–84.  https://doi.org/10.1061/(Asce)Nh.1527-6996.0000037.CrossRefGoogle Scholar
  8. DeYoung, S. E., Wachtendorf, T., Davidson, R. A., Xu, K., Nozick, L., Farmer, A. K., et al. (2016). A mixed method study of hurricane evacuation: Demographic predictors for stated compliance to voluntary and mandatory orders. Environmental Hazards-Human and Policy Dimensions, 15(2), 95–112.  https://doi.org/10.1080/17477891.2016.1140630.CrossRefGoogle Scholar
  9. Donner, W. R. (2007). The political ecology of disaster: An analysis of factors influencing U.S. Tornado fatalities and injuries, 1998–2000. Demography, 44(3), 669–685.  https://doi.org/10.1353/dem.2007.0024.CrossRefGoogle Scholar
  10. Donner, W., & Rodriguez, H. (2008). Population composition, migration and inequality: The influence of demographic changes on disaster risk and vulnerability. Social Forces, 87(2), 1089–1114.CrossRefGoogle Scholar
  11. FEMA. (2017). How to prepae for a hurricane: Federal Emergency Management AgencyGoogle Scholar
  12. Gladwin, H., & Peacock, W. G. (1997). Warning and evacuation: A night for hard houses. In B. H. Morrow & H. Gladwin (Eds.), Hurricane Andrew: Gender, ethnicity and the sociology of disasters (pp. 52–74). New York: Routledge.Google Scholar
  13. Gladwin, C., Gladwin, H., & Peacock, W. G. (2001). Modeling hurricane evacuation decisions with ethnographic methods. International Journal of Mass Emergencies and Disasters, 19(2), 117–143.Google Scholar
  14. Gudishala, R., & Wilmot, C. (2013). Predictive quality of a time-dependent sequential logit evacuation demand model. Transportation Research Record, 2376, 38–44.  https://doi.org/10.3141/2376-05.CrossRefGoogle Scholar
  15. Hasan, S., Ukkusuri, S., Gladwin, H., & Murray-Tuite, P. (2011). Behavioral model to understand household-level hurricane evacuation decision making. Journal of Transportation Engineering-Asce, 137(5), 341–348.  https://doi.org/10.1061/(asce)te.1943-5436.0000223.CrossRefGoogle Scholar
  16. Huang, S.-K., Lindell Michael, K., & Prater Carla, S. (2017a). Multistage model of hurricane evacuation decision: Empirical Study of Hurricanes Katrina and Rita. Natural Hazards Review, 18(3), 05016008.  https://doi.org/10.1061/(ASCE)NH.1527-6996.0000237.CrossRefGoogle Scholar
  17. Huang, S.-K., Lindell, M. K., & Prater, C. S. (2016). Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environment and Behavior, 48(8), 991–1029.  https://doi.org/10.1177/0013916515578485.CrossRefGoogle Scholar
  18. Huang, S.-K., Lindell, M. K., Prater, C. S., Wu, H. C., & Siebeneck, L. K. (2012). Household evacuation decision making in response to hurricane Ike. Natural Hazards Review, 13(4), 283–296.  https://doi.org/10.1061/(Asce)Nh.1527-6996.0000074.CrossRefGoogle Scholar
  19. Huang, S.-K., Wu, H.-C., Lindell, M. K., Wei, H.-L., & Samuelson, C. D. (2017b). Perceptions, behavioral expectations, and implementation timing for response actions in a hurricane emergency. Natural Hazards, 88(1), 533–558.  https://doi.org/10.1007/s11069-017-2877-4.CrossRefGoogle Scholar
  20. Kailiponi, P. (2010). Analyzing evacuation decisions using multi-attribute utility theory (MAUT). 1st Conference on Evacuation Modeling and Management. 3:163–174.  https://doi.org/10.1016/j.proeng.2010.07.016 CrossRefGoogle Scholar
  21. Kolen, B. (2016). Risk based decision making for evacuation in an uncertain world. In M. Lang, F. Klijn, & P. Samuels (Eds.), 3rd European conference on flood risk management (Vol. 7). Cedex A: E D P Sciences.Google Scholar
  22. Lazo, J. K., Bostrom, A., Morss, R. E., Demuth, J. L., & Lazrus, H. (2015). Factors affecting hurricane evacuation intentions. Risk Analysis, 35(10), 1837–1857.  https://doi.org/10.1111/risa.12407.CrossRefGoogle Scholar
  23. Lazo, J. K., Waldman, D. M., Morrow, B. H., & Thacher, J. A. (2010). Household evacuation decision making and the benefits of improved hurricane forecasting: Developing a framework for assessment. Weather and Forecasting, 25(1), 207–219.  https://doi.org/10.1175/2009waf2222310.1.CrossRefGoogle Scholar
  24. LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 815–852.  https://doi.org/10.1177/1094428106296642.CrossRefGoogle Scholar
  25. Lim, M. B. B., Lim, H. R., Piantanakulchai, M., & Uy, F. A. (2016). A household-level flood evacuation decision model in Quezon City, Philippines. Natural Hazards, 80(3), 1539–1561.  https://doi.org/10.1007/s11069-015-2038-6.CrossRefGoogle Scholar
  26. Lindell Michael, K., Lu, J.-C., & Prater Carla, S. (2005). Household decision making and evacuation in response to hurricane Lili. Natural Hazards Review, 6(4), 171–179.  https://doi.org/10.1061/(ASCE)1527-6988(2005)6:4(171).CrossRefGoogle Scholar
  27. Lindell, M. K., & Perry, R. W. (1992). Behavioral foundations of community emergency planning. Washington, D.C.: Hemisphere Pub.Google Scholar
  28. Lindell, M. K., & Perry, R. W. (2004). Communicating environmental risk in multiethnic communities. Thousand Oaks: Sage Publications.Google Scholar
  29. Lindell, M. K., & Perry, R. W. (2012). The protective action decision model: Theoretical modifications and additional evidence. Risk Analysis, 32(4), 616–632.CrossRefGoogle Scholar
  30. Lindell, M. K., & Prater, C. S. (2007). A hurricane evacuation management decision support system (EMDSS). Natural Hazards, 40(3), 627–634.  https://doi.org/10.1007/s11069-006-9013-1.CrossRefGoogle Scholar
  31. Lovreglio, R., Ronchi, E., & Nilsson, D. (2016). An Evacuation decision model based on perceived risk, social influence and behavioural uncertainty. Simulation Modelling Practice and Theory, 66, 226–242.  https://doi.org/10.1016/j.simpat.2016.03.006.CrossRefGoogle Scholar
  32. Matyas, C., Srinivasan, S., Cahyanto, I., Thapa, B., Pennington-Gray, L., & Villegas, J. (2011). Risk perception and evacuation decisions of Florida tourists under hurricane threats: A stated preference analysis. Natural Hazards, 59(2), 871–890.  https://doi.org/10.1007/s11069-011-9801-0.CrossRefGoogle Scholar
  33. Murray-Tuite, P., Yin, W. H., Ukkusuri, S. V., & Gladwin, H. (2012). Changes in evacuation decisions between hurricanes Ivan and Katrina. Transportation Research Record, 2312, 98–107.  https://doi.org/10.3141/2312-10.CrossRefGoogle Scholar
  34. National Weather Service. (2017). Storm Report on Hurricane Dolly in the Rio Grande Valley and Deep South Texas: Update #2: National Weather Service.Google Scholar
  35. National Weather Service. (2017). Hurricane Preparedness. Rio Grande Valley: Hurricane History.Google Scholar
  36. NOAA. (2017). Hurricane Tracks. From National Oceanic and Atmospheric Administration (NOAA). https://www.arcgis.com/home/item.html?id=b3ef2f8b58f54edcb9346329f25475fc. Accessed 15 May 2017.
  37. Regnier, E. (2008). Public evacuation decisions and hurricane track uncertainty. Management Science, 54(1), 16–28.  https://doi.org/10.1287/mnsc.1070.0764.CrossRefGoogle Scholar
  38. Reniers, G. L. L., Audenaert, A., Ale, B. J. M., Pauwels, N., & Soudan, K. (2009). Making evacuation decisions by using a discrete-time approximation methodology. Journal of Hazardous Materials, 164(2–3), 490–496.  https://doi.org/10.1016/j.jhazmat.2008.08.049.CrossRefGoogle Scholar
  39. Solis, D., Thomas, M., & Letson, D. (2009). Determinants of household hurricane evacuation choice in Florida. Selected paper prepared for presentation at the Southern Agricultural Economics Association Annual Meeting, Atlanta, GA.Google Scholar
  40. Stein, R., Buzcu-Guven, B., Duenas-Osorio, L., Subramanian, D., & Kahle, D. (2013). How risk perceptions influence evacuations from hurricanes and compliance with government directives. Policy Studies Journal, 41(2), 319–342.  https://doi.org/10.1111/psj.12019.CrossRefGoogle Scholar
  41. USA Today. (2016). Hurricane Matthew by the numbers Retrieved 03/18/2017, from USA Today http://www.usatoday.com/story/news/nation-now/2016/10/06/hurricane-matthew-by-the-numbers-evacuations-death-toll-east-coast-deadly-hurricane/91673664/. Accessed 20 Oct 2017.
  42. Whitehead, J. C. (2005). Environmental risk and averting behavior: Predictive validity of jointly estimated revealed and stated behavior data. Environmental & Resource Economics, 32(3), 301–316.  https://doi.org/10.1007/s10640-005-4679-5.CrossRefGoogle Scholar
  43. Whitehead, J. C., Edwards, B., Van Willigen, M., Maiolo, J. R., Wilson, K., & Smith, K. T. (2000). Heading for higher ground: Factors affecting real and hypothetical hurricane evacuation behavior. Global Environmental Change Part B: Environmental Hazards, 2(4), 133–142.  https://doi.org/10.1016/S1464-2867(01)00013-4.CrossRefGoogle Scholar
  44. Wu, H. C., Lindell, M. K., & Prater, C. S. (2015). Strike probability judgments and protective action recommendations in a dynamic hurricane tracking task. Natural Hazards, 79(1), 355–380.  https://doi.org/10.1007/s11069-015-1846-z.CrossRefGoogle Scholar
  45. Wu, H. C., Lindell, M. K., Prater, C. S., & Samuelson, C. D. (2014). Effects of track and threat information on judgments of hurricane strike probability. Risk Analysis, 34(6), 1025–1039.  https://doi.org/10.1111/risa.12128.CrossRefGoogle Scholar
  46. Zhang, F. Q., Morss, R. E., Sippel, J. A., Beckman, T. K., Clements, N. C., Hampshire, N. L., et al. (2007). An in-person survey investigating public perceptions of and responses to hurricane Rita forecasts along the Texas Coast. Weather and Forecasting, 22(6), 1177–1190.  https://doi.org/10.1175/2007waf2006118.1.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Disaster Studies MA Program, Department of Sociology and AnthropologyUniversity of Texas Rio Grande ValleyEdinburgUSA

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