Networks and Spatial Economics

, Volume 17, Issue 2, pp 645–680 | Cite as

Heterogeneity Within and Across Households in Hurricane Evacuation Response

  • David S. Dixon
  • Pallab Mozumder
  • William F. Vásquez
  • Hugh Gladwin
Article
  • 244 Downloads

Abstract

A survey of Houston-area households reveals responses to Hurricane Ike in 2008 were as diverse as the households themselves. Review of evacuation literature shows this remains a fundamental problem. In our analysis no clear correlations between household attributes and evacuation motivators emerge unless the respondents are organized into subpopulations based on household attributes and the stated concerns of survey respondents. These subpopulations overlap so that most households fall within multiple classifications, evidence that heterogeneity across households is also present within them. To address heterogeneity within households, an information content metric (information entropy) is considered a proxy for issue saliency. Focusing on the most salient responses to survey questions makes it possible to isolate some of the factors important in the decision to evacuate and the characteristics of the households for which those factors are most important. Regression analysis of the most salient issues of the most concerned respondents informs the creation of behavioral rules for an agent-based model populated with the survey data. The relative strengths of the risk-averting behavior rules are tuned through Monte Carlo simulations using the actual evacuation time of each household as the fitness metric.

Keywords

Hurricane Natural disaster Evacuation Survey Information theory Network game theory Agent-based modeling 

References

  1. Baker EJ (1979) Predicting response to hurricane warnings: a reanalysis of data from four studies. Mass emergencies 4(1):9–24Google Scholar
  2. Baker EJ (1991) Hurricane evacuation behavior. International Journal of Mass Emergencies and Disasters 9(2):287–310Google Scholar
  3. Bateman JM, Edwards B (2002) Gender and evacuation: a closer look at why women are more likely to evacuate for hurricanes. nat Hazard Rev 3(3):107–117CrossRefGoogle Scholar
  4. Berg R (2014) National Hurricane Center Tropical Cyclone Report on Hurricane Ike (AL092008), updated March 18, 2014. http://www.nhc.noaa.gov/data/tcr/AL092008_Ike.pdf, [Online; accessed 10-July-2016]
  5. Bish DR, Chamberlayne EP, Rakha HA (2013) Optimizing network flows with Congestion-Based flow reductions. Networks and Spatial Economics 13 (3):283–306CrossRefGoogle Scholar
  6. Cahyanto I, Pennington-Gray L, Thapa B, Srinivasan S, Villegas J, Matyas C, Kiousis S (2016) Predicting information seeking regarding hurricane evacuation in the destination. Tour Manag 52:264–275CrossRefGoogle Scholar
  7. Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334CrossRefGoogle Scholar
  8. Dash N, Gladwin H (2007) Evacuation decision making and behavioral responses: individual and household. nat Hazard Rev 8(3):69–77CrossRefGoogle Scholar
  9. Dow K, Cutter SL (1998) Crying wolf: repeat responses to hurricane evacuation orders. Coast Manag 26(4):237–252CrossRefGoogle Scholar
  10. FEMA (2008) Hurricane Ike Impact Report - December 2008. Federal Emergency Management AgencyGoogle Scholar
  11. Galeotti A, Goyal S, Jackson MO, Vega-Redondo F (2010) Network games. Rev Econ Stud 77(1):218– 244CrossRefGoogle Scholar
  12. Gladwin H, Peacock WG (1997) Warning and evacuation: a night for hard houses. Hurricane Andrew: Gender, ethnicity and the sociology of disasters:52–74Google Scholar
  13. Hasan S, Ukkusuri SV (2011) A threshold model of social contagion process for evacuation decision making. Transportation research part B: methodological 45 (10):1590–1605CrossRefGoogle Scholar
  14. Hasan S, Ukkusuri S, Gladwin H, Murray-Tuite. P (2011) Behavioral Model to Understand Household-Level Hurricane Evacuation Decision Making. J Transp Eng 137(5)Google Scholar
  15. He X, Peeta S (2014) Dynamic resource allocation problem for transportation network evacuation. Networks and Spatial Economics 14(3):505–530CrossRefGoogle Scholar
  16. Horney JA, MacDonald PDM, Van Willigen M, Berke PR, Kaufman JS (2010) Individual actual or perceived property flood risk: Did it predict evacuation from Hurricane Isabel in North Carolina, 2003?. Risk Anal 30(3):501–511Google Scholar
  17. Houston-Galveston Area Council (2013) Brazoria, Chambers, Galveston, Harris and Matagorda Hurricane Evacuation Zip-Zones Coastal, A, B, C. http://www.hcoem.org/Documents/EvacuationMap.pdf[Online; accessed 15-November-2014]
  18. Huang S-K, Lindell MK, Prater CS (2015) Who leaves and who stays? a review and statistical meta-analysis of hurricane evacuation studies. Environ Behav 0013916515578485Google Scholar
  19. Huang S-K, Lindell MK, Prater CS, Wu H-C, Siebeneck LK (2012) Household evacuation decision making in response to Hurricane Ike. nat Hazard Rev 13(4):283–296CrossRefGoogle Scholar
  20. Lazo JK, Bostrom A, Morss RE, Demuth JL, Lazrus H (2015) Factors affecting hurricane evacuation intentions. Risk Anal 35(10):1837–1857CrossRefGoogle Scholar
  21. Li J, Ozbay K (2015) Evacuation planning with endogenous transportation network degradations: a stochastic cell-based model and solution procedure. Networks and Spatial Economics 15(3):677–696CrossRefGoogle Scholar
  22. Lindell MK, Brandt CJ (1997) Measuring interrater agreement for ratings of a single target. Appl Psychol Meas 21(3):271–278CrossRefGoogle Scholar
  23. Lindell MK, Brandt CJ (1999) Assessing interrater agreement on the job relevance of a test: a comparison of CVI, T, r WG(J), and \(r^{*}_{WG(J)}\)indexes. J Appl Psychol 84(4):640CrossRefGoogle Scholar
  24. Lindell MK, Perry RW (2012) The protective action decision model: theoretical modifications and additional evidence. Risk Anal 32(4):616–632CrossRefGoogle Scholar
  25. Lindell MK, Brandt CJ, Whitney DJ (1999) A revised index of interrater agreement for multi-item ratings of a single target. Appl Psychol Meas 23(2):127–135CrossRefGoogle Scholar
  26. Lindell MK, Prater CS (2008) Behavioral analysis: Texas hurricane evacuation study. Texas A&M University Hazard Reduction & Recovery Center, College Station TXGoogle Scholar
  27. Moore HE, Bates FL, Layman MV, Parenton VJ (1963) Before the wind. A study of the response to hurricane Carla. National Academy of Sciences, National Research CouncilGoogle Scholar
  28. Morrow BH, Gladwin H (2005) Hurricane Ivan behavioral analysis, Federal Emergency Management Agency and US Army Corps of EngineersGoogle Scholar
  29. Ng M, Behr J, Diaz R (2014) Unraveling the evacuation behavior of the medically fragile population: findings from hurricane Irene. Transportation research part A: policy and practice 64:122–134Google Scholar
  30. Ng M, Diaz R, Behr J (2015) Departure time choice behavior for hurricane evacuation planning: the case of the understudied medically fragile population. Transportation research part E: logistics and transportation review 77:215–226CrossRefGoogle Scholar
  31. Petrolia DR, Bhattacharjee S, Hanson TR (2010) Heterogeneous evacuation responses to storm forecast attributes. nat Hazard Rev 12(3):117–124CrossRefGoogle Scholar
  32. Riad JK, Norris FH, Ruback RB (1999) Predicting evacuation in two major disasters: risk perception, social influence, and access to resources. J Appl Soc Psychol 29(5):918–934CrossRefGoogle Scholar
  33. Shannon CE, Weaver W (1948) A mathematical theory of communicationGoogle Scholar
  34. Solís D, Thomas M, Letson D (2010) An empirical evaluation of the determinants of household hurricane evacuation choice. Journal of Development and Agricultural Economics 2(5):188–196Google Scholar
  35. TranStar (2008) Transtar Annual Report for 2008Google Scholar
  36. Ukkusuri SV, Hasan S, Luong B, Kien D, Zhan X, Murray-Tuite P, Yin W (2016) A-RESCUE: An Agent based regional evacuation simulator coupled with user enriched behavior. Networks and Spatial Economics:1–27Google Scholar
  37. Weller SC, Baer R, Prochaska J (2016) Should I stay or should I go? Response to the hurricane Ike evacuation order on the Texas gulf coast. nat Hazard Rev 04016003Google Scholar
  38. Whitehead JC, Edwards B, Van Willigen M, Maiolo JR, Wilson K, Smith KT (2000) Heading for higher ground: factors affecting real and hypothetical hurricane evacuation behavior. Global Environ Change B Environ Hazard 2(4):133–142Google Scholar
  39. Widener MJ, Horner MW, Metcalf SS (2012) Simulating the effects of social networks on a population’s hurricane evacuation participation. J Geogr Syst:1–17Google Scholar
  40. Wilensky U (1999) NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern UniversityGoogle Scholar
  41. Windham GO, Posey EI, Ross PJ, Spencer BG (1977) Reactions to storm threat during Hurricane Eloise, Mississippi State University Social Science Research CenterGoogle Scholar
  42. Yao T, Mandala RD, Chung BD (2009) Evacuation transportation planning under uncertainty: a robust optimization approach. Networks and Spatial Economics 9(2):171CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • David S. Dixon
    • 1
  • Pallab Mozumder
    • 2
  • William F. Vásquez
    • 3
  • Hugh Gladwin
    • 4
  1. 1.Department of EconomicsUniversity of New MexicoAlbuquerqueUSA
  2. 2.Department of Earth and Environment and Department of EconomicsFlorida International UniversityMiamiUSA
  3. 3.Department of EconomicsFairfield UniversityFairfieldUSA
  4. 4.Department of Global and Sociocultural StudiesFlorida International UniversityMiamiUSA

Personalised recommendations