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  • View in gallery
    Fig. 1.

    Cars lining up to get into rest area on Florida’s Turnpike on 8 Sep 2017. Photo by J. Collins.

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    Fig. 2.

    Coauthor Michelle Saunders, a member of the University of South Florida hurricane research team, completing surveys. Photo by E. Cerrito.

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    Fig. 3.

    Location of our surveys.

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    Fig. 4.

    Cone of uncertainty on 6 Sep 2017.

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The Effects of Social Connections on Evacuation Decision Making during Hurricane Irma

Jennifer CollinsSchool of Geosciences, University of South Florida, Tampa, Florida

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Robin ErsingSchool of Public Affairs, University of South Florida, Tampa, Florida

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Amy PolenSchool of Geosciences, University of South Florida, Tampa, Florida

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Michelle SaundersSchool of Geosciences, University of South Florida, Tampa, Florida

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Jason SenkbeilDepartment of Geography, University of Alabama, Tuscaloosa, Alabama

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Abstract

This study investigates the influence of individuals’ social connections in their decision to either evacuate or not evacuate in the days preceding the landfall of Hurricane Irma. Using Hurricane Irma in September 2017 as a case study, a survey was conducted on two groups (those who evacuated and those who did not evacuate) to assess people’s social connections specifically examining three dimensions: dependability, density, and diversity. These variables, together with socioeconomic variables (e.g., race/ethnicity, age, education), were considered in order to better explain the influences on evacuation decision-making. To collect accurate ephemeral decision-making data from evacuees, the surveys were completed during the evacuation for those who evacuated and shortly after the passage of Hurricane Irma for those who did not evacuate. Through statistical analyses, it was concluded that density and diversity of people’s social networks played a significant role in the decision to evacuate or not, with evacuees having more dense and diverse relationships. On the other hand, the perceived dependability of a person’s social connections (i.e., their perceived access to resources and support) did not significantly impact the decision to evacuate for Hurricane Irma. This study has important implications for adding to the knowledge base on community-based sustainable disaster preparedness and resilience.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jennifer Collins, collinsjm@usf.edu

Abstract

This study investigates the influence of individuals’ social connections in their decision to either evacuate or not evacuate in the days preceding the landfall of Hurricane Irma. Using Hurricane Irma in September 2017 as a case study, a survey was conducted on two groups (those who evacuated and those who did not evacuate) to assess people’s social connections specifically examining three dimensions: dependability, density, and diversity. These variables, together with socioeconomic variables (e.g., race/ethnicity, age, education), were considered in order to better explain the influences on evacuation decision-making. To collect accurate ephemeral decision-making data from evacuees, the surveys were completed during the evacuation for those who evacuated and shortly after the passage of Hurricane Irma for those who did not evacuate. Through statistical analyses, it was concluded that density and diversity of people’s social networks played a significant role in the decision to evacuate or not, with evacuees having more dense and diverse relationships. On the other hand, the perceived dependability of a person’s social connections (i.e., their perceived access to resources and support) did not significantly impact the decision to evacuate for Hurricane Irma. This study has important implications for adding to the knowledge base on community-based sustainable disaster preparedness and resilience.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jennifer Collins, collinsjm@usf.edu

1. Introduction

There are a myriad of factors that affect evacuation decision making from concern over the geophysical hazards of the storm, such as the winds and storm surge, to social elements including age, gender, and race. Sociobehavioral and sociocultural indicators in particular have been a concentrated area of focus in the scholarly literature, resulting in an accumulation of knowledge that spans nearly 40 years (Baker 1979, 1991, 1995; Dow and Cutter 1998, 2000; Sherman-Morris et al. 2011; Demuth et al. 2012; Morss et al. 2016). Within this realm, the role of social capital, specifically in the form of family ties and friendships, has gained growing attention. For example, several studies have examined the effect of social connections and social support in encouraging individuals and families to evacuate with the issuance of a storm warning (Dash and Gladwin 2007; Gladwin et al. 2001; Haines et al. 2002; Whitehead et al. 2000). Indeed, Dynes (2002) asserts that one’s risk perception is developed via social networks and, with this, one is then motivated to act by taking preventative action. Our interest lies in addressing one of those social factors, specifically the influence of social connections and support, which re-emerged as an important component for disaster mitigation and preparedness planning after Hurricane Katrina in 2005.

a. Social connections

The death and devastation that impacted New Orleans and adjacent parts of the Gulf Coast region well over a decade ago heightened the urgency to better prepare vulnerable populations who have become both socially and economically marginalized (Cutter et al. 2003; Moore et al. 2004; Real 2007). One approach has been to better understand the role social connections play in the decision to evacuate. A social connection refers to relationships an individual may have with the people around them including family, friends, coworkers, or more casual acquaintances (Collins et al. 2017). Moore et al. (2004) note that it is unclear how people’s social supports and connections are considered in the decision-making process of whether to evacuate or not, and also whether certain social and economic characteristics of individuals and their communities affect this process. For example, studies have examined the role of social connections within impoverished ethnic minority communities to determine whether these networks help or hinder the decision to evacuate (Eisenman et al. 2007; Elder et al. 2007). During Hurricane Katrina, Eisenman et al. (2007) found that strong ties through extended kinship, friends, and community groups contributed to evacuation decision making behavior with regard to whether these connections provided access to transportation and safe shelter. In some cases, social networks were reported to influence how evacuation messages were perceived. Likewise, Elder et al. (2007) reported that the influence of strong family ties, particularly with elderly individuals or those with special needs, hindered the decision to evacuate. Indeed both studies illustrate the dual edge to social connections. In some cases one’s networks and support systems are able to offer resources to aid in evacuating, while in other instances these same ties hinder that decision.

b. Dimensions of social connections

Wisner et al.’s (2004) Pressure and Release (PAR) model identifies various social and geophysical pressures that are believed to heighten the level of vulnerability for individuals and communities, thereby contributing to a disaster. The PAR model incorporates the idea of vulnerability in terms of social connections and supports, and the role these play in buffering people, processes, and places when confronted with exposure to a natural hazard.

Caplan (1974) and Sarason and Sarason (1985) note that negative stressors can be “buffered” by social ties so that people are protected from harmful influences. Cohen and Wills (1985) provide additional evidence of this buffering by suggesting that structural measures of social connectedness assess the existence of relationships. Structural measures refer to things like the number of friendships and the different types of social relationships one has in one’s life (Cohen et al. 1985). But one also has to look at the functions actually provided by those relationships through functional measures such as tangible resources like goods and services. Barrera (1981) and Cohen et al. (1982) found low correlations between the number of social connections and functional support. Cohen and Wills (1985), however, suggest that this is because one only needs one very good relationship to provide adequate functional support. On the other hand, others could have numerous social connections but none good enough to provide that functional support. Cohen et al. (1985) therefore suggest further study is needed considering this provision of functional social support, particularly in the event of an impending stressor. Our conceptual framework, postulated in Collins et al. (2017), considers the role social connectedness and support have on people’s decision to evacuate. The number and types of social connections are often shaped by characteristics such as ethnicity, gender, age, income, and education. In our conceptual model, social connections are operationalized according to three dimensions: density, diversity, and dependability. Density measures the number and the closeness of the social connections, diversity assesses the variety of social connections, and dependability assesses the perceived functional support of the social connections to determine the form of tangible support or resources. We hypothesize that the decision to evacuate or not evacuate is an outcome of these three dimensions of social connections (nonevacuation includes those who sheltered in place or went to a shelter as they did not leave the county).

The role of density, diversity, and dependability during evacuations is born out of other studies. Miller (2007) acknowledges that the number of contacts (density) as well as the range of contacts (diversity) across different social roles aided in the evacuations from Katrina and Rita in 2005 as these allowed for information to be shared and trust to be established. Haines et al. (2002) and Haines and Hurlbert (1992) have shown that greater functional support (dependability) has been provided during a hazard by individuals who have dense and close connections sharing common social and economic characteristics. However, Granovetter (1973) and Unger and Powell (1980) note that this functional support, and increased access to hard to obtain resources, can also be obtained with weaker or less dense social connections as long as there is greater diversity within people’s connections.

Brinkley (2006) who looked at the demographics of those who did not evacuate during Hurricane Katrina noted that one’s vulnerability was affected by social isolation, which resulted from a lack of connectedness. Collins et al. (2017) however, found no significant differences when considering the density and diversity of one’s social connections and their decision to evacuate or not. They did, however, find a significant difference with regards to dependability, such that nonevacuees actually had higher levels of functional support. This may have the effect of decreasing their vulnerability as local friends and neighbors can assist each other with preparing their houses for the storm and other needed preparations. On the other hand, it may increase their vulnerability if the residents are in a mandatory evacuation or storm surge risk area. Certainly, Collins et al. (2017) note that there were some people in areas under a mandatory evacuation who did not evacuate.

The three dimensions of social connectedness in this study are also encompassed in the broader construct of resilience. Understanding the density, diversity, and dependability of social ties and connections adds to one’s resiliency capacity (Paton and Johnston, 2001). A growing body of knowledge on reducing vulnerability through personal and community social capital has spurred the development and refinement of predictive models. Protective action models are anticipated to aid the emergency management field to better understand factors that influence evacuation decision-making behavior.

c. Protective action and social connections

Social networks can be viewed as a tangible form of social capital, resulting in the formation of trusting ties between people and links to support systems that can help to mitigate the impact of a disaster. Besides assessing vulnerability as a method for understanding the potential to cope with a natural hazard, risk reduction models also assess capacities. This alternative approach emphasizes both internal and community-based strengths and assets that can be used to buffer the negative effects of a natural hazard (Tobin 1999).

Resiliency is often described as the ability to “bounce back” or to return to a state of functioning that was in place prior to exposure to a significant stressor (e.g., a natural hazard). Paton (2006) reminds us that any attempt to return to a normal routine postdisaster is hindered by the changed physical and social environment attributed to the impact of the hazard event. Therefore, it is often necessary for individuals to rely upon personal protective factors, which manifest as coping skills in the recovery process. An example would be the reliance on social ties with family, friends, and neighbors in order to connect survivors to systems of mutual support.

The Protective Action Decision Model (PADM) is a social sciences model used to predict an individual’s response to a disaster. The PADM consists of multiple stages of information processing followed by reactions to information taken by an individual. The first stage, the predecisional phase, consists of information gathering from risk communications and environmental cues. Based on the outcome of this phase, the next stage of decision can vary; this decision stage includes eight steps including risk identification, assessment, and, if the threat is deemed important enough, a protective action assessment, communication action, and information needs assessment (Lindell and Perry 2004). In later work, the main components have been preserved but perceptions have been modified. These perceptions represent the factors that affect an individual’s response to a threat, and can be represented by threat, protective action, and stakeholder perceptions (Lindell and Perry 2012).

The PADM is also different from other risk assessment models as it can be implemented in the short term for an individual whereas other models such as the Precaution Adoption Process Model (PAPM) operate on a longer time scale (Weinstein 1988), making it a useful tool for predicting behavioral response during incidents such as a hurricane evacuation that happen over the span of a few days. This model has been used in varying fields of study, including risk communication development, evacuation modeling, and hazard adjustment and mitigation over the longer term (Lindell and Perry 2012).

This study uses the largest evacuation in the history of the United States (Bousquet and Klas 2017) to assess the roles that density, diversity, and dependability of social connections play with regards to evacuation status during Hurricane Irma in September 2017. Specifically, we examined the following hypotheses: 1) People who evacuate out of their county will have a significantly larger number of social connections than those who did not evacuate. 2) People who evacuate out of their county will have significantly more dense social connections (i.e., closer ties) than those who did not evacuate. 3) People who evacuate out of their county will have significantly more diverse social connections (i.e., varied types) than those who did not evacuate. 4) People who evacuate out of their county will have significantly more dependable social connections (i.e., functional support) than those who did not evacuate. This research is important due to the paucity of studies that consider the role that social supports are thought to play in disaster preparedness and decision-making, and the mixed results with regard to evacuations from hazards (Dynes 2002; Riad and Norris 1998). Ultimately, we argue that both structural and functional social connections may buffer people from the impact of a deleterious hurricane. This study has important implications since the development of disaster resilience models as part of mitigation and preparedness planning must consider how social connections are utilized to influence hazard evacuation behavior (Buckle 2006; Dynes 2002). Furthermore, our results may provide valuable insight about how social connectivity variables can inform behavior and decision making models.

2. Methodology

a. Study area

The geography of peninsular Florida provides an ideal state to conduct these surveys due to the vulnerability of the coastal population in particular and the limited options for evacuation routes. Considerable traffic accumulates where vehicles can get stalled for hours during an evacuation (Dow and Cutter 2002). Many evacuees pull over at busy rest areas (Fig. 1), providing an ideal opportunity to survey evacuees (Fig. 2). 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 7 September (the survey team divided into two groups) and at the same rest area on the Florida Turnpike on 8 September (all members went to this location). Surveys of nonevacuees were conducted at a Walmart neighborhood market and Publix in South Pasadena in Pinellas County on 13 September and a Lowes and Hyde Park public area in South Tampa on 16 September (the survey team divided into two groups on the 16th). We considered it necessary for the safety of our team to collect data of nonevacuees in public areas, rather than going door to door in residential areas. The sites chosen were close to areas that had been under mandatory evacuation orders, and were locations where we received permission. Figure 3 shows the survey locations. By 6 September, the whole peninsula of Florida was within the National Hurricane Center’s (NHC) cone of uncertainty (COU) (Fig. 4). Florida residents and visitors saw Hurricane Irma become a record-breaking storm as it became the strongest storm on record to exist in the Atlantic Ocean outside of the Caribbean and Gulf of Mexico (with 185 mph lifetime maximum winds). Irma spent over three days as a category 5 storm—the longest in the Atlantic in the postsatellite era (after 1966)—and hit islands in the Caribbean and Cuba as a category 5 on the Saffir–Simpson hurricane scale. The NHC 5-day COU official track forecasts throughout the day on 6 September showed the center of Irma traveling directly over Miami, although anywhere in the cone was considered a possibility. Irma eventually made first U.S. landfall at Cudjoe Key, Florida, as a category 4 (130 mph sustained winds) on 10 September on the southwest Florida coastline. Evacuations began, particularly from South Florida and the Florida Keys, on 6 September. On 7 September, Governor Rick Scott warned Florida residents to take Irma seriously. On 8 September, the National Weather Service Key West tweeted a dire warning: “***THIS IS AS REAL AS IT GETS*** ***NOWHERE IN THE FLORIDA KEYS WILL BE SAFE*** ***YOU STILL HAVE TIME TO EVACUATE***.” While several major cities (including Tampa) were threatened with a catastrophic event, many avoided such impacts as the storm interacted with a trough at landfall and then tracked up the center of the state undergoing extratropical transition. Estimated numbers indicate more than 6.5 million residents had been under a mandatory evacuation order (Held 2017).

Fig. 1.
Fig. 1.

Cars lining up to get into rest area on Florida’s Turnpike on 8 Sep 2017. Photo by J. Collins.

Citation: Weather, Climate, and Society 10, 3; 10.1175/WCAS-D-17-0119.1

Fig. 2.
Fig. 2.

Coauthor Michelle Saunders, a member of the University of South Florida hurricane research team, completing surveys. Photo by E. Cerrito.

Citation: Weather, Climate, and Society 10, 3; 10.1175/WCAS-D-17-0119.1

Fig. 3.
Fig. 3.

Location of our surveys.

Citation: Weather, Climate, and Society 10, 3; 10.1175/WCAS-D-17-0119.1

Fig. 4.
Fig. 4.

Cone of uncertainty on 6 Sep 2017.

Citation: Weather, Climate, and Society 10, 3; 10.1175/WCAS-D-17-0119.1

b. Data collection procedures

A critical element of the data collection was to capture the information from evacuees at the time of their evacuation and information from nonevacuees shortly after Hurricane Irma passed through. This real-time (in the case of the evacuees) and near-real-time data collection (in the case of the nonevacuees) allows for participants to more accurately recount the intricacies of their decision-making process, allowing us to gain useful insight. Stallings (2002) notes that the potential for “memory decay” is a limitation to some evacuation studies when the data are collected sometime after the event. Not only is there the potential for memory decay, but Baker (1979, 1991) and Lindell et al. (2005) note that people’s ideas and perceptions are also likely to have been altered with time. Brewer (2000) notes that when people attempt to recall their social networks, that this is a particular problem. Interviewing evacuees at busy interstate rest areas has previously been conducted by Senkbeil et al. (2010), Brommer and Senkbeil (2010), and Collins et al. (2017), the last of whom also interviewed nonevacuees immediately after the passage of Hurricane Matthew in 2016. The two rest areas sampled in this research (see Fig. 3) allowed us to capture evacuees primarily from southern Florida with many heading north intending to leave the state. Given the real-time conditions under which data were collected, convenience sampling was used, and data were gathered only from one person from a family or group in order to follow the rule of independence. In an analysis of 225 disaster studies, Norris (2006) noted the prevalent use of convenience sampling. The only incentive offered to the participant to participate was the opportunity to be entered into a drawing for a $250 gas gift card. This study protocol was approved under the American Public University System (APUS) Institutional Review Board (IRB) IRB# Pro00027740.

c. Survey

A survey instrument, containing 62 items, was used to collect data on factors influencing evacuation decision-making; specifically, the density, diversity, and dependability of social connections, as well as information on previous and current hurricane evacuation experiences and sociodemographic data. Two validated instruments that are related to research on social ties and networks were used: 1) the Berkman–Syme Social Network Index (B-SSNI; Berkman and Syme 1979), which measures the diversity and density of an individual’s social connections, and 2) the Interpersonal Support Evaluation List (ISEL; Cohen and Hoberman 1983; Cohen et al. 1985), which measures the functional component (dependability) of social support. Both measures were selected for their ability to capture components of stress buffering through structural and functional aspects of social relationships using a short set of items. In addition, the B-SSNI and ISEL instruments have been well vetted in the scholarly literature [e.g., Brissette et al. (2000) and Lubben and Gironda (2004), respectively]. There were a total of 18 questions from the B-SSNI, which was used to determine both the diversity and density scale. The instrument focuses on four components: 1) marital status, 2) contact with friends and family, 3) church membership, and 4) group membership. Both the number of social connections and relative importance of social ties across the categories are assessed. There were an additional 12 questions used to determine the dependability measure with the ISEL. However, we use only one of the four subscales of the ISEL, the “tangible subscale,” to assess a specific form of support the respondent believes can be obtained from his/her social connections. Each item on the ISEL is rated on a four-point Likert scale with anchors ranging from “definitely true” to “definitely false.”

An example question used to determine density (number) from the survey is as follows:

  • 46. How many close friends do you have? (meaning people that you feel at ease with, can talk to about private matters, and can call on for help) (If ‘0’, check that space and skip to question 48.)

    • ____0 ____1 ____2 ____3 ____4 ____5 ____6 ____7 or more

An example question used to determine diversity (variety) from the survey is as follows:

  • 53. Are you currently involved in regular volunteer work? (If not, check ‘no’ and skip to question 55.)

    • _____ no _____ yes

An example question used to determine dependability is as follows:

  • 25. There are several people that I trust to help solve my problems

    • ____definitely true (3) ____probably true (2) ____ probably false (1) ____definitely false (0)

The ISEL has excellent internal consistency and good test–retest reliability (Cohen et al. 1985). The complete scale reports a Cronbach’s alpha of 0.77, while the tangible support subscale measures 0.71 for internal reliability (Cohen and Hoberman 1983). The B-SSNI reports a Cronbach’s alpha of 0.84 (Sykes et al. 2002). More detail on the use of these instruments can be found in Collins et al. (2017). The survey took approximately 10 minutes to administer.

d. Data analysis techniques

After data were coded and cleaned, two-tailed independent samples t tests for difference in means were conducted to examine evacuation status compared to each social connection measure (density, diversity, dependability). Mann–Whitney U tests were performed to compare sources of information participants relied on to inform their evacuation status, and the degree to which they relied on them. These different tests were used due to the varying types of data collected from the survey, some being continuous, some categorical, and others ordinal.

3. Results

a. Sample size and characteristics

Our total sample size was N = 208. There were 130 evacuation surveys and 78 nonevacuation surveys. The evacuation surveys included 80 early evacuees on 7 September (33 from the I-75 rest area and 47 from the Florida Turnpike rest area) and 50 later evacuees on 8 September. The nonevacuation surveys included 30 Pinellas County nonevacuees on 13 September and 48 Hillsborough County nonevacuees on September 16th. It was noted that evacuees often came from locations outside of areas which were under a voluntary or mandatory evacuation order. Likewise, some nonevacuees were found to live in areas that were ordered to evacuate. The overall rejection rate was 52.6%. When interviewing the evacuees, the rejection rate was higher at the I-75 rest area where more people wanted to use the bathroom facilities and then get back on the road than at the Florida Turnpike rest area on 7 September where people spent more time at the rest area since there was food and gasoline (and charging stations for electric cars). As a result of this lower rejection rate at the Florida Turnpike, all surveys of evacuees were completed there on 8 September. Analyses of the sample revealed the category of each demographic variable with which people self-identified the most; for example, 81.6% of people were between 18 and 64 years of age and 18.4% identified as 65 years and older; 48.8% were female and 50.5 were male; 61.4% were white, 17.3% identified as Latino, 9.1% identified as African American, Asian/Pacific Islanders composed 3.6%, and 8.2% identified as other; and 62.2% had completed a college degree, 26.5% had completed some college, 7.7% had high school or GED education, and 3.6% did not complete high school. Also, 40% made $80,000 or higher in annual household income, and 13.1% made between $60,000 and 69,999. The other $10,000 ranges for income each comprised 10% or less of the survey population. 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 nonevacuee sample. It should be noted that there were several categories that the participant could choose for some of these demographics. For instance, when considering income participants had the choice to self-identify with eight different income ranges. For education, they could choose four different levels.

b. Evacuees versus nonevacuees

Using two-tailed independent sample t tests for difference in means, we examined evacuees compared to nonevacuees (Table 1). It should be noted that only one person surveyed reported going to a shelter. They were in the nonevacuee group as they did not leave the geographic area. When considering the social connection dimension of dependability, measured by the ISEL, results showed no significant differences between evacuees and nonevacuees. However, as measured by the B-SSNI, significant results were found with both social connection dimensions of density in terms of how many people are found in their networks t(195) = 2.611, p = 0.01, and how close these ties are t(195) = 2.659, p = 0.008, as well as the social connection dimension of diversity t(195) = 3.171, p = 0.002 also measured by the B-SSNI index, comparing evacuees to nonevacuees. When comparing the mean score for density (considering number of ties) between the two groups (evacuees and nonevacuees), evacuees show a higher mean score (23.11) compared to nonevacuees (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 and nonevacuees), evacuees show a higher mean score (20.88) compared to nonevacuees (17.47), suggesting 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 nonevacuees (5.90), suggesting that those who evacuated perceived that their social connections were more varied.

Table 1.

Hypotheses, variables, tests, and results (significant results shown in bold).

Table 1.

We might infer that evacuees felt that with their larger, closer, and more varied (such as family, friends, church members, and volunteer groups) social connections that this would help facilitate an evacuation. Likewise, those who did not evacuate may not have the density and diversity of social connections to help facilitate an evacuation. This is supported through the Mann–Whitney U tests that examined evacuees and nonevacuees and sources of information relied on influencing 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 far away, friends far away, and friends nearby for information, which informed their evacuation decision (note that “far away” was defined as 50 miles or more). These three factors showed a significant relationship, with values of (U = 3771.5, p = 0.002), U = 3175, p = <0.001, and U = 3984, p = 0.029, respectively. Smith and McCarty (2009) found a similar relationship in their study of Florida residents who evacuated during the active 2004 hurricane season, with 51% of those surveyed choosing to evacuate to another county or another state to be with family or friends.

Finally, other demographic variables such as race/ethnicity, household income, and level of education were considered in our analyses, but none had significant relationships with those who evacuated compared to those who did not. Furthermore, none of the social ties had any significant relationship with ethnicity. However, it should be noted that the dependability measure showed a significant positive relationship with both education and income (p = < 0.05). Both measures of density and the diversity showed a significant relationship with income (p = < 0.05), with the higher an individual’s annual income, the higher the social connection measure.

4. Conclusions

There are a myriad of social and geophysical variables that influence evacuation decision making. The associations between these are complex and affect the decision to evacuate differently for each individual or household. There have been several evacuation behavior studies that have investigated meteorological variables and hazards related to the decision-making process (Brommer and Senkbeil 2010; Senkbeil et al. 2010; Stein et al. 2010; Zhang et al. 2007). Using the largest evacuation in the history of the United States (Bousquet and Klas 2017), which occurred due to Hurricane Irma in September 2017, this study considers the role that social connections play on hurricane evacuation behavior. Such social connections emerged as having a strong influence particularly after Hurricane Katrina in 2005. This research showed, through statistical analyses, that the density and diversity dimensions of social connections have a significant effect on the decision to evacuate, with those who have more perceived connections, closer connections, and varied connections tending to evacuate. Certainly, when people have access to more people through a variety of ties such as churches or volunteer groups, they have the additional connections which these ties bring with them. For example, churches often have relationships with other churches in different locations, which people may feel would provide support for them in an evacuation. Family and friends far away from them, and friends nearby, significantly influenced those who evacuated, compared to those who did not evacuate, to help them with that decision.

Previous research from Hurricane Matthew (Collins et al. 2017) showed the importance of dependability in the decision to evacuate or not, with those who did not evacuate having higher levels of functional support, indicating nonevacuees felt comfortable hunkering down with their neighbors and local friends. However, this result was not borne out from this study from data collected during and immediately after Hurricane Irma. This may possibly be a result of the size of the evacuation and the threat of Hurricane Irma. While some may have felt comfortable initially hunkering down, because of the widespread mandatory evacuations many people who might have relied on local friends and neighbors were seeing their support network leaving. Further evacuation studies need to focus on social connections in order to verify these results.

It is likely that emergency managers will want to integrate this new knowledge on the role of social support and connectedness into more targeted and focused disaster education campaigns. This is especially true considering that some evacuees and some nonevacuees are making decisions against the advice of emergency managers. For instance, some evacuees are evacuating from locations that are inland and outside of mandatory evacuation zones, whereas some nonevacuees are not evacuating from mandatory evacuation zones. This decision making could be due to a myriad of reasons outside of those surveyed. For example, those who decided to evacuate who were not under mandatory evacuation or located near the flood zones may have felt unsafe in their households due to structural integrity, especially in older housing before code enforcement was as stringent as current standards. Also, media influence could affect both those who should have evacuated in an evacuation zone and vice versa: those who did not evacuate may have seen it as overhyped while those who did evacuate inland may have expected worse due to media sensationalism.

This study also has implications for government leaders and policymakers. The adage “all disasters are local” acknowledges the strain on municipal and state resources as the frequency and magnitude of natural hazards like hurricanes continue to increase. Understanding how social connections might aid in reducing reliance on public assets to assist evacuees has potential for maximizing limited resources. Likewise, those assets might be redeployed for concentrations of nonevacuees who are marginalized through limited social supports and therefore must rely on government services. Furthermore, a better understanding of the role that social connections play can assist a community to gain greater resiliency as local disaster service organizations link people to preparedness and recovery services. Overall, studies that focus on the influence of real-time decision making on the role of social support and connectedness regarding evacuation decision-making will benefit the development of sustainable community-based disaster preparedness.

Some limitations to this study should be noted. First, while there was a team of 13 faculty and students who participated, more students were trained to collect the data. Participation of these additional students would have led to collection of more data. However, because of the projected path and intensity of Hurricane Irma, additional team members could not participate. Many students reported their parents wanted them to be home with them or leave the state. Second, since the research team was from the University of South Florida, the team needed to stop collecting data after their day in the field on 8 September to allow the team time to execute their own hurricane plans for themselves and their family since Tampa was projected to be in the path of the hurricane. Therefore, data on the late wave of evacuees were not collected. If a hurricane is not a threat to the researchers’ homes in the future, it would be interesting to collect data on this group to also compare early versus late evacuees. The largest limitation to this study is that convenience sampling was employed. It was not realistic to employ other types of sampling strategies and, as noted by Norris (2006), there tends to be prevalent use of convenience sampling in disaster studies. Therefore these results from this research provide an interesting case study of a hurricane evacuation given the sample.

Furthermore, this study was completed with real-time data and we are reporting the actual behavior—not predicted behavior. Predictive ability is important and future work will address that with a more comprehensive study including both social and physical factors that affect evacuation behavior. Further studies are needed to verify our results.

Acknowledgments

We would like to acknowledge the remainder of the USF 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. This research was funded in part by a grant from the University of Colorado Natural Hazards Center through its Quick Response Grant Program, which is funded by National Science Foundation Grant CMMI 1333610, as well as a grant from NSF RAPID with (Grant BCS-1760235). Finally, we appreciate the comments and suggestions from three anonymous reviewers, as well as the editor of the journal Weather, Climate, and Society.

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