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Household Decision Making and Evacuation in Responseto Hurricane Lili
Michael K. Lindell1; Jing-Chein Lu2; and Carla S. Prater3
Abstract: This study collected data on the evacuation from Hurricane Lili to answer questions about households’ reliance on informationsources, the factors affecting their decisions to evacuate, the timing of their hurricane evacuation decisions, and the time it took them toprepare to evacuate. The results replicated previous findings on the sources of hazard information, evacuation concerns, and the timing ofevacuation decisions. In addition, they provide new information about evacuation preparation times and the finding that householdcharacteristics are uncorrelated with evacuation decision times or evacuation preparation times.
DOI: 10.1061/�ASCE�1527-6988�2005�6:4�171�
CE Database subject headings: Hurricanes; Evacuation; Traffic management; Risk management.
Introduction
Hurricane evacuations are becoming increasingly problematic be-cause of steady population growth on the Atlantic and Gulf coastscombined with the failure of transportation infrastructure to keeppace with this growth �Dow and Cutter 2002�. As a result, evacu-ations are increasingly characterized by traffic jams whose10–20 h waits certainly are annoying and can generate consider-able antagonism toward government officials. Moreover, commu-nities whose evacuation routes run parallel to surge-prone baysand rivers �e.g., Corpus Christi and Galveston, Texas� could ex-perience a massive loss of life if a storm makes landfall whilethousands of stranded motorists are caught in areas vulnerable tostorm surge, inland flooding, high wind, and tornadoes.
The need to better manage hurricane evacuations raises ques-tions about households’ reliance on information sources, the fac-tors affecting their decisions to evacuate, the timing of their hur-ricane evacuation decisions, and the time it takes them to prepareto evacuate. To address these questions, this study examines pre-vious research addressing these issues and formulates five hy-potheses. These hypotheses are then tested using data collectedafter the evacuations from Hurricane Lili. The article closes witha discussion of the consistency of the results with previous re-search and recommendations for future research.
Literature Review
Previous studies, such as the study by Prater et al. �2000� ofHurricane Bret, have found distinct differences in the extent to
1Professor, Hazard Reduction and Recovery Center, Texas A&MUniv., College Station, TX 77843-3137.
2Research Assistant, Hazard Reduction and Recovery Center, TexasA&M Univ., College Station, TX 77843-3137.
3Research Scientist, Hazard Reduction and Recovery Center, TexasA&M Univ., College Station, TX 77843-3137.
Note. Discussion open until April 1, 2006. Separate discussions mustbe submitted for individual papers. To extend the closing date by onemonth, a written request must be filed with the ASCE Managing Editor.The manuscript for this paper was submitted for review and possiblepublication on February 1, 2005; approved on April 26, 2005. This paperis part of the Natural Hazards Review, Vol. 6, No. 4, November 1, 2005.
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which risk area residents used various information sources. Theseresearchers reported television broadcasts �both national networksand local programming� were the most important source of hur-ricane information, followed by local radio, peers and local au-thorities. Local newspapers and the internet were the least impor-tant sources of hurricane information. The reason for this rankordering can be seen in Driscoll and Salwen’s �1996� study re-porting information sources in Miami during Hurricane Andrewwere differentiated with respect to expertise and trustworthiness.In terms of expertise, the sources were rank ordered television,then radio and newspapers, and finally peers �friends, relatives,neighbors, and co-workers�. However, the ordering of thesesources with respect to trustworthiness was television and radio,then peers, and finally newspapers. These two studies imply tele-vision is more highly utilized because it is considered to be highin expertise and trustworthiness, but they do leave some unan-swered questions. For example, Driscoll and Salwen �1996� didnot distinguish between national and local broadcasters and failedto address the role of local authorities. Moreover, neither thesestudies reported what types of demographic and geographic vari-ables are correlated with the utilization of hurricane informationsources.
Another important research question concerns the variablesthat are related to the evacuation decision itself. A comprehensivereview by Baker �1991� of hurricane evacuation research con-cluded demographic characteristics �sex, age, education, income,ethnicity, marital status, and the presence of children in thehome�, and previous experience �previous hazard impacts andprevious false alarms� have inconsistent correlations with hurri-cane evacuation. By contrast, he concluded hurricane evacuationis most strongly related to people’s prior perceptions of risk,storm-specific threat factors, the hazardousness of households’locations, the characteristics of the structures in which they live,and actions by local authorities.
Some of these determinants of evacuation were addressed byGladwin et al. �2001�, who developed an ethnographic modelidentifying a number of issues risk area residents consider whendeciding whether to evacuate from an approaching hurricane.These include awareness of living in an official evacuation zone,awareness that an evacuation order has been given for their zone,
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Other considerations include believing their homes would be un-safe places to stay in a hurricane, family members are agreed onthe necessity for evacuating, they have the physical ability toleave, evacuation will not require an excessive amount of timeand effort, they have made all necessary preparations to leave,and it is inadvisable to delay evacuation. Finally, people considerwhether there is a specific evacuation destination that will acceptall household members �including any medical patients or pets�,whether they have enough cash or credit to stay in commercialfacilities �if other alternatives are unavailable�, and whetherevacuation routes have adequate capacity to avoid traffic jams.The significance of these variables was recently supported byWilmot and Mei �2004�, who analyzed Louisiana resident’ deci-sions to evacuate from Hurricane Andrew. The results of theirlogistic regression analysis revealed evacuation was significantlymore likely among residents of mobile homes and significantlyless likely among residents of single family dwellings than amongresidents of multifamily dwellings. Martial status �divorced, sepa-rated, and widowed� was also positively related with evacuationas was age, proximity to water ��1.6 km or 1 mi� and receipt ofan official evacuation order.
These considerations are generally consistent with the Protec-tive Action Decision Model �PADM� �Lindell and Perry 1992,2004�, which emphasizes risk area residents’ information receiptfrom environmental cues and social sources and the resulting per-ceptions of threat derived from combining that information withtheir pre-existing beliefs based on past experience. The PADMalso calls attention to people’s perceptions of alternative protec-tive actions, especially efficacy, cost, and impediments, and infor-mation seeking behaviors intended to resolve uncertainty aboutthe situation and an appropriate protective action. One unan-swered question about this theory is the degree to which concernsabout an approaching storm and about evacuation can be pre-dicted by demographic and geographic variables. Specifically, dothese concerns vary with age, education, geographic location, andthe types of structures in which people live?
Finally, the need for computing hurricane evacuation time es-timates has long been recognized �Urbanik 1979�, but empiricalresearch on time-dependent evacuation demand has been largelyneglected �Wilmot and Mei 2004�. Indeed, only a very limitedamount of research has attempted to estimate specific evacuationtime components such as decision, warning, preparation, and re-sponse times �Urbanik et al. 1980; Urbanik 2000�. This evacua-tion time component model was adopted by Lindell et al. �2001�,who collected data on Texas coastal residents’ expectations aboutthe length of time it would take them to perform six evacuationpreparation tasks ranging from preparing to leave work throughsecuring the home and leaving. The resulting times for these sixtasks were summed to generate a distribution �across households�of preparation times that were later used along with a distributionof expected warning times and an evacuation transportationmodel to generate hurricane evacuation time estimates for Texascoastal counties �Lindell et al. 2002a, 2002b�. A major limitationof these preparation time estimates is that they are based oncoastal residents’ expectations rather than reports of how long itactually took them to perform each of the six tasks.
In addition to generating estimates of evacuation time compo-nents, it also is important to determine what variables predictevacuation time components. This issue appears to have beenaddressed only by Sorensen �1991�, who sought to explain varia-tion in households’ times of warning receipt and preparation timesduring an evacuation precipitated by a fire at a toxic chemical
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proximity to the plant �official warnings were first issued tohouseholds closest to the plant�, type of housing structure �resi-dents of single family homes were warned earlier than apartmentresidents, which were warned earlier than mobile home resi-dents�, and warning system type �sirens produced earlier warningsthan mobile loudspeakers, which produced earlier warnings thandoor-to-door notifications�. Preparation times were significantlypredicted only by personalization of the warning source �peerwarnings produced less preparation time than authorities, whichproduced less time than the news media or sirens�. Unfortunately,it does not appear that this type of research has been replicated onany other evacuation studies, let alone in hurricane evacuationstudies.
In summary, previous research supports five hypotheses abouthurricane evacuations.• H1: Risk area residents rely on some information sources more
than others and the order is local news media�national news media�peers� local authorities� internet.There are no specific predictions about what demographic andgeographic variables predict reliance on information sources.
• H2: Risk area residents are more concerned about some infor-mation types more than others and the order is environmentalcues�social cues�personal experience�evacuation impedi-ments. There are no specific predictions about what demo-graphic and geographic variables predict concern about infor-mation types.
• H3: Hurricane evacuation decisions are predicted by coastalproximity, building structure type �mobile home, single family,multifamily�, information sources, and �absence of� evacuationimpediments.
• H4: The timing of the hurricane evacuation decision is pre-dicted by the same variables as the evacuation decision andalso by time of day �i.e., the evacuation rate is higher in themorning if the storm track is stable enough to provide ad-equate forewarning of the need to evacuate�.
• H5: Hurricane evacuation preparation time can be defined bythe time needed to prepare to leave from work, travel fromwork to home, gather all persons who would evacuate, packitems needed while gone, protect property from storm damage,shut off utilities, secure the home, and reach the main evacu-ation route. The preparation time distribution will be similar tothat estimated by Lindell et al. �2001�, which ranged fromapproximately 60 to 450 min, with a mean of 229.9 and astandard deviation of 85.2.
Method
Hurricane Lili originated off the west coast of Africa on Septem-ber 16, 2002, traveled steadily westward, and was upgraded to ahurricane on the 30th. Lili maintained a relatively steady north-west track and forward movement speed as it traveled over theGulf of Mexico toward the central Texas coast from September 28until midafternoon on October 2, intensifying to a Category 4hurricane. The National Hurricane Center �NHC� issued a hurri-cane watch for the coast between St. Louis Pass, Tex. and themouth of the Mississippi River at 4:00 p.m. central daylight time�CDT� on Tuesday, October, 1 and a hurricane warning for thecoast between High Island, Tex. and the mouth of the MississippiRiver at 4:00 a.m. CDT on Wednesday, October 2. These stormconditions prompted local officials to issue an early evacuationadvisory the night before the NHC hurricane warning was issued.
Shortly afterward, Lili veered toward the NNW and dropped to aASCE license or copyright; see http://pubs.asce.org/copyright
Category 1 hurricane with 128 kph �80 mph� wind before makinglandfall on the south central coast of Louisiana at 10:00 a.m. CDTon Thursday, October 3. Lili produced a storm tide that reached3.7 m �12 ft� in some locations, 10–20 cm �4–8 in.� of rainfallnear the coast, and a few short-lived tornadoes of F0–F1 magni-tude �less than 180 kph �112 mph��.
A mail survey of households in the Louisiana parishes of Ver-milion and Cameron and the Texas counties of Orange, Jefferson,and Chambers was conducted 6 months after the hurricane. Thesurvey sampling procedure was designed to yield 200 householdsin each of the five parishes/counties. A list of randomly samplednames from each ZIP code was generated to approximate thedesired number of households within each county. Following theprocedure by Dillman �1978�, each member of the sample wassent a packet containing a questionnaire and those members of thesample who did not return a completed questionnaire within 3weeks were sent a second packet. This process was repeated untilnonrespondents had been sent three packets. A total of 507 house-holds returned usable questionnaires for a response rate of 50.7%,which exceeds the 31–52% range obtained by Mileti and Fitz-patrick �1993� and is much higher than the response rates ob-tained in recent HRRC studies of coastal residents �25.8% byPrater et al. �2000�; 22.4% by Lindell et al. �2001��.
The first five items in the questionnaire assessed the extent towhich each respondent relied on local authorities, local newsmedia, national news media, the internet, and peers. The secondset of items assessed the extent to which they had considered fourtypes of issues in making their evacuation decisions. These in-cluded environmental cues �proximity to the coast, proximity toinland water, and storm conditions�, social cues �seeing busi-nesses closing, seeing peers evacuating, hearing announcementsof watches and warnings, and hearing local officials issue officialevacuation recommendations�, personal experience �previous ex-perience with hurricanes and previous experience with an unnec-essary evacuation�, and evacuation impediments �protectinghomes from looters, protecting homes from storm impact, loss ofincome while evacuated, evacuation expenses, and being stuck intraffic during storm landfall�. Each of these items was rated on a1 �=Not at all� to 5 �=Very great extent� scale. Respondents alsowere asked if they evacuated �1=Yes�. The time at which respon-dents made their evacuation decision was assessed by askingthem to report the day �Tuesday=0, Wednesday=1, orThursday=2� and time �on a 24 h clock� of this decision. Theresponses to these two questions were converted to a single vari-able by multiplying the numerical code for the day by 24 andadding the time of day. This produced a variable that ranged from0 to 72 h �the zero point was midnight on Tuesday, October 1,which preceded the evacuation decision time of any respondent�.
Respondents were asked to report the amount of time it took toperform six evacuation preparation tasks: prepare to leave fromwork; travel from work to home; gather all persons who wouldevacuate with you; pack items you would need while gone; pro-tect property from storm damage; shut off utilities, secure thehome and leave. In addition, respondents were asked to report theamount of time it took to reach the main evacuation route and thelength of time it took for them to reach their final destination.These items were classified into six categories—1=didn’t do,2=1–15 min, 3=16–30 min, 4=31–45 min, 5=46–60 min, and6=61 min or more. During data processing, Category 1 was re-coded to zero minutes and each of the intermediate categories wasrecoded to the midpoint of the range for that category �i.e., Cat-egory 2, 1–15 min., was recoded as 8 min�. The last category,
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In addition, respondents were asked to report their age, sex,ethnicity, marital status, education, and annual household income.They also were asked the number of persons in the household andthe number of these who were children under the age of 17.Finally, they were asked whether they owned or rented theirhomes and what type of building this was �detached single family,multifamily one or two stories, multifamily three or more stories,mobile or manufactured home, or other�.
The questionnaire respondents were predominantly male�53%�, White �80.5%�, middle-aged �arithmetic mean,M =54.4 years� married �68.9%� homeowners �95%�. The house-holds averaged 2.72 persons, had an average of 0.64 childrenunder the age of 18 years of age, and resided in a single familydetached structure �72.6%�. The five parishes/counties vary some-what in their demographic characteristics, but the respondents canbe said to be generally representative of the region in terms ofgender, household size, and number of children, but older andhaving a larger proportion of Whites and homeowners.
Each respondent’s mailing address was entered into an ArcInfodatabase unless it was a location other than a street address �e.g.,a Post Office box�. Next, addresses were geocoded into latitude/longitude coordinates that were used to compute each household’sdistances from the coast, the nearest river, and the nearest lake�most lakes in this area are saltwater estuaries whose connectionto the sea provides a route for storm surge�.
Results
In partial support of H1, there were significant variations in thedegree to which respondents reported using the hurricane infor-mation sources. As Table 1 indicates, people reported relyingmost on local news media �M =4.27� and somewhat less on na-tional news media �M =3.48�. However, they relied more on localauthorities �M =3.08� than on peers �M =2.85�, although they didrely least on the internet �M =1.84�. There were no significantcorrelations of demographic variables with utilization of localmedia or local authorities as information sources, but the geo-graphic variables were significantly related. Specifically, thosewhose houses were located closer to the coast �r=0.21�, rivers�r=0.24�, or lakes �r=0.19� were more likely to rely on localauthorities. There were a few significant correlations of demo-graphic variables with utilization of national media, internet, andpeers as information sources, but no consistent pattern emergedfrom these correlations. However, the geographic variables didhave consistently significant correlations with reliance on the in-ternet; those who were closer to the coast �r=0.28�, rivers�r=0.19�, or lakes �r=0.24� were more likely to rely on thissource.
In partial support of H2, respondents reported consideringmost of the evacuation issues to a moderate or great extent. How-ever, there was a significant degree of variation in the importanceof specific issues within each of the four categories. The greatestconsideration was given to environmental cues, with proximity tothe coast �M =4.06�, proximity to inland waterways �M =3.59�,and storm conditions �M =3.51� receiving generally high ratings.The personal experience variables were the next most important,although there were differences among the issues in the extent towhich they were considered by the respondents. Previous hurri-cane experience �M =3.74� was a major issue, whereas previousexperience of unnecessary evacuations �M =2.87� was rated as amuch less significant concern.
A significant degree of consideration also was given to social
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Table 1. Means �M�, Standard Deviations �SD�, and Correlations among Variables
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1. Locauth 3.08 1.55 1.00
2. Locmedia 4.27 1.05 0.30** 1.00
3. Natmedia 3.48 1.40 0.15** 0.30** 1.00
4. Internet 1.84 1.30 0.24** 0.14** 0.25** 1.00
5. Peers 2.85 1.37 0.22** 0.20** 0.10** 0.15** 1.00
6. Closcoast 4.06 1.21 0.35** 0.26** 0.13** 0.21** 0.23** 1.00
7. Closwater 3.59 1.46 0.23** 0.16** 0.13** 0.13** 0.23** 0.60** 1.00
8. Strmcond 3.51 1.40 0.19** 0.26** 0.08 0.07 0.27** 0.31** 0.40** 1.00
9. Busclos 2.55 1.35 0.29** 0.19** 0.08 0.10* 0.36** 0.26** 0.34** 0.51** 1.00
10. Peerevac 2.94 1.43 0.28 0.23** 0.09 0.13** 0.56** 0.28** 0.27** 0.50** 0.69** 1.00
11. Offwatch 3.59 1.20 0.30** 0.32** 0.24** 0.19** 0.32** 0.36** 0.35** 0.38** 0.45** 0.49** 1.00
12. Offevac 3.77 1.30 0.47** 0.31** 0.14** 0.14** 0.35** 0.45** 0.36** 0.37** 0.44** 0.52** 0.58** 1.00
13. Expstrm 3.74 1.27 0.16** 0.19** 0.11* 0.14** 0.06 0.31** 0.28** 0.28** 0.16** 0.17** 0.27** 0.20** 1.00
14. Falsealrm 2.87 1.48 0.08 0.07 0.03 0.11 0.06 0.12* 0.18** 0.14** 0.21** 0.13** 0.06 −0.01 0.28** 1.00
15. Protloot 2.65 1.51 0.12* 0.09 0.20** 0.04 0.17** 0.06 0.21** 0.22** 0.33** 0.28** 0.23** 0.17** 0.11* 0.38** 1.00
16. Protstrm 3.22 1.42 0.11* 0.18** 0.22** 0.03 0.24** 0.14** 0.25** 0.32** 0.38** 0.36** 0.35** 0.27** 0.19** 0.27** 0.57** 1.00
17. Lostinc 1.93 1.34 0.09 −0.08 0.10 0.08 0.18** 0.14** 0.20** 0.17** 0.28** 0.27** 0.19** 0.19** 0.09 0.25** 0.40** 0.38** 1.00
18. Evacexp 2.39 1.51 0.09 0.09 0.07 0.01 0.25 0.16** 0.18** 0.27** 0.36** 0.39** 0.27** 0.25** 0.09 0.20** 0.34** 0.38** 0.55** 1.00
19. Trafrisk 3.26 1.55 0.11* 0.11* 0.07 0.08 0.14** 0.14** 0.13** 0.16** 0.22** 0.22** 0.22** 0.17** 0.11 0.28** 0.31** 0.27** 0.30** 0.41** 1.00
20. Evacdec 1.54 0.50 0.21** 0.16** 0.10* 0.12* 0.25** 0.31** 0.18** 0.12** 0.16** 0.31** 0.30** 0.36** 0.07 −0.06 −0.04 0.05 0.02 0.14** 0.01 1.00
21. Dectim 29.39 13.43 −0.19** 0.01 0.08 −0.04 0.13 −0.06 −0.10 0.06 0.04 0.09 −0.08 −0.03 −0.12 −0.01 0.03 −0.01 −0.03 0.01 −0.01 −0.01 1.00
22. Preptim 261.3 108.2 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.08 0.08 0.08 0.08 0.08 0.08 1.00
Note: Sample sizes range from 206 to 447 because of varying patterns of item nonresponse. Locauth=local authorities; Locmedia=local newsmedia; Natmedia=national news media; Internet=internet;Peers=friends, relatives, neighbors, co-workers; Closcoast=proximity to coastline; Closwater=proximity to rivers and lakes; Strmcond=storm conditions; Busclos=saw businesses closing;Peerevac=saw peer evacuations; Offwatch=official hurricane watch; Offevac=official evacuation warning; Expstrm=previous experience with hurricanes; Falsealrm=previous experience with unnecessaryevacuation; Protloot=protect property from looting; Protstrm=protect property from storm damage; Lostinc=concern about lost income; Evacexp=concern about evacuation expense; Trafrisk=concernabout traffic accident risk; Evacdec=evacuated; Dectim=time at which the evacuation decision was made; and Preptim=total evacuation preparation time. *= p�0.05 and **= p�0.01
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cues such as official evacuation recommendations �M =3.77� andofficial watches and warnings �M =3.59�; observations of peersevacuating �M =2.94� and observations of businesses closing�M =2.55� received much less consideration. Evacuation impedi-ments generally received less consideration than other issues,with concerns about becoming trapped in a stalled evacuation�M =3.26� and protecting property from storm damage�M =3.22� drawing more attention than expectations of looting�M =2.65� and lost income �M =1.93�.
Overall, 53.6% of those surveyed evacuated from HurricaneLili, but there was significant variation by parish/county. Theevacuation rates were 66.1% in Vermilion Parish, the location oflandfall �an estimated 35,800 evacuees�; 86.8% in Cameron Par-ish �an estimated 8,400 evacuees�; 55.8% in Orange County �anestimated 47,100 evacuees�; 46.1% in Jefferson County �an esti-mated 114,700 evacuees�; and 11.7% in Chambers County �anestimated 3,200 evacuees�. In general, evacuation rates decreasedas the county’s distance from the predicted point of landfallincreased.
Also consistent with H3, evacuation decisions tended to bestrongly correlated with geographic characteristics—proximity torivers �r=0.21�, lakes �r=0.30�, and the coast �r=0.15�. How-ever, contrary to H3, there was a nonsignificant correlation be-tween residence in a mobile home and evacuation �r=0.05�, butBaker �1991� concluded that this variable would predict evacua-tion only for those outside the high-risk area. Consequently, ananalysis was conducted in which mobile home residence, distancefrom the coast, and the product of those two variables were en-tered into a logistic regression. The product variable had a non-significant coefficient �b=0.46, p=0.43, ns�, so it appears mobilehome residence had neither a direct nor an interactive effect onevacuations.
Consistent with H3, respondents reported relying on each ofthe hurricane information sources but the extent to which theyrelied on each of the hurricane information sources �reported inthe test of H1�, did not correspond precisely to the correlations ofthese variables with evacuation. Specifically, utilization of peers�r=0.24� and local authorities �r=0.21� had the highest correla-tions with evacuation. By contrast, utilization of local news media�r=0.16�, the Internet �r=0.12�, and national news media�r=0.10� had noticeably lower correlations with evacuation deci-sions.
Consistent with H3, many information concerns were signifi-cantly correlated with evacuation decisions. Two of the environ-mental cues, proximity to the coast �r=0.31� and proximity toinland waterways �r=0.18�, were more important than the third,storm conditions �r=0.12�. Three of the social cues—officialevacuation recommendations �r=0.36�, observations of peersevacuating �r=0.31�, and official watches and warnings�r=0.30�—were more important than the remaining one, observa-tions of businesses closing �r=0.16�.
Contrary to H3, personal experience and evacuation impedi-ments were not significantly correlated with evacuation decisions.The correlations for these variables were: becoming trapped in astalled evacuation �r=0.14�, previous hurricane experience�r=0.07�, previous experience of unnecessary evacuations�r=−0.06�, protecting property from storm damage �r=0.05�, ex-pectations of looting �r=0.04�, and lost income �r=0.02�. Alsocontrary to H3, only a few of the demographic characteristics—younger �r=0.15� female �r=0.14� respondents with children athome �r=0.23�—were significantly correlated with evacuationdecisions.
Consistent with H4, evacuation decisions are as strongly de-
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pendent on the time of day as on the imminence of hurricanelandfall. The proportion of evacuations was greatest just afterdaylight and decreased steadily throughout the day �see Fig. 1�. Inall, 29.1% of the evacuees made their decision to evacuate beforethe NHC issued a hurricane watch at 9:00 p.m. CDT on Tuesday,whereas 59.8% decided to leave before the NHC’s hurricanewarning at 9:00 a.m. CDT on Wednesday. Also consistent withH4, there were significant correlations with the geographicalvariables—distance from rivers �r=0.16�, lakes �r=0.15�, and thecoast �r=0.19�. These coefficients mean those who lived fartherfrom these sources of danger took longer to make their evacuationdecisions. However, contrary to H4, none of the demographiccharacteristics was significantly correlated with the time at whichrespondents made their evacuation decisions.
In partial support of H5, respondents’ reports on the time re-quired to perform the six evacuation preparation time componentswere: prepare to leave work �M =15.0 min�; travel from work tohome �M =7.9 min�, gather persons �M =30.2 min�; pack items�M =46.1 min�; protect property �M =38.8 min�; and secure thehome �M =33.6 min�. In addition, respondents reported thatreaching the main evacuation route took a significant amount oftime �M =24.6 min�, but the length of time it took for them toreach their final destination could not be estimated reliably be-cause 74.5% of the respondents checked the maximum category61 min or more.
The individual preparation time components were added toproduce a total preparation time for each household and the cu-mulative distribution of these preparation times for Hurricane Liliwas plotted as the dotted line in Fig. 2. The average time thatelapsed between the respondents’ decisions to evacuate and thetime they arrived at a major evacuation route was196.2 min—just over 3 h. However, 25% of the evacuees pre-pared in about 2 h and 95% took 6 h to prepare to leave. Consis-tent with H5, the Hurricane Lili preparation time data are quitesimilar to those for evacuation expectations data �Lindell et al.2001� that are described by the solid line rising slightly after andpeaking slightly before the Lili data. Consistent with H5, totalpreparation time had a significant negative correlation with dis-
Fig. 1. Distribution of evacuation decision times
Fig. 2. Cumulative distribution of evacuation preparation times
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tance from the coast �r=−0.20�, which was primarily due to theeffects of packing items �r=−0.20�, protecting property�r=−0.17�, and securing the home �r=−0.20�. That is, those wholived farther from the coast took less time to perform these tasks.However, contrary to H5, households’ evacuation preparationtimes were not significantly correlated with any of the informa-tion sources or evacuation concerns.
Discussion
The results of this study support previous findings by Prater et al.�2000� that local news media, especially television, are the mostextensively used source of hurricane information for risk arearesidents. However, this study extends those findings by revealingevacuation decisions were more strongly correlated with relianceon peers and local authorities than with local news media. Theseresults imply the extent to which a source is used is not the sameas the impact it has on evacuation decisions. This distinction canbe illustrated by noting a few minutes spent listening to a countysheriff’s deputy at the front door might affect households’ evacu-ation decisions more profoundly than many hours spent watchinghurricane coverage on television. The absence of any statisticallysignificant correlations between source utilization and demo-graphic characteristics means this conclusion applies to all seg-ments of the population. Nonetheless, there were significant cor-relations between source utilization and geographic location,which means those at greatest risk did tend to rely more heavilyon local news media and the internet than did other risk arearesidents.
These data on information sources are broadly consistent witha review by Baker �1991� of hurricane evacuation studies, whichconcluded evacuation is not strongly correlated with the primarysource of information about storm conditions, the source of initialinformation about the hurricane, or the intensity of storm moni-toring activities �frequency of media monitoring or maintaining atracking chart�. However, the Hurricane Lili data seem to suggestconclusions somewhat different from those of Dow and Cutter�1998�, who reported evacuees cited the Governor’s officialevacuation order �24.5 and 22.9% for the 1996 Hurricanes Berthaand Fran, respectively�, Weather Channel, National Weatherservice/local news �26.4 and 26.8%�, local officials warnings�11.3 and 5.2%, respectively�, and actions/advice from peers �6.6and 7.8%� as reasons for evacuating. Even though the data fromHurricanes Bertha and Fran seem to imply the opposite conclu-sions about the relative importance of local authorities and peersversus national and local news media that would be drawn fromthe Hurricane Lili data, closer examination of the data do not bearthis out. In fact, the data are not strictly comparable because Dowand Cutter �1998� combined national and local levels of newsmedia and collected these data only in free response format and,even then, only from evacuees. By contrast, the present studyseparated the two levels of news media and collected fixed-response data on source utilization from evacuees and nonevacu-ees. In any event, these studies, together with Wilmot and Mei�2004� indicate risk area residents rely substantially on officialevacuation warnings but other information sources—especiallythe news media—are important in helping them to determine forthemselves whether they need to evacuate.
The analyses of Hurricane Lili evacuation concerns reveal re-spondents considered environmental cues to be the most impor-tant issues �M =3.72�, followed by personal experience
�M =3.31�, then social cues �M =3.20�, and finally evacuation im-176 / NATURAL HAZARDS REVIEW © ASCE / NOVEMBER 2005
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pediments �M =2.77�. As was the case with the informationsources, ratings of the extent to which the concerns were consid-ered in the evacuation decision showed only moderate correspon-dence to the correlations with evacuation. On average, social cueshad the highest correlations with evacuation �average r=0.28�,followed by environmental cues �average r=0.20�, then evacua-tion impediments �average r=0.06�, and finally personal experi-ence �average r=0.01�. Even though the ratings of utilization didnot correspond exactly to correlations with evacuation, social andenvironmental cues were much more important than past experi-ence and evacuation impediments in determining evacuationdecisions.
These findings on evacuation concerns displayed some simi-larities to the results of previous studies. For example, Dow andCutter �1998� reported relatively few nonevacuees from Hurri-canes Bertha and Fran listed job requirements �7.0 and 8.2%�, andtraffic impediments �10.5 and 0%� as important reasons for decid-ing not to evacuate. Similarly, Dow and Cutter �2002� found onlya small proportion of the nonevacuees from Hurricane Floyd ex-pressed concern about the safety of their homes, evacuation traf-fic, and work responsibilities and Riad et al. �1999� reported fewnonevacuees listed inadequate social or economic resources toevacuate �10%� or believed they needed to stay to protect theirhomes �8%�. Most of these issues fall into the category of evacu-ation impediments, which showed the weakest correlation withevacuation from Hurricane Lili. Dow and Cutter �1998� reportedstructural safety of their homes �8.8 and 36.5%� was a somewhatmore important consideration for nonevacuees from HurricanesBertha and Fran. Similarly, Riad et al. �1999� found many non-evacuees from Hurricanes Hugo and Andrew were confident inthe safety of their homes �24%�. Thus, coastal residents’ confi-dence in the locational and structural safety of their homes seemsto be more significant than evacuation impediments in determin-ing their evacuation decisions, a conclusion consistent with thefindings of Aguirre �1991� and Wilmot and Mei �2004�. Nonethe-less, circumstantial evidence from Dow and Cutter �1998� sug-gests this conclusion would not apply to residents of barrier is-lands, who typically are subject to very high risk and extremelylimited evacuation routes.
It is noteworthy that those who were closer to the coast, rivers,or lakes were more likely to rely on local authorities because thisfinding is consistent with recent findings by Zhang et al. �2004�and Wilmot and Mei �2004�, but it is important to be aware of thecaution by Baker �1991� that those in high-risk areas receive moreattention from authorities. Moreover, the extent to which those atgreatest risk also received information from local media indicatesthis population segment is involved in active monitoring of thenews media as well as in passive information receipt from au-thorities. Moreover, the finding that these residents used the in-formation sources to a greater extent than residents of low-riskareas generally extended to evacuation decisions; residents ofareas closest to rivers, lakes, and the coast were more likely toevacuate. This is consistent with the conclusion by Baker �1991�that there are significant differences in evacuation rates amonghigh-risk locations on barrier islands or open coastlines �83%�,moderate-risk locations on the mainland that are 10–15 ft abovemean sea level �55–65%�, and low-risk areas above 15 ft meansea level �37%�.
In addition to these geographic correlates of evacuation, therealso were some demographic characteristics �younger female re-spondents with children in the home� that predicted decisions toevacuate. This result in consistent with the Riad et al. �1999�
finding of female respondents being more likely to report evacu-ASCE license or copyright; see http://pubs.asce.org/copyright
ating than male respondents but, contrary to Wilmot and Mei’s�2004� finding of older single respondents being more likely toevacuate. Contrary to the Riad et al. �1999� finding that Latinosand Whites are more likely than Blacks to evacuate from Hurri-canes Hugo and Andrew, however, the Hurricane Lili data showedno differences among ethnic groups in evacuation rates eventhough the present sample had an adequate degree of diversity.Specifically, 8.5% of the Hurricane Lili respondents were Black,8.1% were Native American, and 80.5% were White, althoughonly 1% were Hispanic. Moreover, Riad et al. �1999� reportedrenters were slightly more likely than homeowners to evacuate,but the Hurricane Lili data revealed no differences, quite possiblybecause the proportion of renters among the respondents was sosmall �5%�. The conflicting results in these recent studies supportsthe conclusion by Baker �1991� that demographic variables areunstable predictors of evacuation behavior.
Consistent with previous research, evacuation decisions weresignificantly correlated with observations of peer evacuations�r=0.31� and, to a lesser degree, businesses closing�r=0.16�. However, Baker �1991� noted a significant correlationbetween peer response and evacuation could be an artifact of riskarea and officials’ recommendations; areas at greater risk usuallyreceive official warnings to evacuate �with those at greatest riskbeing likely to receive face-to-face warnings�. Indeed, the Hurri-cane Lili data show observations of peer evacuations were signifi-cantly correlated with local authorities issuing evacuation recom-mendations �r=0.52� as well as observations of businessesclosing �r=0.69�. Because the response of peers is so highly cor-related with individuals’ personal assessments of risk and the as-sessments of authorities, evacuation was regressed onto officialwarning and proximity to the coast, and in the next step, obser-vation of peer evacuations. This analysis revealed observation ofpeer evacuations did have a statistically significant coefficient,indicating it made an independent contribution to predictingevacuation decisions, However, inclusion of this variable onlyincreased the prediction of evacuation from 73.6 to 74.9%, indi-cating the incremental effect of this variable was negligible.
The nonsignificant correlation of previous hurricane experi-ence with evacuation conflicts with the finding of Riad et al.�1999�, who reported evacuation experience was the single bestpredictor of evacuation in Hurricanes Hugo and Andrew. How-ever, it is consistent with the conclusion by Baker �1991� thatprevious personal experience has no consistent relationship tohurricane evacuation. As he noted, there are many methodologicalexplanations for the conflicting findings about the effect of expe-rience. Part of the reason for the conflicting results is experiencehas been measured in many different ways—as experience withany disaster, experience with a hurricane, the number of hurri-canes experienced, the most recent hurricane experienced, the se-verity of the hurricanes experienced, the amount of property dam-age experienced, and whether any family injuries wereexperienced. Another reason for the conflicting results is “false”experience in which people presume the maximum storm condi-tions reported in the news media �e.g., 150 mph wind� occurred intheir locations and, to the extent that their homes withstood thesestorm conditions, their structures would be able to withstand fu-ture storms of a similar intensity. Of course, the fallacy in thisreasoning is most people experience wind forces that are muchweaker than the maximum wind speeds reported in the newsmedia. Baker �1991� also attributed inconsistent findings regard-ing hurricane experience to sampling procedures restricting datacollection to the most severely impacted areas, thus producing
variance restriction in the dependent variable �almost everyoneDownloaded 04 Feb 2010 to 130.160.120.56. Redistribution subject to
evacuated� and making it almost impossible to find any variablesthat would correlate with evacuation. This artifact obviously didnot exist in the Hurricane Lili sample because only half of therespondents evacuated.
The nonsignificant correlation of experience with unnecessaryevacuations is consistent with the finding of Dow and Cutter�1998� that evacuations in South Carolina for Hurricanes Berthaand Fran were unrelated to previous experience. Indeed, theseresearchers reported the majority of respondents �76%� in theirsurvey either evacuated from both hurricanes or remained homefor both. Moreover, many more evacuated only from the later�and stronger� Hurricane Fran �21%� than evacuated only fromHurricane Bertha �3%�. Thus, the Hurricane Lili data support theconclusion by Baker �1991� of little evidence that evacuationrates are affected by previous experience, regardless of themethod by which it is measured. They also support the conclusionby Dow and Cutter �1998� that evacuation decisions are evenunaffected by experience involving an �ultimately unnecessary�evacuation.
The Hurricane Lili data also support the conclusion by Baker�1991� that people would rather leave during daylight hours eventhough evacuations have been implemented successfully afterdark. One half of those threatened by Hurricane Lili reportedmaking their decision to evacuate before noon on either Wednes-day or Thursday. Indeed, the desire to complete an evacuationduring daylight hours appears to be more important than a desirefor authoritative recommendations from technical experts be-cause, as noted earlier, two-thirds of the evacuees made theirdecision to leave before the NHC issued a hurricane warning. Inthis regard, the Hurricane Lili data are consistent with the reportby Dow and Cutter �2002� that 25% of the evacuees from Hurri-cane Floyd left within 2–5 h after the governor’s voluntaryevacuation advisory �declared at 7:00 a.m. on Tuesday, September14, 1999� and another 23% left within 2–5 h after the governor’smandatory evacuation order �declared 5 h later at noon�. The tim-ing of these departures led them to conclude many people hadprepared to leave long before the Governor’s orders were issued.
The early evacuations from Hurricane Lili were probably in-fluenced by its steady track and Category 4 intensity, which re-sulted in local officials advising coastal residents the night beforethe NHC issued its hurricane warning that an evacuation wouldbe initiated the next day. The resulting distribution of evacuationdecisions over time is quite different from that assumed by Lin-dell and his colleagues �2002� in computing evacuation time es-timates for the 22 Texas coastal counties. Their distribution,which was intended to represent a late evacuation initiation �e.g.,based upon a major change in hurricane track, forward movementspeed, or intensity�, was derived from data about the times atwhich evacuation warnings were received during the eruption ofMt. St. Helens �Lindell and Perry 1992�. The warning time dis-tribution used by Lindell et al. �2002� begins at the time an offi-cial evacuation warning is initiated, rises rapidly to 50% within15 min, and reaches 95% notification at 2.25 h. By contrast, thedecision time distribution observed during Hurricane Lili was al-ready at 60% by the time the NHC hurricane warning was issuedat 9:00 a.m. CDT on Wednesday but did not reach 95% �of thoseevacuating� until 6:00 a.m. the next day—21 h later. The mostimportant consequence of the early evacuation decisions duringHurricane Lili was to distribute the demand for evacuation routecapacity over a much longer period of time than that assumed inthe Texas coastal evacuation time estimates �Lindell et al. 2002�.In turn, this minimized the occurrence of traffic queues on evacu-
ation routes �Prater et al. 2004�. However, it is important to noteNATURAL HAZARDS REVIEW © ASCE / NOVEMBER 2005 / 177
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the flattened distribution of decision times observed in HurricaneLili would not be expected to apply to storms such as HurricaneBret, which made a 90° change in track in the last hours beforelandfall. Thus, the difference between the decision time distribu-tion assumed by Lindell et al. �2002� and the actual distributionobserved in Hurricane Lili underscores the need for emergencymanagers to recognize the differences among evacuation sce-narios and to understand how variation in the amount of fore-warning can influence coastal residents’ evacuation decisiontimes.
It is noteworthy that two of the preparation time components,prepare to leave from work �M =15.0 min� and travel from workto home �M =7.9 min�, were substantially shorter than the timesfor the other preparation time components. Moreover, as noted byKang et al. �2004�, these task performance times were substan-tially shorter than the estimates some of the respondents providedin an evacuation expectations survey conducted 2 years earlier.However, the discrepancy between the expectations and later be-havior during the hurricane appears to be attributable to the factthat most of the evacuations were initiated in the morning hoursbefore people left for work, which obviated the need to performthe tasks related to departure from work. This explanation is sup-ported by inspection of the category frequencies, which revealsthat 60.2% of the respondents checked the first category �“Didn’tdo”� for prepare to leave work and 61.8% of them checked thiscategory for travel from work to home.
The finding that those who lived farther from rivers, lakes, andthe coast took longer to make their evacuation decisions mightseem quite obvious in retrospect but, in fact, is not at all so.Rather, one might assume that information made available via themass media to all of those in the risk area would produce deci-sions to evacuate at about the same regardless of proximity to thesources of hazard. This would seem to have positive implicationsfor emergency managers because it means the evacuation of thosewho are at greatest risk would not be impeded by the evacuationsof those farther inland. Unfortunately, preparation times werenegatively related to distance from the coast, so residents of areasfarther inland took less time to prepare than did those at greatestrisk. The net result is for the effects of hazard proximity on deci-sion times and preparation times to cancel out, leaving departuretimes approximately random with respect to hazard proximity.That is, evacuations initiated with less forewarning are morelikely to be characterized by inland residents impeding the evacu-ation of those closer to the coast.
There were some limitations to this study of the Hurricane Lilievacuations. First, the response rate was only about 50%. Thoughthis is a relatively high response rate compared to other mailsurveys of environmental hazards, the overrepresentation of older,married homeowners raises questions about the degree to whichthe conclusions apply to other demographic categories such asyounger single renters. However, overrepresentation of some de-mographic categories will produce bias in other variables such asperceived stakeholder characteristics only to the degree the lattervariables are correlated with demographic variables, but such cor-relations are generally low �Lindell and Perry 2000�. Moreover,reports by Curtin et al. �2000�, Keeter et al. �2000�, and Lindelland Perry �2000� indicate low response rates do not appear to biascentral tendency estimates such as means and proportions andLindell and Perry �2000� presented psychometric reasons for be-lieving low response rates are very unlikely to affect correlations.Another limitation is that only about half of the respondents �i.e.,one-quarter of the original sample� provided enough data to com-
pute the preparation time estimates, so these data are correspond-178 / NATURAL HAZARDS REVIEW © ASCE / NOVEMBER 2005
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ingly limited in their precision. Finally, these data are limited intheir applicability to any hurricanes with late changing tracks�e.g., Hurricane Bret� because Hurricane Lili maintained a verystable track over the last 72 h before landfall. This undoubtedlycontributed to the finding that almost two-thirds of the evacueeshad already decided to evacuate before the NHC issued a hurri-cane warning and might have affected the preparation times, aswell.
This study raises a number of questions that need to be ad-dressed in future research. First, there is a need to examine therelationships among three different ways of assessing evacuationpredictors: �1� listing of reasons asked separately for evacueesand nonevacuees �the procedure used by Dow and Cutter �1998�;�2� respondents’ self-reports of the extent to which they relied oneach source �one procedure used in this study�; and �3� the cor-relations of source utilization with evacuation decisions �anotherprocedure used in this study�. Such research is needed to accountfor conflicts in the relative importance of local authorities andpeers �higher in Hurricane Lili than in Bertha and Fran� versusnational and local news media �lower in Hurricane Lili than inBertha and Fran�. The principal obstacle to drawing conclusionsfrom the available data is that differences in methods of measure-ment and analysis are confounded with differences in samplesand situations �geographical location, time of day, and stormbehavior�.
An important objective of future research should be to exam-ine the effects of storms having more erratic behavior—especiallychanges in track, forward movement speed, and intensity—on theresponse of coastal residents in risk areas. As was the case withthe surveys by Dow and Cutter �1998� after Hurricanes Berthaand Fran and the present study of Hurricane Lili, future researchshould survey a wide section of coastline from the projected pointof landfall to the edge of the hurricane watch area, and also fromthe coast to areas well inland. Such studies would make it pos-sible to assess the effects of uncertainties in storm behavior onevacuation decision times and preparation time components, bothof which are variables for which there is an inadequate amount ofempirical data to support hurricane evacuation time estimates.Moreover, future studies should examine the effects of coastalresidents’ assessments of their risk area location on evacuationdecision times and preparation time components �Arlikatti et al.2005; Zhang et al. 2004�. Finally, future research should use othersamples and other hazards in an attempt to replicate the findingsof Sorensen �1991� and the present study regarding the ability ofgeographic variables such as proximity to the rivers, lakes, andthe coast to predict evacuation decision times and preparationtime components.
Acknowledgments
This material is based upon work supported by the National Sci-ence Foundation under Grant No. CMS-0219155. Any opinions,findings, and conclusions or recommendations expressed in thismaterial are those of the writers and do not necessarily reflect theviews of the National Science Foundation. The writers wish tothank Sudha Arlikatti, Jung Eun Kang, and Yang Zhang for their
assistance in data collection.ASCE license or copyright; see http://pubs.asce.org/copyright
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