Jay Baker Department of Geography Florida State University Mike - - PowerPoint PPT Presentation

March 2018

Jay Baker

Department of Geography Florida State University

Mike Lindell

Institute for Hazard Mitigation Planning and Research University of Washington

ž The findings in the presentation are based on surveys.

• Not all surveys are created equal.
• A good survey has a representative sample of a risk area population and

asks questions that are theoretical based and practically significance.

ž We will begin with a broad framework.

• What do we mean by evacuation?
• What does 50 years of research on a wide variety of hazards show are the

major variables that influence household protective actions and how do these variables fit together in a coherent model?

• What are the most important variables affecting hurricane evacuation?

ž We will then turn to a number of specific findings from

hurricane evacuation research.

ž We will conclude with a summary of the most important

issues for emergency managers and evacuation transportation managers.

ž Leaving one’s home to go someplace

safer

• Leaving a surge-defined evacuation zone
• Leaving a structure that would be unsafe

from wind

– Mobile homes – “Substandard” housing

• Other considerations (e.g., loss of electric

power)

ž Principal evacuation behaviors

• Leaving or staying
• Departure timing
• Vehicle use
• Evacuation route choice
• Type of accommodations
• Evacuation destination
• Reentry

5

Pre-decision processes

• Exposure
• Attention
• Comprehension

Protective action perceptions Stakeholder perceptions Threat perceptions Social/ environmental context Personal characteristics

• Physical/psychological, material, social/

political, and economic resources

• Past experience
• Demographic attributes

Information search strategy Protective action decision making Behavioral response

• Information search
• Protective response
• Emotion-focused coping

Situational impediments Situational facilitators Information channels Social cues Information sources Environmental cues Warning messages

Authorities News Media Peers Female White Child Education Income Mobile Home Risk Area HrrExperience Official Warning Environmental Cues Peer Evacuating Business Closing Intensity Nearby Landfall Rapid Onset Surge Risk Flood Risk Wind Risk Casualties Service Disruption Evac Expense Traffic Jams Age Black Hispanic Marital HHSize Homeowner Coastal Tenure UnnExperience Job Disruption Looting Property Protection

0% 33% 66% 99% 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

Consistency Correlation

+ Positive Impact − Negative Impact X Nonsignificant Impact + Positive Impact (Less studied) − Negative Impact (Less studied) X Nonsignificant Impact (Less studied)

Model of Evacuation Participation

Hurricane Katrina/Rita Evacuation Model

• Mobile home residents

are more likely than residents of site-built homes to evacuate.

• In this example

MAINLAND mobile homes evacuated more than site-built homes on ISLANDS.

70 48

10 20 30 40 50 60 70 80 90 100 Mainland MH's Island Site Built Percent

Evacuation in Wilma in Lee County, FL

• After taking into

account a person’s housing type, evacuation zone, and hearing evacuation notices, demographics have relatively little effect on evacuation in most locations. (Data from Florida)

5 10 15 20 25 30 35 40 45 50

Other Ethnic Income Pets Kids Live Alone Age 10 Years in Region Black Mainland Surge Had Plan Expect Flooding Heard Should Go Beach Heard Must Go Mobile Home

Magnitude of Effect

Effect on Evacuation in Andrew

(-) (-)

Table 1: Smoothed percentages of h ouseholds expecting to evacuate for hurricanes in Category One through Category Five, by Risk Area. Risk Area Category One Category Two Category Three Category Four Category Five 1 45.9 63.7 87.8 98.2 100.0 2 35.9 53.7 77.8 88.2 91.4 3 31.1 48.9 73.0 83.4 86.6 4 28.2 46.0 70.1 80.5 83.7 5 26.5 44.3 68.4 78.8 82.0

Source: Lindell and Prater (2007)

• Participation rate

increases with storm category within each risk area.

• Participation rate

decreases with risk area within each storm category.

• There is incomplete

compliance (everything in the red boxes should be 100%) and shadow evacuation (everything

• utside the red boxes

should be 0%).

• People in most regions
• f Florida can’t correctly

indicate the evacuation zone in which they live.

• Labels below the

graph refer to planning regions of Florida – West Florida through Northeast Florida.

• SE Florida’s Cat 1

zone boundary was more simple than others.

WF AP NC WC TB SW SE TC EC NE Cat 1 Zone 11 16 17 21 33 15 58 12 28 18 Cat 3 Zone 21 11 5 7 13 6 13 6 28 3

10 20 30 40 50 60 70 80 90 100

Percent of All Respondents in Evacuation Zone Identified Correct Evacuation Zone Cat 1 and 3 Zones by Florida Region

• People living in more

vulnerable areas are more likely than others to evacuate.

• Still, rates are too low

in the most vulnerable areas and too high in the less vulnerable areas.

• Mandatory evacuation
• rders were issued for

Zone A. Orders varied among counties in other

• zones. The NHC forecast

track was for this location until shortly before landfall.

10 20 30 40 50 60 A B C D E Percent

Tampa Bay Evacuation in Charley by Zone

• The risk area

boundaries in this map are derived almost directly from SLOSH.

• Consequently, they

have a sound scientific basis.

• However, these

boundaries make it very difficult for most people to determine which risk area they live in.

Source: Arlikatti et al. (2006)

• Texas coastal residents are very inaccurate in judging which risk area they are in

—even when given a map such as the one on the previous slide.

• Too few people

usually evacuate from the most vulnerable areas.

• Too many people

usually evacuate from relatively safe areas.

• In Floyd mandatory
• rders were issued for

almost all of the cat 1-2 zones, some of the cat 3-5, but for none of the

• ther areas.

10 20 30 40 50 60 70 80 90 100 Cat 1-2 Cat 3-5 Non-surge Non-coastal

Evacuation in Floyd Site-built Homes

This map is relatively good because evacuation zone boundaries are defined by recognizable features such as roads.

Examples of Evacuation Zones

• The risk area

boundaries in this map are defined by ZIP codes.

• This works well

in densely populated areas where ZIP codes cover small areas

• It does not work

well in sparsely populated \areas where a ZIP code includes areas with very different elevations.

• People are more likely

to evacuate if they believe they have been told to do so by public

• fficials.
• Graphs at right show

the effect within each of four risk zones. Bars indicate % evacuating in each group. (Data from Floyd)

50 100 Percent of Respondents None Should Must Cat 1 50 100 Percent of Respondents None Should Must Othr Surge 50 100 Percent of Respondents None Should Must Non-surge 50 100 Percent of Respondents None Should Must Non-coastal

Evacuation by Official Notice H

• Too few people in

evacuation zones say they hear evacuation

• notices. Evacuation

was ordered in all Cat 1 zones at right except SE FL.

• Too many people
• utside evacuation

zones say they hear evacuation notices. (Data from Floyd)

10 20 30 40 50 60 70 80 90 100 Percent of Respondents Southeast FL

• Treas. Coast FL

East Central FL Northeast FL Brunswick GA Savannah GA Beaufort SC Charleston SC Myrtle Beach SC Southeastern NC Eastern NC

Heard Officials Say Evacuate

Cat 1 Surge Zone

• The combined effect of

hearing mandatory evacuation notices AND believing that one’s home would be unsafe can be very large.

• Bars indicate the

percent evacuating in each group.

10 20 30 40 50 60 70 80 90 100

Coastal Parishes Adjacent Parishes

Evacuation in Lili in Louisiana

Safe, Heard Nothing Unsafe, Heard Must

10 20 30 40 50 60 70 80 90 100

LT $15K GT $15K

Percent of Income Category

Income

Reasons Given for Not Evacuating in Floyd by Income

No Transport No Place to Go Felt Safe

• Most people who don’t

evacuate fail to do so because they don’t believe they need to, not because of constraints to leaving.

• This is true even for low

income households in most locations.

• Most people are more

concerned about WIND than other hazards – even those close to water.

10 20 30 40 50 60 70 80 90 100

LTE 1 Block GT 1 Block Percent

Hazard of Greatest Concern in Sandy by Stated Proximity to Water

Other Don't Know Tornadoes Rainfall Flooding Wind+Surge Surge/Waves Wind

• People who believe

their homes would be unsafe if struck by a hurricane are more likely than others to evacuate.

• Graphs at right show

the effect within each of four risk zones. Bars show % evacuating in each group. (Data from Floyd)

20 40 60 80 Percent of Respondents Safe Not Safe Cat 1

in 125 MPH Hurricane

20 40 60 80 Percent of Respondents Safe Not Safe Othr Surge

in 125 MPH Hurricane

20 40 60 80 Percent of Respondents Safe Not Safe Non-Surge

in 125 MPH Hurricane

20 40 60 80 Percent of Respondents Safe Not Safe Non-Coastal

in 125 MPH Hurricane

Evacuation by Perceived Safe in 125 MPH Hurricane

• Too many people in the

most vulnerable locations underestimate their vulnerability.

• Too many people in

less vulnerable locations

• verestimate their

vulnerability.

• The graph at right

shows the percent who said their homes would flood dangerously from storm surge or waves in Cat 2, 3, and 4 (almost 5) storms. (Data from Florida)

10 20 30 40 50 60 70 80 90 100 Cat 1 Zone Cat 2 Zone Cat 3 Zone Cat 4 Zone Cat 5 Zone Non-surge Non-coastal

Home Would Flood Dangerously Site Built Homes

Flood in Cat 2 Flood in Cat 3 Flood in Cat 4/5

• The graph at right

shows % who said their homes would be unsafe considering both wind and water in cat 2, 3, and 4 (almost 5) storms. (Data from Florida)

10 20 30 40 50 60 70 80 90 100 Cat 1 Zone Cat 2 Zone Cat 3 Zone Cat 4 Zone Cat 5 Zone Non-surge Non-coastal

Home Would be Unsafe Considering Wind and Water --Site Built Homes

Unsafe in Cat 2 Unsafe in Cat 3 Unsafe in Cat 4/5

• People tend to

believe that hurricane winds are MORE likely in their location than NHC calculations.

• These results (and

those in the next five slides) are from interviews conducted during actual hurricane threats, not afterward.

10 20 30 40 50 60 70 80 90 100 18 20 21 22 24 25 26 28 29 Probability NHC Advisory

Subjective and NHC Probabilities of Hurricane Force Winds in Sandy

Subjective Hurricane Winds NHC Hurricane Winds

• Most people still

believe they will be safe, even if they believe hurricane force winds are likely.

10 20 30 40 50 60 70 80 90 100 18 20 21 22 24 25 26 28 29 Probability NHC Advisory

Subjective and NHC Probabilities of Hurricane Force Winds and Perceived Safety in Sandy

Perceived Safety Subjective Hurricane Winds NHC Hurricane Winds

• People

reasonably believe that damaging or dangerous wind is less likely that hurricane wind.

• However, they

also believe surge

• r rainfall flooding

are less likely than damaging or dangerous wind.

10 20 30 40 50 60

Hurricane Wind Damaging Wind Dangerous Wind Damaging Surge Dangerous Surge Damaging Rain Dangerous Rain Probability

Mean Subjective Probabilities in Sandy

• This is true

even for people living near the water.

10 20 30 40 50 60

Hurr Wind Dmg Wind Dngr Wind Dmg Surge Dngr Surge Dmg Rain Dngr Rain

Probability

Mean Subjective Probabilities in Sandy by Stated Proximity to Water

1 Block > Block

• People tend to believe

that hurricane wind is MORE likely in their locations than NHC calculations.

• However, neither

subjective nor NHC wind probabilities is a strong predictor of evacuation intention.

10 20 30 40 50 60 70 80 90 100

LTE 1 Block GT 1 Block

Percent

Evaluation of Threat in Sandy by Stated Proximity to Water

Don't Know if Danger Will Hit and be Danger No danger if Hit Danger if Hit, but Won't Hit

• Most people in Florida

coastal counties can’t indicate the elevation of their home within approximately 5 feet of the actual homesite elevation.

• This makes it unlikely

that they can make good use of forecast heights of storm surge.

12 14 19 55

Accuracy of Perceived Elevation in Florida

Low Correct High No Guess

• Although people have

many misconceptions about forecast information, those beliefs have little effect on evacuation behavior.

• In this 2006 example

from Florida only 29% knew that hurricane warnings were issued 24 hours before arrival of the storm. Hours before Landfall Believe a Hurricane Warning is Issued

12 hrs 20% 24 hrs 29% 36 hrs 8% 48 hrs 10% 72 hrs 4% More than 72 hrs 2% Don't Know 27%

• In this example, the

evacuation percent was the same if people saw just the track line, just the cone, or both.

• Wu et al. (2014) found

similar results in a laboratory experiment.

• Most people say they

have seen “spaghetti plots” of computer models, but there is no difference in evacuation behavior between those who have and haven’t seen the graphics.

• Cox et al. (2013) found

similar results in a laboratory experiment.

• Public education

materials often have little success at improving awareness of one’s vulnerability.

• In this example,

familiarity with a hurricane tabloid in Florida had little effect of perceived vulnerability.

10 20 30 40 50 60 70 80 90 100 Never Saw Tabloid Saw Tabloid Stil Has Tabloid Saw Map in Tabloid ID'd Zone on Map

Said Home Would Not be Safe in Cat 3 Storm

Cat 1 Zone Only

• People still rely on

television for most of their information during hurricane threats.

• They rely much more
• n local TV news than

national TV news.

20 40 60 80 100 Social Media Friend/Relative Radio Internet TV

Percent of Respondents

Source of Most Recent Information in Irene

• However, there are

age differences in reliance on information sources.

• Younger people rely

more than older people

• n internet and social

media. (Data from Earl)

10 20 30 40 50 60 70 80 90 100 <30 30-45 46-60 61-70 71-80 >80 Percent of Respondents Age TV Internet

• People at risk who

evacuate when a storm misses are still likely to evacuate in future hurricane threats.

• Panama City evacuated 3

times in 1985. All 3 times the storm missed.

• Dow and Cutter (1998)

found that the percentage who evacuated for a first hurricane but not a second

• ne was almost exactly the

same as those that did not evacuate for the first hurricane but did evacuate for the second. 20 40 60 80 100 Beaches Mainland Elena1 Elena2 Kate

1985 Evacuations in Panama Cit

• Providing refuges of

last resort could result in slightly lower evacuation rates. (Data from Florida Keys)

10 20 30 40 50 60 70 80 90 100

Percent of Respondents Evacuating

Cat 2 Watch Cat 2 Warn Cat 3 Watch Cat 3 Warn Cat 4 Watch Cat 4 Warn

Threat Scenario No Refuge Refuge

Keys Intended Evacuation Rat

With vs Without Refuges of Last Resort

• Pets have a small

effect on evacuation statistically.

• In this example pet
• wnership had no effect
• n evacuation if owners

believed it would be unsafe to stay in their homes during the storm.

Safe in Cat 2 Unsafe in Cat 2 Pets 35 73 No Pets 50 73 10 20 30 40 50 60 70 80 90 100

Percent

Evacuation in Wilma in Lee County, FL

• Y axis at right shows

cumulative % of eventual evacuees who had left by various times (X axis).

• A minority of those at

risk leave before evacuation notices are issued.

• This is a “slow” two-

day evacuation. SC issued evacuation notices earlier than NC, prior to the warning.

10 20 30 40 50 60 70 80 90 100 6A 8 10 12P 2 4 6 8 10 12A 2 4 6 8 10 12P 2 4 6 8 10 Cumulative Percent of Evacuees Time of Departure

Cumulative Evacuation in Fran

September 4-5, 1996

SC NC

Warning

• This is a more urgent
• evacuation. Most

evacuation notices were issued AFTER the warming.

10 20 30 40 50 60 70 80 90 100 12A 2A 4A 6A 8A 10A 12P 2P 4P 6P 8P 10P 12A 2A 4A 6A 8A 10A 12P 2P 4P 6P 8P 10P Cumulative Percent of Evacuees Time of Departure

Cumulative Evacuation in Opal

October 3-4, 1995

Warning

• Few people leave very

early in the morning (midnight to 5am).

• Most leave in the late

morning (6-11am) or afternoon (noon-5pm).

• Few leave at night

(6pm-midnight).

0.00 5.00 10.00 15.00 20.00 25.00 6-11 AM 12-5 PM 6-11 PM 0-5 AM 6-11 AM 12-5 PM 6-11 PM 0-5 AM 6-11 AM 12-5 PM 6-11 PM 0-5 AM 6-11 AM 12-5 PM 6-11 PM 0-5 AM 6-11 AM 12-5 PM 6-11 PM 0-5 AM 6-11 AM 12-5 PM After Sun Sep 14

Percent

Tues Sep 9 Wed Sep 10 Thu Sep 11 Fri Sep 12 Sat Sep 13 Mon Sep 8 Hurricane Watch 4:00 PM Wed Sep 10 Hurricane Warning 10:00 AM Thu Sep 11 Hurricane Eye Landfall 2:30 AM Sat Sep 13

Hurricane Ike Evacuation (8-13 September 2008)

• Most households have access to personal vehicles
• Median = 89%; Range 87-91%
• Many households take more than one vehicle
• Median 1.38 vehicles/household; Range = 1.25-1.70
• Inter-county range = 1.10-2.15 (Lili), 1.15-1.85 (Katrina/Rita)
• Households do not take all registered vehicles
• Mean = 72%
• Households also take trailers
• Median = 12%; Range = 5-35%.

1 2 3 4 5 Past experience Traffic conditions encountered enroute News media recommendations Local authorities’ recommendations Written materials received in advance

• Most households take

their “normal” route to their evacuation destination

• This is usually the most

direct route by freeways, but 9-37% of evacuees plan to take unofficial routes. Katrina/Rita evacuation route information sources

• Most evacuees go to the homes of friends and relatives.
• Median = 62%; Range 54-70%
• Fewer go commercial facilities (hotels and motels)
• Median = 27%; Range 16-32%
• Very few go to public shelters.
• Median = 3%; Range 2-6%
• Public shelter use is related to
• Demographics (ethnic minorities and lower income)
• Circumstances (evening/night evacuation)

• Evacuation destinations vary among evacuating locations,

depending on the proximity of accommodations.

• Many evacuees travel farther than necessary for safety.
• This is often because friends and family live far away.
• In other cases, evacuees must drive until they find a hotel/motel with

vacancies.

• Average distances vary by storm and location
• Median = 196 mi in four studies
• Evacuation trips take much longer than normal
• Additional 180 min (Katrina), 417 (Rita), 80 (Ike)
• Some Rita evacuees took 15 hours more than normal.

20 40 60 80 100

Elena Georges Charley

Percent of Evacuees

Evacuees Going Out of County In Tampa Bay

20 40 60 80 100 In county (< 50 mi) In state (< 250 mi) Out of state (> 250 mi)

Percent of Evacuees

South Carolina Destinations in Hurricane Floyd

Most Common Evacuation Destinations in Lili

Evacuation Destinations in Rita, by County

• Predictors of extended/permanent relocation
• Renter, greater damage/financial losses, greater perceptions of future

damage, minority ethnicity, lower community bondedness,

• Compliance with official reentry plans
• Rita: 47%; only 20% were aware of the official TXDOT reentry plan
• Ike: 38%; only 36% received a message about the official reentry plan but

this was uncorrelated with compliance.

1.0 2.0 3.0 4.0 5.0 Looting Lost Income Traffic Utilities Extent Return Issue Mean Expected Mean Reported

Ike Expected versus Reported Reentry Issues

45.1% 21.8% 51.5% 54.9% 79.2% 35.6%

0% 20% 40% 60% 80% 100% Hurricane Rita Cedar Rapids Hurricane Ike

No Yes

20 40 60 80 100 1-2 days before evacuating Day you evacuated While at evacuation location Day you decided to return Day you returned home Percentage

Local news National news Local authorities Internet Peers

ž Evacuation Decisions

• Official notices are the most powerful determinants of evacuation

decisions, but “mandatory” and “voluntary” are often misinterpreted.

• Social cues (businesses closing and neighbors evacuating) are also

powerful determinants.

• Risk areas/evacuation zones are strong determinants, but are also often

misinterpreted because people lack maps or can’t interpret them properly.

• Hurricane category is an important determinant of evacuation, but

definition in terms of wind speed seems to have diverted people’s attention from surge risk.

• Noncompliance with evacuation notices is more strongly determined by

lack of perceived risk than by evacuation constraints.

• Demographic variables primarily affect evacuation decisions through

their effects on perceptions of risk.

ž Communities need to educate people

• About the evacuation zone in which they are located to increase warning

compliance and reduce shadow evacuation.

• About their vulnerability to surge, due to the elevation of their homes

above sea level.

• That shadow evacuees slow the evacuation of those who are at risk

because congestion spills back “upstream” in the traffic flow.

• That they should develop a household evacuation plan with

arrangements for accommodations before leaving.

• That waiting for greater certainty about landfall location will put them in

competition with everyone else who is also waiting until the last minute.

• To monitor their community’s website for information about official reentry

plans and conditions in the evacuation zone—especially security from looting.

ž

Arlikatti, S., Lindell, M.K., Prater, C.S. & Zhang, Y. (2006). Risk area accuracy and hurricane evacuation expectations of coastal residents. Environment and Behavior, 38, 226-247.

ž

Baker, E. J. (2011) “Household Preparedness for Hurricane Aftermath in Florida” Applied Geography, 31 pp. 46-52.

ž

Baker, E. J. (2002) “Social Impacts of Tropical Cyclone Forecasts and Warnings,” World Meteorological Organization Bulletin, vol. 51, no. 3, pp. 229-235.

ž

Baker, E. J. (2000) “Hurricane Evacuation in the United States,” in R. Pielke and R. Pielke (eds), Storms Vol 1, Routledge.

ž

Baker, E. J. (1991) "Evacuation Behavior in Hurricanes," International Journal of Mass Emergencies and Disasters, Vol. 9, pp. 287-310.

ž

Cox, J., House, D. & Lindell, M.K. (2013). Visualizing uncertainty in predicted hurricane tracks. International Journal for Uncertainty Quantification, 3, 143-156.

ž

Fu, H., C. G. Wilmot, H. Zhan, and E. J. Baker (2007) “Modeling the Hurricane Evacuation Response Curve,” Transportation Research Record: Journal of the Transportation Research Board, No. 2022, Transportation Research Board of the National Academies, Washington, DC, pp. 94-102.

ž

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, 991-1029.

ž

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, 283-296.

ž

Huang, S-K, Lindell, M.K. & Prater, C.S. (2017). Toward a multi-stage model of hurricane evacuation decision: An empirical study of Hurricanes Katrina and Rita. Natural Hazards Review, 18(3), 05016008 1-15.

ž

Kang, J.E., Lindell, M.K. & Prater, C.S. (2007). Hurricane evacuation expectations and actual behavior in Hurricane Lili. Journal of Applied Social Psychology, 37, 881-897.

ž

Lin, C-C., Siebeneck, L.K., Lindell, M.K., Prater, C.S., Wu, H.C. & Huang, S.K. (2014). Evacuees’ information sources and reentry decision making in the aftermath of Hurricane Ike. Natural Hazards, 70, 865-882.

ž

Lindell, M.K. (2017). Communicating imminent risk. In H. Rodríguez, J. Trainor, and W. Donner (eds.) Handbook of Disaster Research (pp. 449-477). New York: Springer.

ž

Lindell, M.K., Kang, J.E. & Prater, C.S. (2011). The logistics of household evacuation in Hurricane

• Lili. Natural Hazards, 58, 1093-1109.

ž

Lindell, M.K. & Perry, R.W. (2012). The Protective Action Decision Model: Theoretical modifications and additional evidence. Risk Analysis, 32, 616-632.

ž

Lindell, M.K. & Prater, C.S. (2007). Critical behavioral assumptions in evacuation analysis for private vehicles: Examples from hurricane research and planning. Journal of Urban Planning and Development, 133, 18-29.

ž

Lindell, M.K., Prater, C.S. & Peacock, W.G. (2007). Organizational communication and decision making in hurricane emergencies. Natural Hazards Review, 8, 50-60.

ž

Siebeneck, L.K., Lindell, M.K., Prater, C.S., Wu, H.C. & Huang, S.K. (2013). Evacuees’ reentry concerns and experiences in the aftermath of Hurricane Ike, Natural Hazards, 65, 2267–2286.

ž

Wu, H.C., Lindell, M.K. & Prater, C.S. (2012). Logistics of hurricane evacuation in Hurricanes Katrina and Rita. Transportation Research Part F: Traffic Psychology and Behaviour, 15, 445-461.

ž

Wu, H.C., Lindell M.K., Prater C.S. & Huang, S-K. (2013). Logistics of hurricane evacuation in Hurricane Ike. In J. Cheung and H. Song (Eds.), Logistics: Perspectives, Approaches and Challenges (pp. 127-140). Hauppauge NY: Nova Science Publishers.

ž

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, 1025-1039.

ž

Zhang, Y., Prater, C.S., & Lindell, M.K. (2004). Risk area accuracy and evacuation from Hurricane

• Bret. Natural Hazards Review, 5, 115-120.