Evacuation Behavior Measured During an Evacuation Order

An Assessment of the Effects of Social Connections on the Decision to Evacuate

Jennifer Collins
University of South Florida

Robin Ersing
University of South Florida

Amy Polen
University of South Florida

Michelle Saunders
University of South Florida

Publication Date: 2017

Abstract

Using Hurricane Irma as a case study, this research investigates evacuation decisions, specifically the influence of social connections on that decision. A survey of those who evacuated and those who did not evacuate was conducted to assess individual social connections by examining three dimensions: dependability, density, and diversity. These variables, together with socioeconomic variables (e.g. race/ethnicity, age, education) were looked at to better explain the influences on evacuation decision making. The surveys of those who evacuated were completed during the evacuation. Those who did not evacuate were surveyed shortly after the hurricane had passed. Such real time and near real time data collection, as opposed to collecting the data sometime after the event, allows for more accurate information since people can better recall the intricacies involved in their decision making. Through statistical analyses, we found that evacuees had significantly more dense and diverse relationships. However, no significant relationship was found between the perceived dependability of a person’s social connections (i.e. their perceived access to resources and support) and the decision to evacuate or not. This study has important implications for adding to the knowledge base on community-based sustainable disaster preparedness and resilience.


The views expressed in the report are those of the authors and not necessarily those of the Natural Hazards Center or the University of Colorado. Quick Response Research Reports capture perishable data on recent events. All analysis is preliminary.


Introduction

This study examines whether social connections affect evacuation behavior at the time of an expected hurricane landfall—specifically, the structural and functional components of social support and connectedness and their relationship to evacuation behaviors during the Hurricane Irma evacuation. Previous research by Collins et al. (2017), using Hurricane Matthew as a case study, found significant differences related to the dependability of one’s social connections—i.e. the functional nature of the social relationship—to determine any tangible type of support or resource available. Those who stayed were found to have more functional support within their social connections. These results will be presented later in this report in relation to the findings from this Hurricane Irma study.

Emergency management leaders at the federal, state and municipal levels stress the need for effective disaster preparedness strategies, including the ability to evacuate from a threatened area. Despite this, evidence from previous hurricanes—such as Hurricane Katrina, which had a devastating impact on the states of Mississippi and Louisiana—suggests that not all households take effective evacuation actions and therefore share responsibility for the outcome (Select Bipartisan Committee 20061). Further research is warranted to increase knowledge about factors that influence and promote successful evacuation decisions.

Two groups were surveyed in this study: 1) individuals who chose to evacuate and leave the area threatened by a land-falling hurricane (i.e., evacuees); and 2) individuals who chose not to evacuate, but instead sheltered in their homes or went to a local shelter (i.e., non-evacuees). Specifically, the density, diversity, and dependability of social support and social connections were examined to understand their influence on decisions of whether or not to evacuate the geographic area threatened by a hurricane predicted to make landfall. This study has important implications for adding to the knowledge of community-based sustainable disaster preparedness and resilience.

The impact of major hurricanes striking the United States has directed attention to the influence of social support and social connections that prompt individuals and families to evacuate when a storm warning is issued (Dash and Gladwin 20072; Gladwin, Gladwin and Peacock 20013; Haines, Beggs, and Hurlbert 2002; 4). The devastating impact—particularly of Hurricane Katrina on New Orleans and the surrounding Gulf Coast communities—heightened the need for advanced knowledge about the way social connections function, especially for vulnerable populations that become socially and economically marginalized (Cutter, Boruff, and Shirley 20035; Moore et al. 20046; Real 20077). As a result, understanding the influence of social connectedness and social support on evacuation behavior remains relevant for disaster mitigation and preparedness planning.

Vulnerability research encompasses geophysical processes, as well as social and economic factors (Cutter and Finch 20088; Tobin and Montz 19979; Wisner et al. 200410). Wisner et al. (2004) define vulnerability as “the characteristics of a person or group and their situation that influence their capacity to anticipate, cope with, resist and recover from the impact of a natural hazard” (11). This conceptualization of vulnerability shifts attention to the role social connections and support play in buffering (protect, shield, and safeguard) people, processes, and places when confronted with exposure to a natural hazard. Much of this thinking is incorporated into the well-known Pressure and Release Model (PAR) developed by Wisner et al. that identifies social and geophysical pressures that can heighten the level of vulnerability for individuals and communities, and thereby contribute to a disaster.

As the research literature on the function of social connectedness in context to natural disasters continues to emerge, we have gathered empirical evidence on the utility of social ties in buffering pathogenic stressors (Caplan 197411; Sarason and Sarason 198512) to inform our conceptual framework for this study. Indeed, several studies have noted that the amount and diversity of social connections can affect health—people whose spouses, friends, and family members provide psychological and material resources experience better health than those with fewer social support structures (Broadhead et al. 198313; Leavy 198314; Mitchell, Billings, and Moos 198215).

Similar studies suggest an inverse correlation with mortality where mortality is greater among persons with relatively low levels of social support (Berkman and Syme 197916; House, Robbins, and Metzner 198217). For the purpose of our work, we consider the role that social support and social connectedness have on evacuation decision making. We draw on the work of Cohen and Wills (1985) who note that social supports act to “buffer” people from potentially pathogenic influences of stressful life events. Likewise, we argue that supportive, functional social connections can buffer people from the deleterious impacts of a hurricane. We see applicability in Cohen and Wills’ work, since their definition of stress closely links stress with feelings of helplessness, which can also be seen in a land-falling hurricane situation.

Cohen and Wills (1985, p314) discuss structural and functional measures of social integration and note that while the former assesses the existence of relationships, it falls short of measuring “the functions actually provided by those relationships”. They further note low correlations between the number of social connections and functional support reported in other studies (e.g., Barrera 198118; Cohen et al. 198219) since “adequate functional support may be derived from one very good relationship, but not be available to those with multiple superficial relationships” (Cohen and Wills 1985, 31520). Cohen et al. (1985) recommend further study of the functional domain of social support, particularly tangible resources on which an individual can depend when coping with an impending stressor, and the utility of social supports within other contexts.

Lakey and Cohen (2000)21 suggest that social supports buffer stressors to the extent that the support type is adequate to meet the demand of the stressor. Therefore, it is important to examine the diversity of an individual’s social connections and the function or dependability of the support available. Miller (2007)22, who explored the dynamics of one East Texas community’s responses to hurricanes Katrina and Rita, acknowledged the important role diverse formal and informal social connections played in sharing information and establishing trust during the evacuations. Both the number of contacts and the range of contacts across different contexts (e.g., faith-based, school, work, etc.) aided in the evacuation process in East Texas.

In some cases, the density or closeness of social connections and ties with individuals who share similar social and economic characteristics has been shown to provide increased sources of support during a natural hazard (Haines et al. 200223; Haines and Hurlbert 199224). The demographic profile of those who did not evacuate during Hurricane Katrina suggested social isolation, stemming from a lack of connectedness with social supports, contributed to vulnerability (Brinkley 200625). Likewise, social connections deemed to be weaker or less dense, but providing greater network diversity, suggested increased access to resources which were harder to obtain (Granovetter 197326; Unger and Powell 198027).

The ways in which social connections influence hazard evacuation behavior remains a critical component for the development of disaster resilience models in mitigation and preparedness planning (Buckle 200628; Dynes 200229). Dynes (2002) posits that “social networks provide the channels whereby individuals develop a perception of risk and can be motivated to take some type of preventative action” (18). While social supports are intuitively thought to play a pivotal role in disaster preparedness and decision making (Mathbor 200730), a paucity of studies on the topic provides mixed results in relations to hazard evacuations (Dynes 2002; Riad and Norris 199831). Therefore, it remains uncertain exactly how informal supports and social connections are used to arrive at a decision to stay or leave, and whether or not the process varies in relation to the social and economic characteristics of individuals and their communities (Moore et al. 2004).

Based on this literature, we proposed the following hypotheses in regard to evacuation status related to Hurricane Irma in September 2017:

  1. People who evacuated out of their county will have a significantly larger number of social connections (i.e., more dense), than those who did not evacuate.

  2. People who evacuated out of their county will have significantly closer social connections (i.e., more dense) than those who did not evacuate.

  3. People who evacuated out of their county will have significantly more varied types of social connections (i.e., more diverse) than those who did not evacuate.

  4. People who evacuated out of their county will have significantly more functionally supportive social connections (i.e., more dependable) than those who did not evacuate.

Figure 1 depicts the conceptual framework that guided this study. Individual characteristics such as ethnicity, gender, age, income, and education often shape the number and types of social connections formed between people and groups.

In this study, we operationalize social connections according to three dimensions: density, diversity, and dependability. Each dimension is assessed from high to low using two validated instruments. Social connection density refers to the number of social ties and the closeness of those relationships. Social connection diversity measures the variety of social relationships (e.g., within or outside the family). Social connection dependability assesses the perceived functional nature of the social relationship to determine any tangible type of support or resource available.

All three social connection and support dimensions are posited to influence behavior examined in this study. Solid arrows in the figure illustrate our deconstruction of social connectedness into the three key dimensions mentioned above. These dimensions are posited to influence evacuation behavior. Dashed arrows indicate possible outcomes resulting from evacuation decision making.

Figure 1

Figure 1: Conceptual Framework

This conceptual framework provides flexibility for exploring additional outcomes beyond the original hypotheses developed for this study. We anticipate using data collected on the dimensions of social connection to develop a predictive model to determine core variables that account for the greatest amount of variation between the two groups. We will also examine interactions among the dimensions of density, diversity, and dependability to further inform knowledge building. Results from this study will allow us to refine the conceptual model, thus providing an extended opportunity to further understand the role of social connections and supports as a tool for sustainable disaster preparedness.

Methodology

During an evacuation, considerable traffic accumulates and vehicles can get stalled for hours (Dow and Cutter 200232). Many evacuees pull over at busy rest areas (Figure 2). This provides an ideal opportunity to survey evacuees.

Figure 2

Figure 2: Traffic congestion as vehicles entered a rest area during the Hurricane Irma evacuation. Photo Credit: Jennifer Collins.

Study Area

The geography of Peninsular Florida provides ideal conditions to conduct these surveys due to the particular vulnerability of the coastal population and the limited options for evacuation routes.

This study surveyed evacuees of Hurricane Irma at a northbound rest area on Interstate 75 in Pasco County, Florida, and at a northbound rest area on the Florida Turnpike in Orange County on September 7, 2017 (the survey team divided into two groups) and at the rest area on the Florida Turnpike on September 8, 2017 (all members went to this location). Surveys of non-evacuees were conducted at a Walmart Neighborhood Market and a Publix supermarket in South Pasadena, Pinellas County, Florida on September 13, as well as at a Lowes Home Improvement store and Hyde Park public area in South Tampa on September 16 (the survey team divided into two groups on the 16).

Figure 3 shows the survey locations. By September 6, the whole peninsula of Florida was within the National Hurricane Center’s Cone of Uncertainty. Evacuations, particularly from South Florida and the Florida Keys, began on September 6. Irma eventually made first U.S. landfall on the southwest Florida coastline at Cudjoe Key, Florida, as a Category 4 storm (130 mph sustained winds) on September 10.

Figure 3

Figure 3: Survey Locations

Data Collection Procedures

Most studies examining evacuation behavior have been conducted post-storm or post-storm season. An important limitation to such studies is that survey participants may be affected by memory decay when considering their past actions and the factors that influenced their decisions (Stallings 200233). Memories change with the passage of time, so the timing of research in relation to hurricane experiences and evacuation decisions is critical. In this study, we avoided that problem by collecting the data for the expected land-falling hurricane as evacuation commenced, interviewing evacuees at an Interstate rest area.

The I-75 and Florida Turnpike location allowed us to collect data from evacuees as they came from the south of Florida and headed north. In addition, data were collected on non-evacuees immediately after the storm passed. This methodology of collecting real-time or near-real-time data is likely to provide more accurate results than previous studies, since people are more likely to remember the intricacies of their decisions, allowing us to gain useful insights. The research team also captured different waves of the evacuation, as some people evacuated earlier than others.

Because of the real-time conditions under which data were gathered, we used a convenience sampling method for this study. We recognize that this is a limitation of our study in that we will not be obtaining a random sample. In her analysis of 225 disaster studies, Norris (2006)34 stresses the prevalence of convenience samples. It is necessary that the sample be representative in order to make inferences about the population at large. Norris (2006) notes that for the studies that used convenience sampling, more than half of the samples were highly or at least moderately representative of their respective populations. Ideally, one would conduct the study using a random sampling method to ensure normalcy and representativeness. However, this is impractical and unrealistic under the conditions at the time of evacuation. Convenience samples do have limitations regarding inferences made to the broader population (Anderson 200135; Herek 200836).

In this context, we considered some parameters that enabled us to obtain as representative of a sample as possible. For example, an immediate question of the participant at the rest-area was to discover if they are travelling with family or friends in more than one car. If so, we excluded them from the survey as there was a chance that we could also interview another family member in another car and break the rule of independence required for a representative sample.

The questionnaire had been pilot tested in English and Spanish to assure clarity of questions. In addition, a successful pilot study was conducted in the summer of 2009 at a rest area along I-75 to assess past and intended evacuation behavior. We acknowledge that this is a different scenario than the real-time evacuation situation; however, this population still had differing levels of social ties. It was noted that people had difficulty recalling who had helped them in the past evacuations and what kind of help they received. Hence, we found this earlier testing supported our research methodology to conduct this project at the time of an actual evacuation. In addition, we were able to test this methodology with Hurricane Matthew in 2016 as people evacuated from east coast to west coast along Interstate 4.

Before the pilot test, a preliminary study was conducted to confirm that the measure of social ties was reasonable to use for the current study. To establish this, the questions that examine social ties were presented to University of South Florida experts in the fields of hazards (colleagues in the Geography Department) and test development (statisticians). The questions were evaluated for face and content validity. The experts had the opportunity to eliminate survey questions if they determined that the questions reflected content that differed from what the questions try to measure (e.g., see Lunsford 200137).

Survey

The 10-minute survey instrument constructed for this study consisted of 62 items that gathered data on the socio-demographics of the interviewee (including age, gender, ethnicity, income, and years of education); previous and current hurricane evacuation experiences; factors influencing evacuation decision making (e.g., access to a vehicle, pet ownership); and the density, diversity, and dependability of social connections. The instrument was administered face-to-face as a paper-and-pencil questionnaires with trained data collectors asking questions and recording responses (Figure 4, 5, and 6).

Figure 4

Figure 4: University of South Florida hurricane research team member Michelle Saunders conducts surveys at a busy rest area. Photo credit: Jennifer Collins.

Figure 5

Figure 5: University of South Florida hurricane research team member, Amy Polen, conducts surveys at the Turkey Lake Plaza off the Florida Turnpike. Photo Credit: Jennifer Collins.

Figure 6

Figure 6: University of South Florida hurricane research team member, Christian Santiago, conducts surveys at the rest area on Interstate 75, north of Land O’ Lakes. Photo Credit: Jennifer Collins.

The survey was anonymous with no personal identifiers. Eligible participants had to be 18 or older and a current Florida resident. Versions of the survey were also available in Spanish, Haitian Creole, and French. A significant portion of the survey incorporates two distinct and well-published questionnaires related to social ties and networks, which are described below.

Berkman-Syme Social Network Index (B-SSNI)

The B-SSNI (Berkman and Syme 1979) provides a measure of the diversity and density of social connections. We use the SNI to assess the type, size, closeness, and frequency of contacts in a respondent’s current social network. This 11-item instrument is a composite measure of several types of social connections: sociability (number and frequency of contact with close relatives and close friends); church group membership; and membership in other community organizations.

Interpersonal Support Evaluation List (ISEL)

The ISEL (Cohen and Hoberman 1983) provides a measure of the functional component of social supports. Cohen and Syme (1985) refer to social supports as those resources that can be offered by other individuals to provide aid or assistance. We use the ISEL to assess the perceived dependability (or functionality) of an individual’s social networks. The 40-item general population version of the ISEL is divided into four subscales. For the purpose of our study, the Tangible Subscale was used, incorporating 10 items to assess specific forms of support the respondent thinks are obtainable from his/her social support network. For example: “If I needed a ride to the airport very early in the morning, I would have a hard time finding someone to take me.” Each item is rated on a four-point Likert scale with anchors ranging from “definitely true” to “definitely false.” Some items are indicated for reverse scoring. Cohen and Wills (1985) report the ISEL to have excellent internal consistency and good test-retest reliability.

Data Analysis Techniques

Data collected for this study were coded and cleaned, and preliminary univariate analyses were conducted. All statistical analyses used SPSS Version 23. Locational data were geocoded and mapped for analyses using ArcGIS 10.3. Our statistical analyses focused on independent t-tests to examine evacuation status compared to each measure (i.e., density, diversity, and dependability) to determine significant differences. We used Mann-Whitney U tests to compare the sources of information participants relied on to inform their evacuation decision, as well as the degree to which they relied on them.

Preliminary Results

Sample Size and Characteristics

Our total sample size was N=208. Those surveyed included 80 early evacuees on September 7 (33 from the I-75 rest area and 47 from the Florida Turnpike rest area), 50 later evacuees on September 8, 30 Pinellas County non-evacuees on September 13, and 48 Hillsborough County non-evacuees on September 16. The groups were combined to create 130 evacuation surveys and 78 non-evacuation surveys.

It was noted that evacuees often came from locations outside of areas that were under a voluntary or mandatory evacuation order. Likewise, some non-evacuees were found to live in areas that were ordered to evacuate. The overall rejection rate was 52.6 percent. When interviewing the evacuees, the rejection rate was higher on September 7 at the I-75 rest area, where more people wanted to use the bathrooms and get back on the road, than it was at the Florida Turnpike rest area, where people spent more time since there was food, gasoline, and charging stations for electric cars. As a result of this lower rejection rate at the Florida Turnpike on September 7, all surveys of evacuees on the following day (September 8) were completed at the Turnpike rest area.

Analyses of the sample revealed the demographic variables that people self-identified the most were as follows: 81.6 percent were between 18-64 years of age; 48.8 percent were female; 61.4 percent were white; 62.2 percent completed a college degree; and 40 percent made $80,000 or more in annual household income. Although our sample was more educated and slightly wealthier than the general population, there were no statistically significant differences in education or income between the evacuee sample and the non-evacuee sample.

Evacuees vs. Non-Evacuees

Using a two-tailed independent samples t-test for difference in means, we examined evacuees compared to non-evacuees (Table 1). It should be noted that only one person surveyed reported going to a shelter. They were in the non-evacuee group since they didn’t leave the geographic area.

When considering the social connection dimension of dependability, measured by the ISEL, results showed no significant differences between evacuees and non-evacuees. However, as measured by the B-SSNI, significant results were found with both social connection dimensions of density in regard to the number of people who are found within their social connections (t(195) = 2.611, p = .01) and how close these ties are (t(195) = 2.659, p = .008). Additionally, the social connection dimension of diversity (t(195) = 3.171, p = .002) was also measured by the B-SSNI index, comparing evacuees to non-evacuees.

When comparing the mean score for density (considering number of ties) between evacuees and non-evacuees, evacuees show a higher mean score (23.11) compared to non-evacuees (19.79) suggesting that those who evacuated perceived that they had more social connections. When comparing the mean score for density (considering closeness of ties) between the two groups, evacuees show a higher mean score (20.88) compared to non-evacuees (17.47). This suggests that those who evacuated perceived that they had closer social ties. Comparing the mean scores for diversity, evacuees show a higher mean score (6.74) compared to non-evacuees (5.90), suggesting that those who evacuated perceived that their social connections were more varied.

We might infer that evacuees felt their larger, closer, and more varied social connections (including groups such as family, friends, church members, volunteer groups) would help facilitate evacuation. Likewise, those who did not evacuate might not have the density and diversity of social connections to help facilitate evacuating. This is supported through the Mann-Whitney U tests which examined evacuees and non-evacuees and sources of information that influenced their decision to evacuate or not. It was found that those who evacuated, compared to those who did not evacuate, relied more heavily on family and friends far away and friends nearby for information that informed their evacuation decision. Note that far away was defined as 50 miles or more. These three factors showed a significant relationship, with (U= 3771.5, p = .002), (U= 3175, p = <.001), and (U= 3984, p = .029) respectively.

Finally, other demographic variables such as race/ethnicity, household income, and level of education were considered in our analyses, but none were significantly related to those who evacuated compared to those who did not.

Hypothesis Variables Tests Results
People who evacuate out of their county will have a significantly larger number of social connections than those who did not evacuate Measures of network size from B-SSNI t-test t(195) = 2.611 , p = .01
People who evacuate out of their county will have significantly more dense social connections (i.e. closer ties) than those who did not evacuate B-SSNI density index t-test t(195) = 2.659, p = .008
People who evacuate out of their county will have significantly more diverse social connections (i.e. varied types) than those who did not evacuate B-SSNI diversity index t-test t(195) = 3.171, p= .002
People who evacuate out of their county will have significantly more dependable social connections (i.e. functional support) than those who did not evacuate ISEL index t-test t(201) = -1.768, p = .079


Table 1: Hypotheses, Variables, Tests, and Results (significant results are bolded)

Previous research from Hurricane Matthew (Collins et al. 201738) showed the importance of dependability in the decision to evacuate or not, with those not evacuating having higher levels of functional support, which indicates non-evacuees felt comfortable hunkering down with their neighbors and local friends. This result, however, was not borne out by data collected during and immediately after Hurricane Irma. This might be a result of the size of the evacuation and the threat of hurricane. While some might have felt comfortable hunkering down initially, the widespread mandatory evacuations meant many people who might have planned to rely on local friends and neighbors saw their support network leave. Further evacuation studies need to focus on social connections to verify these results. 

Experience

During Irma, although 71.5 percent of the evacuees we surveyed were first-time evacuees, there was still a significant difference (p = <.01) between those who evacuated and those who didn’t evacuate in regard to their previous evacuation experience. Those who evacuated had more evacuation experience and past experience with hurricanes. Several evacuees came from in and around Homestead, Florida, an area impacted significantly by Hurricane Andrew in 1992. Many reported that if one had experienced a storm like that, then they would not want to stay the next time.

Broader Impacts

This research will allow the integration of teaching and research on a number of levels:

1) The development of sustainable, community-based disaster preparedness, mitigation, and recovery policies will benefit from approaches that consider the influence of social support and connectedness on evacuation decision-making. Specifically, this study informs whether social connection dimensions such as density (i.e., quantity and closeness of ties), diversity (i.e., a range of relationships), and dependability (i.e., tangible resources) enhance or hinder evacuation behavior. This research has value in attempting to develop an evidence-based model of social supports and evacuation decisions, particularly among populations deemed more vulnerable due to socioeconomic status, ethnicity, and age. Hazard managers will be able to integrate this new knowledge into more targeted disaster education campaigns and planning strategies. Already the director of emergency management of Volusia County has noted that he will use these results when considering future messaging. Likewise, local disaster service organizations (e.g., humanitarian, social service, and faith-based groups) can better understand the role social support and connectedness play in linking individuals to preparedness and recovery services to promote community resiliency. As part of this project, our existing working relationship with the Florida Division of Emergency Management and Florida Department of Transportation will be strengthened. The results of this study will be provided to these agencies to aid future decision making. Therefore, this research allows for community engagement on a variety of scales.

2) These results will be most useful in teaching students about hurricane hazards, particularly in classes such as natural hazards or social interventions. There are also numerous NSF-funded Research Experience for Undergraduates (REU) programs across the nation, such as the one focused on weather, climate, and society at the University of South Florida, beginning this summer 2018. These provide an ideal opportunity to present results to students and further the research.

3) Results were presented at several professional disciplinary conferences, such as the Southeast Division of the American Association of Geographers in November 2017, and will be presented at future meetings such as the national American Association of Geographers in April 2018. Results were also shared at a special working hurricane meeting held by the University of Central Florida in November 2017. Results will also be disseminated through top tier peer-reviewed journals. We have already submitted our first paper.

4) This project has encouraged graduate and undergraduate students to conduct research—some for the first time—with an interdisciplinary team (from the fields of geography and social work). The grant allowed the training of several undergraduate and graduate students. These students are undertaking directed and independent study projects related to various aspects of the research and will produce written reports of their work. This will inform publications related to this study. As part of this process, the students will gain first-hand knowledge of the scientific method, research and analytical techniques, and skills related to scientific writing.

Future Work

The success of this preliminary study has instilled high confidence in our research protocol and our training and preparation of student data collectors. This preliminary study also allowed the team to identify and address areas of improvement which can be integrated into future protocols. One major methodological limitation was the inability to capture truly late evacuees. Although we collected data over two days, we had to stop as the storm impacted our home area and we made preparations for our own families and houses. Therefore our initial hypotheses, which considered early vs. late evacuees, had to be modified to those presented in this report. In future, we would like to reconsider these original hypotheses—comparing early versus late evacuation behavior as we think this is an important consideration worthy of study.

Acknowledgements

We would like to acknowledge additional members of the University of South Florida hurricane team for their fieldwork, including Emily Cerrito, Saurav Chakraborty, Christian Santiago, Simran Gill, Vikrant Pendharkar, Luwen Wang, Sinjana Kolipaka, and Brad Perich. We would also like to acknowledge Douglas Lunsford for his statistical advice in analyzing an aspect of this dataset. Research related to the evacuee part of the study was additionally funded from a National Science Foundation RAPID grant (Award #BCS-1760235).

References


  1. Select Bipartisan Committee to Investigate the Preparation for and Response to Hurricane Katrina. 2006. A Failure of Initiative. Report No. 109-377. Washington, DC: U.S. Government Printing Office. 

  2. Dash, Nicole and Hugh Gladwin. 2007. “Evacuation Decision Making and Behavioral Responses: Individual And Household.” Natural Hazards Review 8 (3): 69-77. 

  3. Gladwin, Christina, Hugh Gladwin, and Walter Gillis Peacock. 2001. “Modeling Hurricane Evacuation Decisions with Ethnographic Method.” International Journal of Mass Emergencies and Disasters 19: 117-143. 

  4. Whitehead, John, Bob Edwards, Marieke van Willigne, John Maiolo, Kenneth Wilson, and Kevin Smith. 2000. “Heading For Higher Ground: Factors Affecting Real and Hypothetical Hurricane Evacuation Behavior.” Environmental Hazards 2: 133-142. 

  5. Cutter, Susan, Bryan Boruff, and William Shirley. 2003. “Social Vulnerability to Environmental Hazards.” Social Sciences Quarterly 84: 242-261. 

  6. Moore, Spencer, Mark Daniel, Laura Linnan, Marci Campbell, Salli Benedict, and Andrea Meier. 2004. “After Hurricane Floyd Passed: Investigating The Social Determinants of Disaster Preparedness and Recovery.” Family Community Health 27 (3): 204-217. 

  7. Real, Byron. 2007. “Hard Decisions in the Big Easy: Social Capital and Evacuation of the New Orleans Area Hispanic Community During Hurricane Katrina.” In Perspectives on social vulnerability, edited by Koko Warner, 72-83. United Nations University: Institute for Environment and Human Security. 

  8. Cutter, Susan, and Christina. Finch. 2008. “Temporal and Spatial Changes in Social Vulnerability to Natural Hazards.” Proceedings of the National Academy of Sciences 105 (7): 2301-2306. 

  9. Tobin, Graham, and Burrell Montz. 1997. Natural Hazards: Explanation and Integration. New York: Guilford Press. 

  10. Wisner, Ben, Piers Blaikie, Terry Cannon, and Ian Davis. 2004. At risk: Natural Hazards, People’s Vulnerability and Disasters (2nd ed.). United Kingdom: Routledge. 

  11. Caplan, Gerald. 1974. Support Systems and Community Mental Health. New York City: Behavioral Publications. 

  12. Sarason, Irwin, and Barbara Sarason, eds. 1985. Social Support: Theory, Research and Application. The Netherlands: Martinus Nijhoff. 

  13. Broadhead, W. Eugene, Berton Kaplan, Sherman James, Edward Wagner, Victor Schoenbach, Roger Grimson, Siegfried Heyden, Gosta Tibblin, and Stehpen Gehlbach. 1983. “The Epidemiologic Evidence for a Relationship Between Social Support and Health.” American Journal of Epidemiology 117: 521-537. 

  14. Leavy, Richard. 1983. “Social Support and Psychological Disorder: A Review.” Journal of Community Psychology 11: 3-21. 

  15. Mitchell, Roger., Andrew Billings, and Rudolph Moos. 1982. “Social Support and Well-Being: Implications for Prevention Programs.” Journal of Primary Prevention. 3: 77-98. 

  16. Berkman, Lisa, and S. Leonard Syme. 1979. “Social Networks, Host Resistance, and Mortality: A Nine-Year Follow-Up Study of Alameda County Residents.” American Journal of Epidemiology 109: 186-204. 

  17. House, James, Cynthia Robbins, and Helen Metzner. 1982. “The Association of Social Relationships and Activities With Mortality: Prospective Evidence From The Tecumseh Community Health Study.” American Journal of Epidemiology 116: 123-140. 

  18. Barrera, Manuel, Jr. 1981. “Social Support in the Adjustment of Pregnant Adolescents: Assessment Issues.” Social networks and Social Support 4: 69-96. 

  19. Cohen, Patricia, Elmer Struening, Gregory Muhlin, Louis Genevie, Seymour Kaplan, and Harris Peck. 1982. “Community Stressors, Mediating Conditions, and Well-Being in Urban Neighborhoods.” Journal of Community Psychology 10: 377-391. 

  20. Cohen, Sheldon, and Thomas Wills. 1985. “Stress, Social Support, and the Buffering Hypothesis.” Psychological Bulletin 98: 310-357. 

  21. Lakey, Brian, and Sheldon Cohen. 2000. “Social Support Theory and Selecting Measures of Social Support.” In Social Support Measurement and Interventions: A Guide for Health and Social Scientists, edited by Sheldon Cohen, Lynn Underwood, and Benjamin Gottlieb, 3-28. Oxford: University Press. 

  22. Miller, Lee. 2007. Collective disaster responses to Katrina and Rita: Exploring therapeutic community, social capital, and social control. Southern Rural Sociology 22(2). 

  23. Haines, Valerie, John Beggs, and Jeanne Hurlbert. 2002. “Exploring Structural Contexts of the Support Process: Social Networks, Social Statuses, Social Support, and Psychological Distress.” Advances in Medical Sociology 8: 271–294. 

  24. Haines, Valerie, and Jeanne Hurlbert. 1992 “Network Range and Health.” Journal of Health and Social Behavior 33: 254-266. 

  25. Brinkley, Douglas. 2006. The Great Deluge: Hurricane Katrina, New Orleans, and the Mississippi Gulf Coast. New York: Harper Perennial. 

  26. Granovetter, Mark. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78 (6): 1360-1380. 

  27. Unger, Donald, and Douglas Powell. 1980. “Supporting Families Under Stress: The Role of Social Networks.” Family Relations 29 (4): 566-574. 

  28. Buckle, Phillip. 2006. “Assessing social resilience.” In Disaster resilience: An Integrated Approach, edited by Douglas Paton and David Johnston, 88-104. Springfield, IL: Charles C. Thomas. 

  29. Dynes, Russell. 2002. “The Importance of Social Capital in Disaster Response.” Preliminary Paper #327. University of Delaware Disaster Research Center. 

  30. Mathbor, Golam. 2007. “Enhancement of Community Preparedness For Natural Disasters: The Role of Social Work in Building Social Capital for Sustainable Disaster Relief and Management.” International Journal of Social Work 50 (3): 357-369. 

  31. Riad, Jasmin, and Frank Norris. 1998. “Hurricane Threat and Evacuation Intentions: An Analysis of Risk Perception, Preparedness, Social Influence, and Resources.” Preliminary Paper #271. University of Delaware Disaster Research Center. 

  32. Dow, Kirstin, and Susan Cutter. 2002. “Emerging Hurricane Evacuation Issues: Hurricane Floyd and South Carolina.” Natural Hazards Review 13 (1): 12-18 

  33. Stallings, Robert. ed. 2002. Methods of disaster research. Bloomington, IL: Xlibris. 

  34. Norris, Fran. 2006. “Disaster Research Methods: Past Progress and Future Directions.” Journal of Traumatic Stress 19 (2): 173-184. 

  35. Anderson, David. 2001. “The Need to Get the Basics Right in Wildlife Field Studies.” Wildlife Society Bulletin 29: 1294-1297. 

  36. Herek, Gregory. 2008. “A Brief Introduction to Sampling.” Available at http://psychology.ucdavis.edu/rainbow/html/fact_sample.html 

  37. Lunsford, Douglas. 2001. “Effect of Test Anxiety, Study Anxiety, Study Habits, Test Taking Skills and Academic Problems on GPA.” Master’s Thesis, University of South Florida. 

  38. Collins, Jennifer, Robin Ersing, and Amy Polen. 2017. “Evacuation Decision Making during Hurricane Matthew: An Assessment of the Effects of Social Connections.” Weather, Climate and Society 9 (4), 769-776.