CSAP Project Progress Report Form

Transcription

1 1 CSAP Project Progress Report Form Please complete this progress report form and return by the date indicated in the ed progress report request from the Connecticut, New York and New Jersey Sea Grant College Programs. Fill in the requested information using your word processor (i.e., Microsoft Word), and e mail the completed form to Cornelia Schlenk, cornelia.schlenk@stonybrook.edu, Assistant Director, New York Sea Grant. Do NOT mail or fax hard copies. Please try to address the specific sections below. If applicable, you can attach files of electronic publications when you return the form. If you have questions, please call Cornelia Schlenk at (631) Please note that this report will be shared with the other CSAP PIs and the Program Steering Committee. Please fill out all of the following that apply to your specific research project. Pay particular attention to goals, accomplishments, benefits, impacts and publications, where applicable. Name of Submitter: Ricardo A Daziano Date of Report submission: April 21, 2014 Project #: R/CSAP 5 NY Progress Report # 1 Dates of the reporting period, as specified in the transmittal From 01/02/2014 to 03/31/2014. Project Title: Forecasting evacuation behaviors of coastal communities in response to storm hazard information Principal Investigator(s) and Affiliation(s): 1. Ricardo A. Daziano, School of Civil and Environmental Engineering, Cornell University 2. Jonathon Schuldt, Department of Communication, Cornell University 3. Phil Liu, School of Civil and Environmental Engineering, Cornell University 4. Linda Nozick, School of Civil and Environmental Engineering, Cornell University

2 2 A. PROJECT GOALS AND OBJECTIVES: This project seeks to collect, analyze, and model microdata on informed evacuation behavior within coastal communities in the tri state areas impacted by Hurricane Sandy. Our main hypothesis is that there is an efficient mix of warnings type of information and media that will encourage people to quickly enact their evacuation plans. Thus, the goal of this project is to enhance our knowledge about the opportunity to exploit social media to support evacuation, while addressing heterogeneity in the processes of making and updating evacuation decisions. We basically expect to identify the best tools for enacting safe and effective evacuation plans, including the opportunities associated with the use of social media. More specifically, we will design a web based survey instrument to collect detailed information about awareness, preparedness, evacuation, and survival to weather hazards from households in coastal communities of New Jersey, New York, and Connecticut to achieve the following research objectives: 1. Build novel methods for both presenting and generating new data using discrete choice experiments of behavioral response to storm hazards. Hypothetical storms and preventive information need to be presented in a way that is realistic to the individual. An additional challenge is to account for events that present an uncertain evolution in time. The media used to convey and visualize information is key for addressing the dynamics of weather hazards. Thus, we will control for different media in a web based, customized survey. We will also combine intended actions with actual evacuation behavior during hurricane Sandy. 2. Construct stochastic models of evacuation behavior. We will identify the causal relationship between probabilistic measures of evacuation behaviors (when, where, and how to evacuate) and a complete set of explanatory variables such as attributes of the dynamic predictions of weather conditions (e.g. storm intensity) and coastal impacts (e.g. flood risks), risk measures and attitudes toward risk (e.g. evacuation orders), network effects (e.g. evacuation behavior by neighbors and friends, social media reports), and socioeconomic characteristics of the household (e.g. region, distance to shoreline, demographics, and lifestyle and values). 3. Derive robust estimates of evacuation probabilities for heterogeneous individuals as a concrete measurement of the impact of differing types of storm related risk information and information outlets (social media) on evacuation behavior. Credible sets will be used to account for uncertainty in the determination and prediction of the probabilistic measures of evacuation behaviors. 4. Design effective evacuation communication tools and policies accounting for the differing, uncertain response of coastal communities. Using the evacuation predictors, behavioral forecasts, and empirical evidence we expect to elucidate the factors that will encourage safe evacuation. We also expect to determine the mix of most effective

3 3 information tools and outlets for better informed decisions in response to storm hazards. B. PROGRESS: (Summarize progress relative to each of the project goals and objectives. Highlight outstanding accomplishments, outreach and education efforts; describe problems encountered and explain any delays.) Successful completion of the four research objectives above requires adequate design of the web based instrument that will be used to collect the data. We are currently working in designing the pretest instrument. For the pretest, we will use a Qualtrics panel with a small, random but non representative sample of individuals living in the New York City Metropolitan Area. A Qualtrics panel of 150 individuals for a 25 minute survey has been already purchased. Below, we summarize the main elements that we have been considering for the design of the pretest. B.1) Attitudinal Response to Extreme Events Prior research at the intersection of psychology, communication, and risk analysis has examined the how citizens process information about extreme events, including the hurricane disaster and evacuation scenarios. In general, this work reveals that citizens evacuation intentions are shaped by a variety of situational factors, including warnings issued by authorities, messages received from media sources, and the desires and expectations of loved ones (e.g., Lindell, Kang & Prater, 2011; Perry, 2004). Because the majority of scholars have focused on the factors affecting citizens intentions to evacuate as a function of these and other situational variables, the theory of reasoned action (TRA; Fishbein & Ajzen, 1975) has guided much of the work in this area. TRA posits that behavioral intentions are comprised of individuals subjective evaluations of the outcome(s) of a given behavior as well as their perceptions about what other people think and desire for them. For instance, if a person held a positive evaluation of evacuating to a safe shelter and perceived that important loved ones thought similarly and wished them to evacuate, they would likely form a behavioral intention to do so. Behavioral intentions are posited to be a primary determinate of behavior, unless unforeseen situational factors arise that constrain the intended behavior or encourage an alternative response. That subjective norms are an important factor in both behavioral intentions to evacuate and actual evacuation decisions is supported by the literature (e.g., Dash & Gladwin, 2007; Riad, Norris, & Ruback, 1999). It has been long been argued that warnings from credible and trusted authority figures are among the most important factors in citizens decisions to evacuate (e.g., Perry, 1979; see also Dow & Cutter, 2008). While technology has long been integral to the dispersal of evacuation warnings e.g., in the form of television coverage and emergency broadcast systems new mobile technologies (e.g., Smartphones, social media applications) provide novel forms of rapid information transmission that open up new questions and opportunities for research. To that end, in this project we will explore

4 4 how different hurricane warning messages influence behavioral evaluations, subjective norms, and behavioral intentions, when transmitted over new technological platforms such as smartphones. We will also explore the use of non traditional data collection methods for elicitation of stated preferences, such as the use of smartphone apps. Attitudes and response to new sources of information, including Twitter and other social media, will be evaluated using sociological theories that integrate concepts such as subjective norms and behavioral control into discrete choice models. In particular, drawing on research from across the behavioral sciences, we will include survey questions that assess a number of response variables related to the effectiveness of risk messages. These include beliefs about the focal risk itself (Sjöberg, 2000), perceived efficacy of message recommendations (Witte, 1992), attitudes, perceived behavioral control, and subjective norms (Ajzen, 1991), perceptions about the visual communication tool itself (e.g., message clarity, see Weinstein & Sandman, 1993), subjective psychological distance to the threat (Morton & Duck, 2001), general and discrete emotional responses (Lerner et al., 2003), and intentions to act in ways that are consistent with message prescriptions (Ajzen, 1985). These attitudinal variables, which will constitute the main outcome measures of the survey, can be broadly divided into three categories that are theorized to underlie behavioral responses: cognitions (e.g., beliefs such as personal risk assessments), emotions (e.g., fear/anxiety, attitudes toward evacuating), and behavioral intentions (e.g., state likelihood of leaving one s property and evacuating a high risk zone). Note that the discrete choice experiments described above measure one important dimension of these behavioral intentions, namely the self reported likelihood of evacuation as a response to the storm information in each choice situation. However, there are additional dimensions of behavioral intentions that we will explore. We will also measure individual difference variables that have been found to predict message persuasiveness, notably personal topic relevance (Petty & Cacioppo, 1986). Regardless of visual information treatment, all survey respondents will complete the same standard set of questions, thus allowing us to establish the effect of different treatments on outcomes that have been shown to predict behavioral compliance and that are widely considered to be markers of effective risk messages involving visuals (Lipkus & Hollands, 1999). Specifically, we propose to include the following survey items that fall within the aforementioned categories: B.2) Design of pretest instrument As a preliminary assessment of the validity and reliability of key outcome measures, we will conduct a survey using participants from Qualtrics panel ( in which a non representative sample of Americans will complete measures related to cognitive, emotional, and behavioral responses to information about extreme weather events. This preliminary study will also us to pilot visual graphics that communication information about severe storms, to gain initial insights about audience response valuable information that

5 5 will guide our selection of stimuli to include in the later, representative survey of residents in coastal areas of New York, New Jersey, and Connecticut. This pretest also provides an opportunity to imbed discrete choice experiments in order to test their feasibility before launching the representative survey. We will field a variety of measures and visual graphics as part of the pretest. Because a primary focus of the project is to understand the factors that influence behavioral intentions to evacuate, we are guided by theories of behavioral intention (e.g., Theory of Planned Behavior; Ajzen, 1991) during measure selection. Relevant constructs include attitudes toward the behavior, beliefs about the consequences of the behavior, subjective norms and one s motivation to comply with those norms, and perceived control over the behavior and its likely success. In short, the theory suggests that intentions to evacuate will be high when people feel positively toward evacuating, when they perceive others do also, and when they feel capable of enacting the necessary steps. Figure 1. Hurricane Sandy forecast path (Friday October 26, 2012 at 5 PM). Participants will answer a series of questions that assess these variables after viewing visual storm graphics. For instance, participants will be exposed to a hurricane tracking map (a hurricane cone ; see Figure 1) that conveys information about the storm s severity and

6 6 forecasted impacts. Such visuals will boost the realism of the decision scenario and allow us to test the effectiveness of different visual formats in promoting behavioral intentions to evacuate. To gauge affective responses, participants will be asked questions such as the following: Given this information, how positively (negatively) do you feel toward evacuating? We expect that measures of perceived severity and susceptibility will correlate significantly with affect and thus plan to include various items to tap into these constructs, for example: Imagine that you lived in the forecasted impact zone. How much do you think this hurricane could harm you and your family and/or your local community? How serious of a threat would this hurricane pose to your family and/or your local community? (1=Not at all; 7=Very much). In addition to beliefs about susceptibility and severity, we will assess subjective norms by measuring endorsement of statements such as the following: My friends and family would like me to evacuate to safer areas. I feel compelled to behave in ways that are consistent with the wishes of my friends and family (1=Strongly agree; 7=Strongly disagree). We will also gauge perceived behavioral control with items such as the following: I feel capable of evacuating to safer areas if ordered to do so. I have the means to evacuate if ordered to do so (1=Strongly agree; 7=Strongly disagree). Finally, we will directly assess behavioral intentions to evacuate with items like the following: Given this information, I would plan on evacuating to safer ground if ordered to do so (1=Strongly agree; 7=Strongly disagree). B.3) Dynamic evolution of storms and behavioral intentions to evacuate People enact their evacuation plans in response to severe weather based on information they receive from a mix of sources. The likelihood of evacuation given a set of storm attributes will be measured using discrete choice experiments (Louviere et al., 2000), which is a statistical technique that originated in mathematical psychology. Discrete choice experiments are based on conjoint analysis techniques, where stated preferences measure intended actions in response to hypothetical storms. The behavioral intentions measured in discrete choice experiments are informative because they are the immediate antecedent of actions. In fact, intentions correspond to the cognitive representation of the strength of an individual s willingness to perform a given action. To pretest behavioral responses to hypothetical storm information, the Qualtrics survey will present evolution of the projected path and other storm information at discrete times before landfall (Wilmot and Gudishala, 2013). At each discrete time, respondents will be asked to state their risk perceptions. In addition, different dimensions of the likelihood of evacuation given the information will be measured. In the next two pages we present two examples of the forecast conditions and questions that will be included in the pilot.

7 7 Forecast 1) Day of Forecast: Thursday / Storm expected to arrive Monday Evaluation of risk How much do you think this hurricane could harm you and your family and/or your local community? (1=Not at all; 7=Very much) How serious of a threat do you think this hurricane could pose to you and your family and/or your local community? (1=Not at all; 7=Very much) Likelihood of evacuation Given this forecast, I believe that my family and/or friends would like me to evacuate to safer areas (1=Not at all; 7=Very much) I strongly value the opinions of my family and/or friends when it comes to evacuation decisions (1=Strongly disagree; 7=Strongly Agree) I would feel capable of evacuating to a safer area if ordered to do so (1=Strongly disagree; 7=Strongly agree) If I saw this forecast, I would make concrete plans to evacuate to a safer area (1=Strongly disagree; 7=Strong agree)

8 8 Forecast 2) Day of Forecast: Friday / Storm expected to arrive Monday Evaluation of risk How much do you think this hurricane could harm you and your family and/or your local community? (1=Not at all; 7=Very much) How serious of a threat do you think this hurricane could pose to you and your family and/or your local community? (1=Not at all; 7=Very much) Likelihood of evacuation Given this forecast, I believe that my family and/or friends would like me to evacuate to safer areas (1=Not at all; 7=Very much) I strongly value the opinions of my family and/or friends when it comes to evacuation decisions (1=Strongly disagree; 7=Strongly Agree) I would feel capable of evacuating to a safer area if ordered to do so (1=Strongly disagree; 7=Strongly agree)

9 9 If I saw this forecast, I would make concrete plans to evacuate to a safer area (1=Strongly disagree; 7=Strong agree) References Ajzen, I. (1985). From intentions to actions: A theory of planned behavior (pp ). Springer Berlin Heidelberg. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), Dash, N., & Gladwin, H. (2007). Evacuation decision making and behavioral responses: Individual and household. Natural Hazards Review, 8(3), Dow, K., & Cutter, S. L. (2002). Emerging hurricane evacuation issues: hurricane Floyd and South Carolina. Natural hazards review, 3(1), Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Lerner, J. S., Gonzalez, R. M., Small, D. A. and Fischhoff, B. (2003). Effects of Fear and Anger on Perceived Risks of Terrorism A National Field Experiment. Psychological science, 14(2), Lindell, M.K., Kang, J.E. and Prater, K.S. (2011). The Logistics of Household Hurricane Evacuation. Natural Hazards 58(3), Lipkus, I. M. and Hollands, J. G. (1999). The visual communication of risk. JNCI Monographs, 1999(25), Louviere, J.J., Hensher, D.A. and Swait, J.D. (2000). Stated choice methods: analysis and application. Cambridge University Press, Cambridge. Morton, T. A. and Duck, J. M. (2001). Communication and Health Beliefs Mass and Interpersonal Influences on Perceptions of Risk to Self and Others. Communication Research, 28(5), Perry, R. W. (2004). Communicating environmental risk in multiethnic communities (Vol. 7). Sage. Perry, R. W. (1979). Evacuation decision making in natural disasters. Mass Emergencies, 4(1), Petty, R. E. and Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and Persuasion (pp. 1 24). Springer New York. Riad, J. K., Norris, F. H., & Ruback, R. B. (1999). Predicting evacuation in two major disasters: risk perception, social influence, and access to Resources. Journal of Applied Social Psychology, 29(5), Sjöberg, L. (2000). Factors in risk perception. Risk analysis, 20(1), Weinstein, N. D., & Sandman, P. M. (1993). Some criteria for evaluating risk messages. Risk Analysis, 13(1),

10 10 Wilmot, C. and Gudishala, R. (2013). Collection of time dependent data using audio visual stated choice. In Zmud, J., Lee Gosselin, M., Munizaga, M. and Carrasco, J.A. (Eds) Transport Survey Methods: Best Practice for Decision Making. Emerald, Bingley, UK. C. COLLABORATORS, PARTNERS, and INTERACTIONS: (List and describe any additional organizations or partners involved in the project, including participation or interactions with CT, NY or NJ Sea Grant extension staff, and industry, agency, or other stakeholder representatives.) D. PROJECT PUBLICATIONS, PRODUCTS, PRESENTATIONS, AND PATENTS: Journal Articles: 0 (List URLs) Conference Papers: 0 Other articles, such as proceedings or book chapters: 0 Web sites, Software, etc.: 0 Technical Reports / Other Publications: 0 Other Products (including popular articles): 0 Planned Publications: 0 Patents: 0 (List those awarded or pending as a result of this project.) Presentations and Posters: 0 (Include name and date of the conference or meeting, whether it was a talk or poster, if it was invited, and who the presenter was.) E. FUNDS LEVERAGED: N/A F. STUDENTS: (Document the number, type, and name of students involved with this project, during this reporting period. Indicate those that received Sea Grant (CSAP) funding. Note: New students are those who have not worked on this project previously. Continuing students are those who have worked on this project previously. If a student volunteered time on this project, please use section G, below.) Total number of new K 12 students: 0 Total number of new undergraduates: 0 Total number of new Master s degree candidates: 0 Total number of new Ph.D. candidates: 0 Total number of continuing K 12 students: 0 Total number of continuing undergraduates: 0 Total number of continuing Master s degree candidates: 0 Total number of continuing Ph.D. candidates: 0

11 11 In the case of graduate students, please list student names, degree pursued, and thesis or dissertation titles related to this project. Student Name: Degree Sought: Thesis or Dissertation Title: Date of thesis completion: Expected date of graduation: G. VOLUNTEER HOURS: N/A H. PICTORIAL: N/A I. HONORS AND AWARDS: N/A