communicating hurricane warnings: factors …...communicating hurricane warnings: factors affecting...

Communicating Hurricane Warnings: Factors Affecting Protective Behavior Jeffrey K. Lazo 1, Ann Bostrom 2, Rebecca Morss 1, Julie Demuth 1, Heather Lazrus 1

1 National Center for Atmospheric Research, Boulder CO 2 University of Washington, Seattle, WA

Conference on Risk, Perceptions, and Response Harvard School of Public Health, March 20-21, 2014

Question A) How can we identify and assess cases where reactions to risk have led, or are likely to lead, to harmful behaviors, from an ex post or ex ante perspective?

http://app1.kuhf.org/articles/1378852818-Looking-at-Evacuation-Plans-Five-Years-After-Ike.html http://www.metairie.com/Hurricane%20Katrina%20Photos/New%20Orleans/04%20Jet%20Ski.jpg

Hurricane Katrina - Rescue August 2005

• Objective • Conceptual Approach • Methods • Results • Ongoing research

Outline

• Build understanding of why different people respond to the risk of an approaching hurricane in the ways that they do

• What can be done to improve people’s decisions about protective actions?

• Relevancy to all weather related risk decision making (flash floods ↔ hurricanes)

Objectives

Informed by mental model research to analyze forecast and warning related decision making

Conceptual Approach

Protective Action Decision Model (PADM) Appraisal Theory

Cultural Theory Psychometric research on risk perceptions

• Survey development and pre-testing – Mental models interviews with forecasters, broadcasters,

emergency managers, and public – Prior hurricane-related survey work – Risk communication literature (e.g., psychometric paradigm) – Cultural theory (Leiserowitz, Douglas and Wildavsky) – Pre-tested with one-on-ones and small sample online

• Implementation

– Online survey – available in Spanish – Knowledge Networks (KN) KnowledgePanel®

– May 4-24, 2012 – N=808 (61.6% of those invited to the survey)

• 460 in the Miami-Dade area • 348 in the Galveston-Houston area

Methods

• Sociodemographic characteristics • Stated likelihood of evacuation

– evacuation order – saw hurricane forecast indicating the threat

• Cultural Theory of Risk • Perceived risks and vulnerability • Prior experience and mitigating behaviors • Perceived barriers and motivations to evacuation • Sources of information

• Regression analysis on evacuation intentions

– between “evacuation order” and “saw forecast” – between Florida and Texas respondents

Results

Respondents’ Socio-Demographics (n=804) Characteristic Mean

Age (Years) 51.8

Total Yrs in Hurricane Vulnerable Area 26.6

Education (Years) 14.5

Income (Thousands) 57.8

Dummy Variables Female 60.4%

Own Residence 68.8%

Children in House 31.7%

Took Survey In Spanish 21.6%

Single Family House – Detached 57.3%

“How likely is it that you would evacuate (leave your residence for somewhere safer) if…”

Mean likelihood of evacuation under different information conditions “Extremely Unlikely”=1; “Somewhat Likely”=3; and “Extremely Likely”=5

“Explanatory” Variables Cultural Theory of Risk • Individualist ↔ Communitarian • Egalitarian ↔ Hierarchist Perceived risks and vulnerability • Evacuation zone • Likelihood of impacts / conditions

• Blowing Objects • Storm Surge • Inland Flood • Mortality/Morbidity • Looting

• Likelihood of Hurricane in Next Year • Hurricane Risks

• Catastrophic • Controllability

“Explanatory” Variables Prior experience and mitigating behaviors • Prior experience severity scale • Prior preparations • Prior evacuation Motivations and barriers to evacuate • Motivations • Barriers • House safe Sources of information • Public sources (e.g., NWS) • Private sources (e.g., friends)

Perceived Barriers and Motivations to Evacuation Principle Component Analysis

“Strongly Disagree”=1 to “Strongly Agree”=5 (n=804)

Barriers and Motivations Factor1

Motivations Factor 2 Barriers

Factor 3 House Safe

… a hurricane could injure or kill me 0.81 … I want to keep my family safe 0.81 … I trust hurricane warnings and forecasts 0.80 … not be stuck in the area after the hurricane 0.74 … my house is vulnerable to hurricane winds 0.63 -0.46 … my house is vulnerable to hurricane storm surge 0.55 -0.44 … health or disabilities make it difficult to evacuate 0.79 … family health/disability makes evacuation difficult 0.76 … lack transportation 0.75 … do not know how to evacuate 0.62 … distrust hurricane warnings and forecasts 0.59 … pet(s) make it difficult to evacuate 0.53 … need to protect my home or business 0.48 0.40 … house is safe from hurricane storm surge 0.82 … house is safe from hurricane winds 0.79

• OLS on Likelihood of Evacuation – saw hurricane forecast indicating the threat – evacuation order

• Combined models and separate Texas and Florida models

• Complete model and hierarchical models

Regression analysis

OLS Regression Analysis on Evacuation Intentions Saw Forecast Evacuation Order

Combined (N=804) Texas (N=347) Florida (N=457) Combined

(N=804) Texas (N=347) Florida (N=457)

Model Statistics Adj R-Sq 0.34 0.31 0.32 0.22 0.20 0.21

Sociodemographics Age 0.13 ** -0.09 ** -0.11 **

Yrs Reside in Hurr Area Education 0.15 *** Spanish 0.09 *

Single Family House Income

Male -0.09 *** -0.11 ** -0.09 ** Own Residence -0.15 ** 0.10 ** 0.07 * 0.13 **

Children Cultural Theory (Factor Scores)

Individualistic Egalitarian

Perceived risk and vulnerabilities Evac Zone (Perc) Blowing Objects 0.09 ** 0.14 **

Storm Surge -0.10 ** -0.11 * -0.11 ** Inland Flood

Mortality/Morbidity Looting

Risk Catastrophic Risk Controllable 0.09 *** 0.11 ** 0.08 * 0.08 ** 0.10 *

Hurricane Likelihood - Next Yr Prior experience

Prior Evacuation 0.17 *** 0.11 ** 0.17 *** 0.10 *** 0.11 ** No Prior Experience -0.06 * Prior Preparations

Perceived motivations and barriers (Factor Scores) Motivations 0.42 *** 0.45 *** 0.42 *** 0.32 *** 0.29 *** 0.35 ***

Barriers -0.11 *** -0.14 *** House is Safe -0.24 *** -0.22 *** -0.262 *** -0.08 *** -0.12 **

Sources of information (Factor Scores) Official 0.14 *** 0.17 *** 0.13 ***

Personal

Factors increasing stated likelihood of evacuation • Older • Increased on “motivation” factor score • Perception house is not safe • Perception impacts are controllable • Prior evacuation experience Factors not affecting stated likelihood of evacuation • Income, children, education, house type … • Cultural Theory (Individualism and Egalitarianism) • Perceived evacuation zone • Other perceived vulnerabilities (flood, looting, mortality …) • Personal information sources

Results

Factors increasing stated likelihood of evacuation with “Evacuation Order” but not “Saw Forecast” • Females more likely to evacuate (“Male effect”) • Increased perception of blowing objects impacts • Decreased perception of storm surge impacts • Accessing “Official Information” sources Differences between Florida and Texas • Owning residence

• FL increased likelihood of evacuating when own residence • TX decreased likelihood of evacuating when own residence

• When getting an evacuation order increased likelihood of evacuating when • Fewer perceived barriers in FL • Higher education in TX

• When getting “Saw” increased likelihood of evacuating • Spanish in FL

Results

• Being younger, less hurricane-experienced, male, perceiving barriers to evacuation and thinking one’s house is safe are each associated with a lower likelihood of evacuating on receiving an evacuation order

• Those who perceive the risk as controllable, have evacuated previously, perceive positive motivations for evacuation, and rely on official sources of information are more likely to say they would evacuate on receiving an evacuation order

Summarizing

• May need to help people better understand what the vulnerability of their residence is – “Run from the water and hide from the wind”

• Better address perceived motivations and barriers to evacuation – helpful to emphasize the benefits of and motives for evacuating – Inform that the risk from hurricanes is controllable (and how to

control such risks).

• Need better understanding of why people do or don’t know if they are in an evacuation zone – 31% of respondents not in an evacuation zone said they were – 11% of respondents in an evacuation zone said they were not – Overall ~20% don’t know if they are in an evacuation

• Need longitudinal data across multiple events – Ex ante – intentions prior to hurricane – Ex post – actual decision making in a hurricane

Discussion …

• Refining conceptual model • Refining interpretation of variables

– Especially factor analysis of barriers and motivations

• Revising analysis variables – Actual evacuation zone, distance, elevation

• Combined model with state-interaction terms • Ordinal logistic regression

– Possible path model?

Ongoing Work

Thank You!

Feedback on manuscript will be much appreciated!

Jeff Lazo lazo@ucar.edu

PO Box 3000, Boulder, CO 80307 Office: 303-497-2857

This material is based on work supported by the National Science Foundation under Grant No. AGS-0729511

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