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Heading for higher ground: Factors affecting real and hypothetical hurricane evacuation behavior
Abstract
The purpose of this paper is to assess the determinants of hurricane evacuation behavior of North Carolina coastal households during Hurricane Bonnie and a future hypothetical hurricane. We use the data from a telephone survey of North Carolina coastal residents. Hypothetical questions are used to assess whether respondents will evacuate and where in the case of a future hurricane with varying intensities. We examine the social, economic, and risk factors that affect the decisions to evacuate and whether to go to a shelter or motel/hotel relative to other destinations. The most important predictor of evacuation is storm intensity. Households are more likely to evacuate when given evacuation orders, when they perceive a flood risk, and when they live in mobile homes. Households who own pets are less likely to evacuate. Non-white households, pet owners and those with more education are less likely to go to either a motel/hotel or shelter, preferring instead to stay with friends or family.
... In addition to households' evacuation destinations, prior survey research examines the accommodations where people stay during an evacuation. Actual response surveys observe that approximately 54-70% of U.S. Gulf and Atlantic Coast evacuees stay at the homes of friends and relatives, 16-33% stay at a hotel/motel, 1-6% stay at public shelters, and 8-13% stay in other accommodations, such as second/vacation homes, peerto-peer rentals (e.g., Airbnb), churches, or recreational vehicles (RVs) (Bierling et al., 2020;Mitchell et al., 2017;Whitehead et al., 2000;Wu et al., 2012). Although few evacuees stay in public shelters or other accommodations, these evacuees often include vulnerable populations (Mesa-Arango et al., 2013;Wong et al., 2018). ...
... Comparatively, intended response surveys of U.S. Gulf and Atlantic coast residents observe that 51-60% of respondents plan to stay with family or friends, 24-33% intend to stay at hotel/motel, 5-12% intend to stay at public shelters, and 4-8% plan to stay in other accommodations (Mitchell et al., 2017;Morrow & Gladwin, 2006;Whitehead et al., 2000). Together, these studies suggest that accommodation intentions correspond with households' actual accommodation observed during hurricane evacuations (Kang et al., 2007). ...
... Prior studies examine relationships between households' destinations and accommodations, and various demographic and non-demographic factors. The former include factors such as age (Whitehead et al., 2000), disability/special needs (Ng et al., 2014), home ownership (Lindell et al., 2011), income (Yabe & Ukkusuri, 2020), pet ownership (Whitehead et al., 2000), and race (Mitchell et al., 2017), however, these factors often fail to predict evacuation destinations and distances (Wu et al., 2012). Other studies examine demographic factors related to accommodation decisions, and observe that low-income households with young children, elderly persons, or persons with disabilities/special needs are more likely to evacuate to homes of family or friends or stay at a public shelter than stay at a hotel (Wong et al., 2018). ...
... There are comprehensive review papers on evacuation planning in the literature [14,15]. Although there are also many studies in the literature that analyze the evacuation decisions and behaviors of the people in notice disasters [16][17][18][19][20][21], there is a significant gap in the literature for no-notice disasters [22][23][24]. In the 1970s, the first evacuation studies were carried out on hurricanes [25,26]. ...
... Many such examples can be given. Most of the time, these studies are conducted through surveys with RP [19,29,30], and stated preference (SP) datasets [22,23,[31][32][33]. ...
... Consequently, the trips of people who evacuated immediately after the earthquake to destination points should be questioned in detail, and their usage of the road network should be determined. Studies on disasters, both notice and no-notice, emphasized that the severity level of the disaster affects evacuation decision behaviors [19,22,24,36]. Similarly, in a study examining four large-scale earthquakes in Japan, Yabe et al. stated that the seismic intensity at which people start to evacuate is approximately 5.2, and the evacuation rate increases significantly between intensities of 5.5 and 6.0 [38]. ...
Following an earthquake, abnormal travel demand causes traffic congestion and poses significant problems for relief efforts. Research on post-earthquake travel demand is essential for disaster management. An effective disaster management strategy ensures achieving sustainable development goals. This study focused on this critical period and analyzed post-earthquake trip decisions. The city of Elazığ, a region not at risk of tsunami, was used as a case study. A 6.8 magnitude earthquake hit Elazığ in January 2020. After the earthquake, data from 2739 individuals were collected by a household survey conducted face-to-face. The data were segregated into two categories, depending on the earthquake’s intensity. The study used a binary logit model to examine the potential factors of trip decisions after an earthquake. The results showed that 75% of participants made at least one trip within 24 h after the earthquake. It was observed that household, building-and disaster-related attributes influence earthquake survivors’ trip decisions. The initial location at the time of the earthquake was the most significant factor affecting trip decisions. It was also found that individuals who experienced the earthquake outside their homes in both datasets were more likely to make a trip. Additionally, the dataset with higher earthquake intensity had more significant variables affecting the trip decision.
... Women are also more likely to evacuate than men, possibly due to their increased vulnerability due to social inequalities, their broader networks that may allow them to be more aware of warnings, and their perception of disasters as life-threatening events. (Bateman & Edwards, 2002;Riad et al., 1999;Smith & McCarty, 2009;Whitehead et al., 2001). However, not all studies have found significant differences in evacuation rates between men and women (Zhang et al., 2004). ...
... There is some evidence to suggest that higher income and education levels may lead to higher evacuation rates, as these resources can provide access to information and facilitate the development of effective evacuation plans. However, the evidence on this point is not conclusive, and many studies have found that income and education have little or no impact on evacuation rates (Bateman & Edwards, 2002;Whitehead et al., 2001). Previous research has indicated that individuals who do not evacuate during an emergency are more likely to cite emotional attachments to their home or environment and close connections to their neighbours as reasons for not evacuating (McLennan et al., 2013). ...
... Researchers use a range of questions or variables to measure flood risk perception, including perceived flood risk, awareness, cause, likelihood, affect (such as fear, dread, or concern), and impact. Flood risk perception itself is influenced by a variety of factors, including age, gender, income, education level, household size, the number of children and elderly in the household, years of residence, the duration and extent of the hazard, and knowledge about warnings and evacuation Bateman & Edwards, 2002;Botzen et al., 2009;Bradford et al., 2012;Bubeck et al., 2012;Dash & Gladwin, 2007;Ho et al., 2008;Lin et al., 2008;Ludy & Kondolf, 2012;Miceli et al., 2008;Rana et al., 2020;Smith & McCarty, 2009;Whitehead et al., 2001). Understanding these factors is crucial for improving risk perception and promoting effective flood preparedness and response (Kellens et al., 2013;Rana et al., 2020;Shah et al., 2022). ...
Evacuation is considered an essential aspect of flood risk reduction. It is important to identify the factors affecting the decision-making process during evacuation. The purpose of this study was to examine the factors that influence evacuation decision-making in flood-prone rural communities along the Indus River in Dera Ismail Khan. A total of 465 household surveys were conducted in high flood-risk areas along the river to gather data on evacuation characteristics and risk perception. Pearson's correlation technique was utilised to determine the relationship between flood risk perception indicators and the likelihood of evacuation, while a binary logistic regression test was implemented to identify the socioeconomic factors that influence evacuation. The results of the study indicated that socioeconomic conditions and risk perceptions can have a direct or indirect effect on evacuation decisions. It was found that those living in closer to the river tended to have a lower risk perception. However, respondents in the study reported experiencing high levels of fear in regard to floods. Age and proximity to hazards were identified as significant factors that impact willingness to evacuate. These findings suggest the need for urgent implementation of awareness campaigns in settlements located near the river in order to promote evacuation. ARTICLE HISTORY
... An event or threat that is judged to be real and has some unacceptable level of perceived risk may motivate those at risk to engage in information search (e.g., obtaining more information about the threat from others) [22,23] to gather more information about the characteristics of the hazard, which may eventually lead them to decide on protective actions (e.g., evacuation or sheltering in-place) [6,16]. Risk perception is a strong, consistent predictor of evacuation [6,13,14,[24][25][26]. In addition, studies reported that those who evacuate often cite not feeling safe or being vulnerable (which conceptually means perceiving higher risks) as the main reason why they evacuate, while those who do not evacuate mostly say they felt safe [6,27]. ...
... Stein et al. [29] measured risk perception as perceived surge risk, perceived flood risk, and perceived wind risk (all three risk factors were measured on a 3-point scale ranging from low = 1 to high = 3) for Hurricane Rita evacuees living in the Houston Metropolitan Area. Similarly, both Whitehead et al. [25] and Smith's [30] studies conducted for Hurricane Bonnie evacuees in North Carolina measured risk perception for wind and flooding. While Smith [30] measured wind risk and flood risk on a 3-point scale (low, medium, and high), Whitehead et al. [25] measured both wind risk and flood risk as binary variables (high or medium = 1; 0 for otherwise). ...
... Similarly, both Whitehead et al. [25] and Smith's [30] studies conducted for Hurricane Bonnie evacuees in North Carolina measured risk perception for wind and flooding. While Smith [30] measured wind risk and flood risk on a 3-point scale (low, medium, and high), Whitehead et al. [25] measured both wind risk and flood risk as binary variables (high or medium = 1; 0 for otherwise). Siebeneck and Cova [31] captured risk perception by asking evacuees of the 2008 Iowa flood at Cedar Rapids, Iowa, to rate how dangerous they perceived the floods to be (on a 5-point scale ranging from not at all = 1 to very great extent). ...
In hurricane evacuation studies, the extant literature has extensively explored the effect of risk perception on evacuate/stay decisions. However, less attention has been paid to how perceived certainty affects households' evacuate/stay decisions. The objectives of this paper are to explore the effects of (1) perceived certainty about location of impact on both risk perception and perceived certainty about evacuation logistics; (2) risk perception on perceived certainty about evacuation logistics; and (3) perceived certainty about evacuation logistics on evacuate/stay decisions. Survey data gathered from households in the Jacksonville, Florida metropolitan area after Hurricane Matthew (2016) were analyzed using structural equation modeling (SEM). In addition, SEM allowed us to identify the factors that could be used to predict risk perception, perceived certainty about location of impact, and perceived certainty about evacuation logistics. The results showed that perceived certainty about location of impact had a non-significant effect on risk perception. Perceived certainty about location of impact had a positive effect on perceived certainty about evacuation logistics. However, the effect of risk perception on perceived certainty about evacuation logistics was non-significant. Both risk perception and perceived certainty about evacuation logistics had positive effects on households' evacuation decision while perceived certainty about location of impact had a negative effect on households' likelihood of evacuating. Overall, the findings can be used to improve on the prediction of households’ evacuation behavior.
... Ben-Akiva and Lerman (1985) provide an overview of discrete choice modeling, and Wong et al. (2018) reviews research articles using discrete choice analysis for hurricane evacuations. Basic binary (two choice) and multinomial (multiple choice) logit models have been developed for the decision to evacuate or not (e.g., Whitehead et al. 2000;Zhang et al. 2004), destination choice (e.g., Cheng et al. 2011), shelter choice (e.g., Smith and McCarty 2009;Deka and Carnegie 2010), transportation mode choice (e.g., Deka and Carnegie 2010), route choice (e.g., Akbarzadeh and Wilmot 2015), and reentry compliance (e.g., Siebeneck et al. 2013). Recent advances in discrete choice modeling for transportation have also been applied in the evacuation field. ...
... Meanwhile, the evacuation keen class will be more likely to evacuate, even without any orders. Overall, the results verify long-standing and significant evidence from discrete choice models in the evacuation field that mandatory evacuation orders are effective for wildfires (e.g., McCaffrey et al. 2018;Lovreglio et al. 2019), hurricanes (e.g., Whitehead et al. 2000;Wilmot and Mei, 2004;Hasan et al. 2011Hasan et al. , 2012Huang et al. 2012;Yin et al. 2016;Wong et al. 2018) and other hazards (e.g., Murray-Tuite and Wolshon, 2013;Lindell et al. 2019). Content courtesy of Springer Nature, terms of use apply. ...
... Those living in a high fire risk zone (as denoted by the California Department of Forestry and Fire Protection [Cal Fire]) were more likely to be part of the evacuation keen class, along with females and young adults. The result adds to the consensus from hurricane evacuations (Riad et al. 1999;Whitehead et al. 2000;Smith and McCarty 2009) that females are more likely to evacuate. For the age variable, Toledo et al. (2018) found that young adults were more likely to evacuate but Lovreglio et al. (2019) found the opposite, which altogether indicates uncertainty of age in evacuation decision-making for wildfires. ...
For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior.
... East Tx (41%), Cent Tx (20%), North Tx (9%), West Tx (3%), Coast Tx (2%), Dallas/Fort Worth (5%), Own City (0.3%), Multi Destinations (1%), Other State (18%) Katrina & Rita (2005) Texas and Louisiana Yin et al. (2014b) Note: "↑" indicates increased likelihood effect; "↓" indicates decreased likelihood effect; a study found the corresponding factor to be significant; b study found the corresponding factor to be non-significant. With regard to modeling methodology, Bian et al. (2019) and Mesa-Arango used nested logit models; Smith and McCarty (2009) used binary logit models; Yin et al. (2014b) used both binary and multinomial logit models, Whitehead et al. (2000a) used multinomial logit models, both Lindell et al. (2011) and Wu et al. (2012) used correlation analyses. ...
... Prior studies reported that friend/family members' (peers') homes, commercial establishments (hotels/motels) and public shelters are the common accommodation types selected during hurricane evacuation. A peer's home is highly preferred, followed by hotels/motels and finally, public shelters (Bian et al., 2019;Yin et al., 2014b;Whitehead et al., 2000a;Whitehead, 2003;Mesa-Arango et al., 2013;Lindell et al., 2011, andUSACE, 2017) as shown in Table 1. Lindell et al. (2019) estimated the range of evacuees for each accommodation type as 54% to 70% with a median estimate of 62% for friends/relatives (peers), 16% to 32% with a median estimate of 27% for hotels/motels, and 2% to 6% with a median estimate of 3% for public shelters. ...
... However, Smith and McCarty's (2009) results showed a positive association between mobile home residents and public shelters, while the association between staying in a hotel and mobile home residents was negative. Damera et al. (2019) suggested that most mobile home residents tend to have low income and so they are more likely to stay in public shelters (Whitehead et al., 2000a). ...
This paper investigates how perceived certainty factors influenced households’ selection of destination and accommodation type during evacuation. Using survey responses from Jacksonville, FL, multinomial logit models were developed for both choices. For the first, greater understanding of hurricane-related graphics decreased households' probability of staying within their community. Households with a member who has special medical needs and those evacuating with a greater number of vehicles were more likely to stay in the eastern portion of their county. Greater perceived certainty about the hurricane impact location decreased households’ probability of evacuating to the south. For the accommodation model, married evacuees and those who received official evacuation notices had increased likelihood of staying in hotels/motels, while those who evacuated a day before landfall were less likely to do so. Greater perceived certainty about hurricane impact time and frequency of communication with social network members increased the probability of staying in a peer’s home.
... This study argues that local governments should increasingly prioritize building these soft policies and cultivating strong linking ties among residents and local officials to accelerate evacuation and protect society's most vulnerable from disasters' growing toll. scholarship has investigated why residents decide to stay or leave, starting in the 1990s in response to severe hurricanes (Perry and Lindell 1991;Riad et al. 1999;Whitehead et al. 2001) and catalyzed by Hurricane Katrina in 2005 (Boin and McConnell 2007) and Hurricane Sandy in New York in 2012 (Sadri et al. 2017;Gehlot et al. 2019). More recent evacuation studies apply increasingly sophisticated mixed methods toolkits in response to numerous disasters in the late 2010s (DeYoung et al. 2016;Lucero et al. 2020;Ahmed et al. 2020;Fraser et al. 2020), including big data approaches from mobile phones (Yabe et al. 2019), Twitter (Martin et al. 2017), and Facebook data (Fraser et al. 2020). ...
... Past disaster studies relied on ad hoc highway interviews, speaking with evacuees en route to their destinations, capturing individuals' experiences in high detail (Collins et al. 2018), albeit with challenges of maintaining representativeness. Others used post hoc surveys of residents weeks or months afterwards (Riad et al. 1999;Whitehead et al. 2001;Sadri et al. 2017;Gehlot et al. 2019), which allow for broader coverage but can involve recollection challenges for respondents and only usually better reflects the population after the disaster than before. Each method provides tradeoffs, and recent big data approaches offer a third method for evaluating evacuation's drivers, providing movement from complete populations of users (e.g., mobile phones, Twitter, or Facebook) but only at aggregate, anonymized levels, to ensure user privacy (Martin et al. 2017;Metaxa-Kakavouli et al. 2018;Yabe et al. 2019). ...
... Evacuation rates certainly also depend on access to road infrastructure (Na and Banerjee 2015), hurricanes' physical characteristics (Zhu et al. 2020), and individual characteristics (Perry and Lindell 1991;Riad et al. 1999;Whitehead et al. 2001;Metaxa-Kakavouli et al. 2018). However, past studies have found considerable variation in evacuation rates after evacuation orders (Martin et al. 2017), especially among communities of color (Riad et al. 1999;Whitehead et al. 2001;DeYoung et al. 2016;Lucero et al. 2020), or residents with less trust in government (Manuell and Cuckor 2011) and isolated social networks (Ahmed et al. 2020). ...
Why do citizens evacuate and where do they go once they have left disaster zones? Using Facebook data aggregated to the neighborhood level, this mixed methods study analyses the movement of Facebook users to and from cities struck by storms and floods. This study examines why evacuation varied among cities during Hurricane Dorian, a major hurricane which struck the US southeast in 2019. This study examines the intersecting roles of evacuation orders, policy tools, bonding, bridging, and linking social capital, and social vulnerability. The author combines mobility network analysis and geographic information systems with statistical matching models and geospatial case studies of affected communities. This study highlights how linking social capital and “soft” community-oriented preparedness policies boosted evacuation between cities, while bonding social capital was associated with less evacuation. By clarifying community-level factors in evacuation, this study aims to open a research agenda for analyzing the politics of human mobility during crises.
Graphical abstract
... However, the importance of people- 14 centered risk communication has been highlighted [5], especially individual, 15 community-based, and participatory approaches [6]. 16 Studies on participatory approaches show that evacuation drills can in- 17 crease evacuation rates [7] and that workshops increase flood knowledge, 18 especially for "unexposed-to-flood" populations [8]. However, few scholars 19 have highlighted the importance of longitudinal studies for examining par-20 ticipatory approaches [9]. ...
... Individual characteristics such as gender, age, 68 and location significantly influence the decision to evacuate [13,14,15]. Fur-69 thermore, evacuation behavior is influenced not only by the likelihood of 70 J o u r n a l P r e -p r o o f Journal Pre-proof a residence being flooded but also by the local characteristics of the area 71 such as roads, population, and whether an area is coastal or inland [16,17]. ...
People-centered risk communication is important to mitigate the flood damage caused by the recent increase in heavy rainfall events in Japan. Longitudinal studies are particularly important for evaluating the effectiveness of risk communication methods; however, current research is insufficient. To address this gap, we conducted a longitudinal study, specifically through four panel surveys conducted over a short period, to investigate the effects of various risk communication methods such as running an evacuation simulation to learn about flood damage, providing information about the evacuation behavior of others, and distributing hazard maps. The results of a fixed effects analysis of the panel data suggest that the impact of risk communication depends on the initial evacuation attitude. In particular, we find that distributing hazard maps had a negative effect on the evacuation behavior of those who initially responded that they would evacuate. This suggests that residents in non-flood-prone areas may have acquired the correct hazard perception from these hazard maps. However, for those who initially chose not to evacuate, receiving the distributed content had a positive effect on their evacuation behavior 12 hours before the typhoon hit. This suggests that those who initially chose not to evacuate may have reconsidered their decision. The findings of this study may help future risk communication by reducing congestion at evacuation sites due to excessive evacuation, while increasing the evacuation rate of those who should evacuate.
... In the existing literature of both qualitative studies and quantitative studies in investigating evacuation responses, many studies mainly focused on the evacuation choices of community-dwelling households from the general population [8][9][10][11]. Baker [8] studied a number of hurricanes in the Atlantic states from Texas through Massachusetts occurring between 1961 and 1989, where sample surveys from the general population were used to identify characteristics, such as hazardousness of the region, public service, residence type, perceived risk, and general storm severity, influencing aggregate-level evacuation rates. Wolshon et al. [9] combined results from a survey on state evacuation plans performed by Louisiana State University and other published studies at the time. ...
... The work summarized evacuation policies and procedures implemented by state authorities, focusing on the transportation service utilization and response perspective for the general population. Whitehead et al. [10] examined prospective hurricane evacuation behavior of North Carolina coastal residents following occurrence of Hurricane Bonnie through telephone surveys, and concluded that storm severity, reception of evacuation order, possibility of flooding, housing structure, and socio-demographic disparity to important determinants of evacuation. Hasan et al. [11] considered post-storm damage assessment data of households affected by Hurricane Ivan and characterized various factors affecting evacuation behavior. ...
Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents’ health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.
... The data may describe behavior in past hurricanes (revealed preference) or intended behavior in hypothetical future events (stated preference). These are typically standard binomial logit or probit regression models (Basolo et al. 2017, Bateman and Edwards 2002, Cahyanto et al. 2014, Horney et al., 2011, Huang et al. 2012, Karaye et al. 2019, Kyne and Donner 2018, Lazo et al. 2015, Mei 2002, Meyer et al. 2018, Mozumder and Vásquez, 2018, Sadri et al. 2017, Solís et al. 2010, Stein et al. 2010, Whitehead 2005, Whitehead et al. 2000, Wong-Parodi and Feygina 2018, Xu et al. 2016, and Yang et al. 2016, but include other types as well, such as neural networks (Wilmot and Mei 2004) and a latent class choice model (Wong et al. 2020). Hasan et al. (2010) and Yin et al. (2016) apply random parameter logit models. ...
... Some previous models have included one or more of these variables, but as a static quantity. Whitehead et al. (2000) and Xu et al. (2016), for example, include hurricane category as a predictor, but do not specify the time at which it is measured, leaving its interpretation ambiguous. The structure of the time-dependent evacuation demand models (Section 2.2) can accommodate time-varying predictors, and thus those models do tend to include hurricane characteristics that vary with time. ...
This study advances prediction of population evacuation behavior during hurricanes by comprehensively comparing five different models based on their practical utility for future hurricanes. The models—participation rate (PR-S), logistic regression (LR-S), random parameter logit (RPL), time-dependent Cox (TD-Cox), and dynamic discrete choice (DDC)—were fitted using population survey and hurricane data collected in a consistent format across four different hurricanes (Florence 2018, Michael 2018, Dorian 2019, and Barry 2019). Out-of-sample predictive power was evaluated in terms of prediction of total evacuation rates, spatial distribution of evacuees, evacuation timing, and individual behavior. The final set of predictors can be obtained for a whole region and applied in the future for prediction. The results suggest that if only an estimate of the total evacuation rate for the whole region is required, the LR-S is easiest to implement and provides good predictive power. However, if spatial and/or timing predictions are required, the DDC is recommended. The results suggest that in general, for future hurricanes, the best models currently available can estimate total evacuation rate within one to nine percentage points; evacuation rate for each county within 10 to 15 percentage points; and departure curve within several hours. Results also indicate that errors become smaller as geographic granularity increases.
... For this research, we consider five different choices for shelter: 1) a friend's residence, 2) a family member's residence, 3) a hotel/motel, 4) a public shelter, and 5) an "other" location such as an Airbnb or a second residence. Other research in shelter choice from Whitehead et al. (2000) also built a multinomial logit model, while Smith and McCarty (2009) Only 3.5% of the sample stayed at a public shelter, but 4.3% used a peer-to-peer service such as Airbnb. The rise of Airbnb as a sheltering option could help alleviate demand on public shelters. ...
... Often, hotels/motels and public shelters do not allow pets, which decreases the likelihood that individuals would choose these sheltering options. Lower-income households (under $40,000) were less likely to shelter at a hotel/motel but much more likely to stay at a public shelter, confirming results from Whitehead et al. (2000) and Mesa-Arango et al. (2012). This unsurprising result reflects the high cost of hotels/motels compared to the free option of a public shelter. ...
In September 2017, Hurricane Irma prompted one of the largest evacuations in U.S. history of over six million people. This mass movement of people, particularly in Florida, required considerable amounts of public resources and infrastructure to ensure the safety of all evacuees in both transportation and sheltering. Given the extent of the disaster and the evacuation, Hurricane Irma is an opportunity to add to the growing knowledge of evacuee behavior and the factors that influence a number of complex choices that individuals make before, during, and after a disaster. At the same time, emergency management agencies in Florida stand to gain considerable insight into their response strategies through a consolidation of effective practices and lessons learned. To explore these opportunities, we distributed an online survey (n = 645) across Florida with the help of local agencies through social media platforms, websites, and alert services. Areas impacted by Hurricane Irma were targeted for survey distribution. The survey also makes notable contributions by including questions related to reentry, a highly under-studied aspect of evacuations. To determine both evacuee and non-evacuee behavior, we analyze the survey data using descriptive statistics and discrete choice models. We conduct this analysis across a variety of critical evacuation choices including decisions related to evacuating or staying, departure timing, destination, evacuation shelter, transportation mode, route, and reentry timing.
... In the past few decades, discrete choice models have been widely used to understand evacuee choice-making. Most recent hurricane studies have concentrated on one dimension of behavior, in particular whether to evacuate, through traditional binary logit models (Whitehead et al., 2000;Zhang et al., 2004;Smith and McCarty, 2009;Stein et al., 2010;Hasan et al., 2012;Huang et al., 2012;Murray-Tuite et al., 2012;Murray-Tuite and Wolshon, 2013;Wong et al., 2018b) and mixed logit models (Deka and Carnegie, 2010;Solís et al., 2010;Hasan et al., 2011;Xu et al., 2016;Yin et al., 2016). A number of other choices in evacuations have been assessed in isolation, including: mode choice (Deka and Carnegie, 2010;Sadri et al., 2014a); shelter and accommodation type (Whitehead et al., 2000;Mesa-Arango et al., 2013); route choice (Sadri et al., 2014b;Sadri et al., 2015); and reentry (Siebeneck et al., 2013). ...
... Most recent hurricane studies have concentrated on one dimension of behavior, in particular whether to evacuate, through traditional binary logit models (Whitehead et al., 2000;Zhang et al., 2004;Smith and McCarty, 2009;Stein et al., 2010;Hasan et al., 2012;Huang et al., 2012;Murray-Tuite et al., 2012;Murray-Tuite and Wolshon, 2013;Wong et al., 2018b) and mixed logit models (Deka and Carnegie, 2010;Solís et al., 2010;Hasan et al., 2011;Xu et al., 2016;Yin et al., 2016). A number of other choices in evacuations have been assessed in isolation, including: mode choice (Deka and Carnegie, 2010;Sadri et al., 2014a); shelter and accommodation type (Whitehead et al., 2000;Mesa-Arango et al., 2013); route choice (Sadri et al., 2014b;Sadri et al., 2015); and reentry (Siebeneck et al., 2013). A review of this hurricane literature using discrete choice models can be found in Wong et al., (2018b). ...
Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with growing need for evacuation resources. To understand influencers on sharing willingness in evacuations, we employed a multi-modeling approach using three model types: 1) four binary logit models that capture sharing scenario separately; 2) a portfolio choice model (PCM) that estimates dimensional dependency, and 3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from 2017 Hurricane Irma evacuees (n=368). The multi-model approach uncovered behavioural nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classes – transportation sharers, adverse sharers, and interested sharers – with different memberships. We suggest that local agencies consider holistic sharing mechanisms across resource types and time (i.e., before, during, and after evacuations).
... Various methods have been developed to simulate these two critical phases. In the initial stage of evacuation, social and environmental factors significantly influence evacuation decisions (Whitehead et al., 2000). For instance, Lovreglio et al. (2015) and Zhao et al. (2020) examined how physical factors (e.g., alarm systems, building space) and social factors (e.g., group location and size) affect evacuees' evacuation status. ...
As disasters, both natural and human-induced, grow more frequent, effective sheltering and evacuation become crucial. While existing studies considered approaches optimizing shelter location by simulating individuals’ evacuation behaviors, most rely on pre-determined functions and overlook the heterogeneity of individuals’ behaviors. To address this limitation and account for the complexities of residents’ evacuation processes, we develop an approach that integrates Agent Based Modeling (ABM) with a Genetic Algorithm (GA), using the evacuation simulation results from ABM to optimize community shelter locations. We validate the proposed method by simulating residents’ evacuation in an assumed nighttime earthquake event within a student residential community in Shenzhen. This study provides a tool assisting in the optimization of community shelter locations in pre-disaster planning.
... The odds ratio of 0.244 indicates that females are approximately 76 percent less likely to evacuate than males (Table 5). This finding contradicts some previous studies (Whitehead et al. 2001; Bateman and Edwards Kyne et al. (2020) and Wong et al. (2020), which indicate that women might exhibit lower levels of preparedness and be more reluctant to evacuate than men. ...
Minority populations frequently face heightened vulnerabilities compared to other racial groups during disasters. This research investigates how the responses to disaster issues of minority populations vary across rural and urban areas. The study examines differences in disaster evacuation vulnerabilities among minority populations by analyzing their evacuation status and choices of evacuation destinations. The 2023 survey employed a two-target groups approach, sampling minorities of all ages in both urban (eighty people) and rural areas (eighty people), and collected a total of 160 responses from northwest Florida. Our study could be influenced by various disasters, but we assume that people respond based on their perceptions of hurricanes. The survey was conducted using Pollfish, an online tool specializing in mobile application surveys, based on diverse eligibility criteria. Twelve logistic regression models were developed, examining evacuation status and future evacuation destination as dependent variables, with independent variables spanning socioeconomic, health, and past consequences dimensions. The results show that 61.3 percent of urban minorities and 81.3 percent of rural minorities evacuated at least once in past disasters. The study identifies statistically significant variables, highlighting evacuation behavior differences between urban and rural minorities, with implications for emergency management to address marginalized populations’ evacuation needs more effectively.
... These models include the protective action decision model (PADM) and the warning response process (Lindell and Perry 2012;Mileti and Sorensen 1990). Key predictor variables that have emerged in decades of evacuation research include socio-demographics Huang et al. 2016;Thiede and Brown 2013;Elder et al. 2007)-including having children (Tierney et al. 2001) or pets (Whitehead et al. 2000), hazard characteristics (Hasan et al. 2013;Xu et al. 2016), transportation and infrastructure variables (Murray-Tuite et al. 2013, Wolshon 2009, Yazici et al. 2008, risk communication (Cahyanto et al. 2016;Dash and Gladwin 2007;Huang et al. 2016), social networks (Collins et al. 2018), exposure to warning messages (Sorensen and Sorensen 2007), and housing type (Kusenbach and Christmann 2013). ...
This study aims to help understand and predict evacuation behavior by examining the relationship between evacuation decisions and visits to certain businesses using smartphone location and point of interest (POI) data collected across three hurricanes—Dorian (2019), Ida (2021), and Ian (2022)—for residents in voluntary and mandatory evacuation zones. Results from these data suggest residents visit POIs as part of preparatory activities before a hurricane impacts land. Statistical tests suggest that POI visits can be used as precursor signals for predicting evacuations in real time. Specifically, people are more likely to evacuate if they visit a gas station and are more likely to stay if they visit a grocery store, hardware store, pet store, or a pharmacy prior to landfall. Additionally, they are even less likely to leave if they visit multiple places of interest. These results provide a foundation for using smartphone location data in real time to improve predictions of behavior as a hurricane approaches.
... Additionally, flood intensity and permanent/frequent flooding conditions significantly influence travel decisions, including changes to destination and mode or route. Residents consider a cyclone's location and strength when deciding whether to evacuate: intense cyclones with landfall in the vicinity are more likely to trigger an evacuation (28). The context of tourist evacuation reinforces the impact of increasing cyclone intensity on escalating risk perception (18). ...
Investigating the arrival of tourists subject to the forecast of an impending cyclone can help to understand the possible evacuation demand. This study focused on tourists’ decisions to either cancel, reduce stay duration, or continue entire stay duration of their upcoming trip after receiving information about an impending cyclone expected to affect their destination. The objective is to decipher the arrival of tourists and the potential evacuation demand. A stated preference survey, designed using the scenario variables 1) cyclone intensity, 2) spendable stay duration, and 3) days left to initiate the outward journey from home was conducted at Puri, the cyclone-susceptible tourist location in Odisha, India. Multinomial logit and nested logit models are developed. The tourists’ propensity to continue entire stay duration is significantly influenced by lower average age, male dominance, low income, and education of the group head. Tourists’ decisions are sensitive to the intensity of the impending cyclones and the sunk costs involved in the travel plan. Their inclination to reduce stay duration is observed to be predominant when they can spend at least two-thirds of their planned stay duration. Assessment of various possible evacuation demand levels and other evacuation pre-planning inputs are derived by investigating the impact of influential variables.
... Studies also investigate the relationship between evacuees' actual and intended accommodations and demographic and evacuation factors. Some studies observe that low-income households with young children, elderly persons, or persons with disabilities/special needs are more likely to evacuate to homes of family or friends or stay at a public shelter than stay at a hotel (Whitehead et al., 2000;Wong et al., 2018). Studies also observe that whites, large families, and high-income households are more likely to stay in hotels and less likely to stay in shelters (Lindell et al., 2011;Wu et al., 2012). ...
This study reports findings from a survey administered in 2023 to investigate New Orleans households’ social support networks: family and friends who can provide them with accommodations and other assistance during evacuations caused by hurricanes and other hazards. Findings describe the demographics and evacuation destination and accommodation intentions of households with and without social support, as well as the factors these households consider when choosing an evacuation destination. Preliminary findings show that 86% of respondents report at least one relative or friend with whom they can stay during an evacuation. However, the range and size of respondents’ social support networks vary and influences where they will go and stay during an evacuation. In contrast, 14% of respondents lack social support and these respondents are most likely to stay at a hotel or public shelter during an evacuation. Implications for the design of evacuation management systems are discussed, including systems that support the discovery of vulnerable households and coordination with community organizations that provide transportation and temporary housing during evacuations.
... In the coastal plain, historic population growth and groundwater over-extraction has driven state restrictions on pumping (US EPA 2010; US Census Bureau 2012); despite improved conditions, potential for freshwater shortages and saltwater intrusion continues (Ferguson and Gleeson 2012). Lastly, natural disasters have driven demographic shifts, as the state's coastal region faces an increase in the frequency, intensity, and duration of hurricanes (Poulter and Halpin 2008;Whitehead et al. 2000); rapid population loss in coastal communities following major hurricanes has left some cities worse off for servicing debt (Ross 2018). ...
In a world of increasing pressures from climate change, water utilities need to maintain—or even improve—their ability to continue provision safe and secure water supply. To ensure capacity in service delivery, some providers have embraced different forms of interlocal collaboration. Yet, such interdependence engenders risk, thus driving some collaborating providers to enter into contractual agreements. While these agreements can reduce risk, but new complexities may still arise, especially when the agreement is capital intense and physically constrained. This study asks: i) How does perceived risk of from external climate-related pressures to public service provision affect preferences for the future of current contractual agreements? and ii) how do local efforts to offset need for collaboration shape these future preferences? This study examines how beliefs and local strategies (i.e., technical, managerial, or programmatic advances) affect contract preferences among community water systems linked through interlocal agreements. The paper discusses insights about ways ontological beliefs may shape operational decisions specific to interlocal collaboration and the potential for consolidation of water service operations.
... Evacuation can be defined as withdrawal from a specific area based on current or anticipated threat. There are characteristics that differentiate evacuation behaviour between populations at risk, such as social structure, attitudes towards authority, experience, and perceptions of risk [27][28][29][30][31][32]. Whilst many factors influence people's decision to evacuate, their capacity and motivation to do so is limited by their vulnerabilities [33]. ...
Storm surge poses a significant threat to property, livelihoods, and life for coastal populations. The Philippines is expected to see the highest rise in storm surge exposure of any country over the next century under climate change, requiring robust knowledge of how current risk and evacuation intentions for households intersect. Drawing on a survey of 12,150 households in the Municipality of Carigara, we compare building-level storm surge risk between the primary place of residence and intended evacuation locations. Our results compare how risk differs for households that plan to evacuate to public centres, residences of family and friends, and those who intend to shelter-in-place. We found that 28% of households planned to evacuate to private residences, 12% planned to evacuate to public evacuation shelters, 48% planned to shelter-in-place, and 11% who recognised a need to evacuate but were unsure where they would go. While we find a positive association between storm surge risk and plans to evacuation, under the highest storm surge advisory (SSA4), we find that 30% of evacuations to private residences and 61% of evacuations to public centres are to locations at higher risk than an evacuee's primary place of residence. Our study problematises the assumption that evacuation will lead to higher safety, contributing new knowledge of intended evacuation plans under storm surge advisories in the Philippines and highlighting a need for continued public awareness of not only hazard zones, but also the vulnerability of evacuation structures.
... Despite the major role of rain-induced flooding in deaths and damages related to tropical cyclones (TCs) (Rappaport, 2014;Czajkowski and Kennedy, 2010;Czajkowski et al., 2013) and predictions that TC rainfall rates and associated flooding impacts will increase due to climate change (Knutson et al., 2010;Wang et al., 2015;Wright et al., 2015), the main determining factor in evacuation decision-making remains the intensity of the TC (Stein et al., 2010;Whitehead et al., 2000;Senkbeil et al., 2019). The potential for heavy rainfall is present regardless of the storm's category (Titley et al., 2021) so it is vital to increase public awareness of the dangers of river flooding in TCs. ...
Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS) to explore the connections between the skill of the TC track, intensity, precipitation, and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyze the connections between each forecast component. Strong relationships are found between track, precipitation, and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eyewall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases to investigate how this cascade of predictability varies with different forecast and geographical contexts in order to help inform flood early warning in TCs.
Significance Statement
This study demonstrates a methodology to analyze the cascade of predictability and uncertainty through the various stages of the tropical cyclone (TC) flood forecasting chain, illustrating how it can provide useful information to modelers interested in optimizing flood forecast skill, and to those who prepare and communicate flood forecasts with stakeholders and end-users in TC cases. The results highlight the importance of improving verification of ensemble TC precipitation forecasts, and of focusing on more than just the category of the storm and landfall location when forecasting and communicating flood impacts in TC cases.
... Among the different types of CRP measures, studies using correlation and regression analyses found perceived personal casualties or injuries to be strongly associated with evacuation decisions (Fu et al., 2007;Sharma & Patt, 2012). Hurricane wind, storm surge, and flood impact variables are also highly associated with evacuation decisions (Dow & Cutter, 2000;Huang et al., 2012;Morrow & Gladwin, 2005;Van et al., 2002;Whitehead et al., 2000). Perceived job and service disruption are also reported as significant predictors in several hurricane evacuations studies using regression analysis, but depending on the studies, they can have either negative or positive impacts (Dow & Cutter, 2000;Morrow & Gladwin, 2005;Smith & Mccarty, 2009). ...
This study investigates how different risk predictors influenced households’ evacuation decisions during a dual‐threat event (Hurricane Laura and COVID‐19 pandemic). The Protective Action Decision Model (PADM) literature indicates that perceived threat variables are the most influential variables that drive evacuation decisions. This study applies the PADM to investigate a dual‐threat disaster that has conflicting protective action recommendations. Given the novelty, scale, span, impact, and messaging around COVID‐19, it is crucial to see how hurricanes along the Gulf Coast—a hazard addressed seasonally by residents with mostly consistent protective action messaging—produce different reactions in residents in this pandemic context. Household survey data were collected during early 2021 using a disproportionate stratified sampling procedure to include households located in mandatory and voluntary evacuation areas across the coastal counties in Texas and parishes in Louisiana that were affected by Hurricane Laura. Structural equation modeling was used to identify the relationships between perceived threats and evacuation decisions. The findings suggest affective risk perceptions strongly affected cognitive risk perceptions (CRPs). Notably, hurricane and COVID‐19 CRPs are significant predictors of hurricane evacuation decisions in different ways. Hurricane CRPs encourage evacuation, but COVID‐19 CRPs hinder evacuation decisions.
... In contrast, households with lower levels of damage (0-25% damage) were found to be those less likely to evacuate. The expectation of damage or damage suffered in the past has been consistently reported as a good predictor of evacuation [11,[33][34][35]. When individuals feel they or their relatives are at risk of death or injury, or that their house could face serious damage, they are more likely to evacuate. ...
Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models.
... Based on prior research, households choose whether they will comply based on a number of factors. Among others, these factors include receiving warning messages, message content, warning confirmation, warning-source credibility [31][32][33], perceived risk, and the possession of a plan [34,35]. When warned, households interpret the warning message, try to understand it, attempt to confirm its accuracy with other sources, assess the degree to which that message is relevant to them, and consider if actions are feasible prior to making a decision [11]. ...
Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households’ responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes (“evacuation reluctant” and “evacuation keen”) by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. “Evacuation reluctant” individuals with a longer housing tenure are more likely to follow phased evacuation orders. “Evacuation keen” individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees’ compliance behavior to phased evacuation orders.
... The survey was conducted during the hurricane season, when there was heightened awareness of hurricane risk, though the questions posed did not reference a named storm. The use of intention to evacuate is consistent with studies of prospective or hypothetical behavior, and research findings are consistent between studies of intended and actual behavior (Huang et al., 2016;Kang et al., 2007;Whitehead et al., 2000). ...
This study examines households' prospective evacuation behavior during a hurricane‐pandemic compound threat. Data from a 2020 survey of coastal Virginia households help answer two questions: (1) What factors associated with the threat and impacts of the COVID‐19 pandemic and hurricanes influence the prospective evacuation behavior of households during a compound hurricane‐pandemic event? (2) What are the equity implications for emergency management policies and practices to support evacuation and sheltering during a compound hurricane‐pandemic event? Households in the sample were split between those who stated they would evacuate away from the at‐risk region and those who would stay. Greater household vulnerability to hurricanes and COVID‐19 and having sufficient financial resources increase the likelihood of evacuation. Higher‐income households were more likely to have resources to evacuate and were less likely to suffer financial consequences from a hurricane or pandemic. Racial minorities are more vulnerable to the pandemic and face greater resource challenges when evacuating.
... (Crisci and Kassinove, 1973) Whitehead indicated that the term "voluntary evacuation" does not convey an appropriate level of risk to people, resulting in low evacuation rates. (Whitehead et al., 2000) There is a reasonable relationship between forecasting behavior and actual behavior during natural disasters. Lamb and Walton demonstrated that the actual evacuation behavior after the Gisborne earthquake in New Zealand in 2007 had a similar trend to the behavioral expectations of participants after a simulated earthquake in Wellington, New Zealand. ...
A rainstorm disaster occurred on July 20, 2021 in the Henan Province, China, posing a severe threat to residents' lives and property. During this period, the victims engaged in shadow evacuation behaviors-that is, people who had not received official evacuation instructions or were not in the designated evacuation areas chose to spontaneously evacuate. Social capital refers to resources acquired or mobilized through social connections and relationships; it is a significant factor during all stages of natural disasters. We investigated the relationship between social capital and shadow evacuation. A total of 290 questionnaires were obtained from Fengquan and Weihui in Xinxiang City, Henan Province, China. The collected data were analyzed using descriptive statistics, confirmatory factor analysis, and binary logistic regression analysis using SPSS. The results showed that bonding social capital (p = 0.037 < 0.05, B = 0.347), bridging social capital (p = 0.003 < 0.01, B = 0.520), and linking social capital (p = 0.014 < 0.05, B = 0.390) had significant positive impacts on shadow evacuation behavior. The control variables of living with the disabled or elderly (p = 0.003 < 0.01, B = 0.989) and disaster experience (p = 0.000 < 0.01, B = 1.250) also had positive impacts on shadow evacuation behavior; the demographic variables had a limited impact.
... In some of the earliest research, Heath and colleagues warned that "pet ownership can be a significant threat to public and animal safety during disasters" [11] (p. 664). If a pet-friendly hotel, emergency shelter, or other accommodation cannot be secured in advance, attempting to find one during a disaster could delay evacuation [12]. In the absence of pet-friendly accommodations, owners may choose to ignore evacuation orders. ...
Simple Summary
The Marshall Fire, a grass-fire-turned-urban-firestorm, destroyed over 1000 homes in southeastern Boulder County, Colorado, within six hours on 30 December 2021. The fire occurred on a weekday, when many residents were at work, and during the holidays, when many were traveling. When the fire began spreading rapidly in populated areas, roadblocks and dense smoke prevented people from returning home to rescue their pets. The fire displaced 30,000 residents. Although a precise count of animal deaths is not possible, it is likely that over 1000 pets died. Through interviews with pet owners whose animals died, this research examined what prevented them from rescuing their pets and what might reduce future mass animal fatalities. This research also assessed the fire’s impact on veterinary clinics located within the burn zone. The study challenges claims that attribute the failure to evacuate pets to weak human–animal bonds and adds to the literature on rapid-onset disasters.
Abstract
Although much of the literature on pets in disasters associates the failure to evacuate pets with a weak or absent human–animal bond, rapid-onset disasters challenge the foundations of that claim. Colorado’s Marshall Fire, which occurred on 30 December 2021, took the lives of more than 1000 pets. The fire began in open grassland and quickly became an “urban firestorm” when it spread into densely populated areas. Due to the timing of the fire’s onset, owners could not return home to rescue their pets. Although first responders, volunteers, and other evacuees rescued some animals, many died inside their homes. Analysis of qualitative interviews with a small sample of pet owners whose animals died in the fire reveal the factors that prevented owners from rescuing their pets. Through analysis of traditional and social media, and emergency notifications, this research presents a timeline of events on the day of the fire and examines pitfalls in evacuation notification. Participant observation and field conversations provide insight into the impact of the fire on veterinary clinics. The study concludes with suggestions intended to reduce future mass deaths of animals.
... Educated respondents were found also to be more likely to evacuate. Consequently, owing to the fact that most of them are more likely to stay with friends or families in non flooded areas [10], this would somehow benefit in such a way that this will decongest evacuation shelters. This could mean that disaster managers can focus more on serving the needs of the low income group that usually go to these shelters. ...
To identify factors that influence the decision to evacuate upon flood warning by authorities, a study was conducted in a flood prone area in the province of Bukidnon in the Philippines. A survey of flood victims was conducted in Batangan Village, Valencia City, Bukidnon, Philippines wherein 150 respondents were interviewed. Logistic regression analysis was done to test the socio demographic factors that could influence a family’s decision to either evacuate or stay upon advice by government authorities. College education, presence of children in the home, poverty, and extent of flood experienced were found to significantly influence the decision of the family to evacuate. Based on this information, the study provides recommendations for disaster managers in case of future flood incidence in the area.
... At the same time, individual evacuation may be constrained by a host of factors ranging from access to transportation, monetary resources, health impairment, job responsibilities, gender, and the reluctance to leave home. There is a consistent body of literature on hurricane evacuations in the United States, for example, that finds: 1) individuals tend to evacuate as family units, but they often use more than one private vehicle to do so; 2) social influences (neighbors, family, friends) are key to individual and households evacuation decisionmaking; if neighbors are leaving then the individual is more inclined to evacuate and vice versa; 3) risk perception, especially the personalization of risk by individuals, is a more significant factor in prompting evacuation than prior adverse experience with hurricanes; 4) pets and concerns about property safety reduce household willingness to evacuate; and 5) social and demographic factors (age, presence of children, elderly, or pets in households, gender, income, disability, and race or ethnicity) either constrain or motivate evacuation depending on the particular context (Perry and Lindell, 1991;Dow and Cutter, 1998Whitehead et al., 2000;Bateman and Edwards, 2002;Van Willigen et al., 2002;Sorensen et al., 2004;Lindell et al., 2005;Dash and Gladwin, 2007;McGuire et al., 2007;Sorensen and Sorensen, 2007;Edmonds and Cutter, 2008;Adeola, 2009). Culture also plays an important role in evacuation decisionmaking (Clot and Carter, 2009). ...
This Intergovernmental Panel on Climate Change Special Report (IPCC-SREX) explores the challenge of understanding and managing the risks of climate extremes to advance climate change adaptation. Extreme weather and climate events, interacting with exposed and vulnerable human and natural systems, can lead to disasters. Changes in the frequency and severity of the physical events affect disaster risk, but so do the spatially diverse and temporally dynamic patterns of exposure and vulnerability. Some types of extreme weather and climate events have increased in frequency or magnitude, but populations and assets at risk have also increased, with consequences for disaster risk. Opportunities for managing risks of weather- and climate-related disasters exist or can be developed at any scale, local to international. Prepared following strict IPCC procedures, SREX is an invaluable assessment for anyone interested in climate extremes, environmental disasters and adaptation to climate change, including policymakers, the private sector and academic researchers.
... Prior experience shows a positive impact consistent with previous studies (Dash & Gladwin, 2007;Yang & Zhuang, 2020). Higher respondent age consistently predicts higher information perception (as in (Whitehead et al., 2000)), but lower situation certainty. Interestingly, having more children (minors) in the household predicts lower information perception of the respondent while having more seniors predicts lower situation certainty. ...
Conventional evacuation studies typically do not gauge the development of participants’ certainty about evacuation-related decisions with the updates in the information provided to them. This study uses an online survey that provides three kinds of progressively varied information about the current status of a hypothetical hurricane for five days leading to its landfall and collects respondents’ certainty of their situational comprehension and evacuation-related decisions each day. Most participants (84%) made a final decision (60% evacuate) after seeing information of just one day (four days before the landfall), indicating a tendency of swift decision-making. Modeling shows that the time spent looking at information, especially uncertainty cone forecast maps, positively influences the understanding of the hurricane’s status, which in turn helps in increasing the certainty of making evacuation-related decisions, with an increasing temporal effect. This study contributes to the understanding of the public perception of information and its association with evacuation-related decision-making.
... These risk-related factors influence evacuation decisions as well as the choice of destinations that can be a shelter, friends/relatives house, or motel/hotel (Whitehead et al., 2000). ...
With natural hazard events increasing globally, it is important to establish an effective evacuation procedure to mitigate their impacts. This paper investigates factors contributing to individuals’ evacuation decision-making under an imminent threat of volcanic eruption based on the data collected from a stated preference survey conducted in Auckland, New Zealand. Several factors are analysed using a logistic regression approach, including socio-demographic factors and factors related to risk, awareness, preparedness, evacuation warning and order, evacuation route choice, evacuation mode choice and evacuation destination choice. The results revealed that some of these factors are influential for individuals’ evacuation decision-making, including ethnicity, choice of destination, mode of transport, length of residency, risk awareness, annual household income and household with children. These findings will be useful for planners and policymakers in managing risks and planning to improve the safety of the vulnerable community by identifying appropriate evacuation strategies and reducing risk-increasing evacuation behaviour.
... Earlier work shows that risk is not perceived in the same way for all decisionmakers and may be influenced by social dimensions and the evacuation context (Dash and Gladwin, 2007). Perceived risk for flooding has been shown to be particularly important in a flooding context (Whitehead et al., 2000). Emotion has been shown to influence evacuation decision-making by impacting risk perception and message interpretation (DeYoung et al., 2019;Slovic and Peters, 2006 pandemic concern reduces by the equivalent amount of $85.02. ...
The decisions of whether and how to evacuate during a climate disaster are influenced by a wide range of factors, including sociodemographics, emergency messaging, and social influence. Further complexity is introduced when multiple hazards occur simultaneously, such as a flood evacuation taking place amid a viral pandemic that requires physical distancing. Such multi-hazard events can necessitate a nuanced navigation of competing decision-making strategies wherein a desire to follow peers is weighed against contagion risks. To better understand these nuances, we distributed an online survey during a pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. In this paper, we estimate a random parameter logit model in both preference space and willingness-to-pay space. Our results show that the directionality and magnitude of the influence of peers' choices of whether and how to evacuate vary widely across respondents. Overall, the decision of whether to evacuate is positively impacted by peer behavior, while the decision of how to evacuate is negatively impacted by peers. Furthermore, an increase in flood threat level lessens the magnitude of these impacts. These findings have important implications for the design of tailored emergency messaging strategies. Specifically, emphasizing or deemphasizing the severity of each threat in a multi-hazard scenario may assist in: (1) encouraging a reprioritization of competing risk perceptions and (2) magnifying or neutralizing the impacts of social influence, thereby (3) nudging evacuation decision-making toward a desired outcome.
... Bateman and Edwards (24) indicated that women were more likely to evacuate because of their higher risk perceptions compared with men. Whitehead et al. (25) indicated that the strongest predictor of evacuation was storm intensity, followed by evacuation orders, perceived flood risk, and living in mobile homes. Peacock et al. (26) found that having flood insurance and strong home structures were negatively associated with evacuation because households perceived lower risk of storm damage. ...
Hurricane evacuation has become an increasingly complicated activity in the U.S. as it involves moving many people who live along the Atlantic coast and Gulf coast within a very limited time. A good deal of research has been conducted on hurricane evacuation, but only a limited number of studies have looked into the timing aspect of evacuation. This paper intends to contribute to the literature on evacuation timing decisions by investigating what factors influence the time preference at the household level. Two hurricane survey data sets were used to analyze household evacuation behaviors across the Gulf coast as well as the Northeast and Mid-Atlantic coast in a comparative perspective. Using the Heckman selection model, we examined various factors identified in the literature on the two possible outcomes (evacuation and early evacuation). We found that the most important determinants of evacuation were prior evacuation experience, evacuation orders, and risk perceptions, while the most important determinants of early evacuation were prior evacuation experiences, days spent at the evacuation destination, and the cost of evacuation. Socioeconomic factors also influenced the two decisions but differently. These results provide implications for future hurricane evacuation planning and for improving emergency management practices.
The Philippines is a country that has a high risk of climate-related calamities. The most frequent natural hazard in the nation is believed to be typhoons, and countries that experience numerous typhoons are vulnerable to floods. Quezon, one of the country’s provinces located in the eastern part of the Philippines, has had this critical problem. To comprehend how the source of influence affects disaster preparedness behavior, this study incorporated and extended the integrated theories of protection motivation and planned behavior. A total of 525 people responded to an online survey with 45 modified adapted questions that was carried out in the municipalities of Quezon. According to the structural equation modeling, the latent variables, including family and community, media information, and prior experiences, are all reflective of the source of influence. Additionally, the source of influence has a significant and direct impact on perceived vulnerability, perceived severity, attitude toward behavior, perceived behavioral control, and subjective norms. It also indirectly affects the intention to evacuate. This study could not only broaden our understanding of how to prepare for typhoons and floods, but it also offers guidance for planning and managing natural hazard mitigation and disaster risk preparedness in Quezon, Philippines.
The increasing frequency and severity of extreme weather events, such as hurricanes and tropical cyclones, address the importance of prompt and effective natural disaster response strategies. Our research aims at developing a learning-based decision-making framework tailored for evacuation shelter opening time (ESOT), with a focus on prioritizing the demands of vulnerable populations. This approach seamlessly integrates various complex supply- and demand-related factors, including evacuation demand forecasting and shelter operations requirements. The shelter opening time is formulated as a multi-class optimal stopping problem, which readily addresses the trade-off between the risks of false alarms and the perilous consequences of delayed responses, accommodating the uncertainties in disaster state evolution. To improve the computational and sample efficiency, we created a hierarchical policy approximation approach, providing provable optimality guarantees. Through a case study of Hurricane Florence in 2018 using historical wind speed data, our findings demonstrate the efficiency and flexibility of the ESOT policy, clearly outperforming standard stochastic optimization methods. For example, the total cost saving using our approach ranges from 6.6 to 28.2%, and the cost saving is more significant when the variance of the predictor is larger. These results highlight the benefits of integrating learning-based disaster management strategies with physics-informed forecasting models for protecting vulnerable populations in the face of disasters.
This paper explores the relationship among education, knowledge, perception and disaster experience to investigate whether household disaster preparedness behaviour mitigates income losses. We employ instrumental variables approach and generate indigenous knowledge from a large‐scale dataset to examine responsiveness of disaster preparedness via unemployment and production. We identify disaster and climate knowledge perception as new determinant towards disaster risk reduction. Our findings suggest Disaster Preparedness Index (DPI) is almost 64% effective in mitigating household per capita net income loss in comparison with the mean via unemployment channel. We argue that informal education and community‐based training could bring more efficacies in this loss mitigation mechanism.
Objectives:
The Communication (C), Maintaining Health (M), Independence (I), Services, Support and Self-Determination (S), and Transportation (T) is a framework (C-MIST) for identifying functional needs in an emergency response. A C-MIST documentation tool provides shelter staff with a list of potential client needs and actions to address them. This retrospective review describes the needs and actions indicated on completed C-MIST documentation tools (ie, records) within domestic general population shelters following Hurricane Florence in 2018.
Methods:
A convenience sample of 1209 records completed by shelter disaster health services personnel was provided by the American Red Cross. The records correspond to client stays in 19 shelters between September and October 2018. Data abstracted from hardcopy forms were de-identified and recorded in a database. Summary statistics were computed.
Results:
High incidence needs included medical supplies for everyday care (including medications) not related to mobility (15.4%), medically or culturally needed diets (12.2%), durable medical equipment (9.7%), mental health care (8.8%), and transportation (8.4%). High incidence actions included replacement medication (9.3%), refer to Disaster Mental Health Services (6.4%), provide assistive mobility equipment (5.1%), provide diabetes management supplies (5.0%), provide alternative food and beverages (4.1%), and provide transportation (3.9%).
Conclusions:
The process for identifying health and functional support needs in shelters should be standardized through the use of the C-MIST framework.
People-centered risk communication is important to mitigate flood damage caused by the recent increase in heavy rainfall. Longitudinal studies are particularly important for evaluating the effectiveness of different risk communication methods. However, there is a lack of sufficient research. We conducted a longitudinal study, specifically through four panel surveys conducted over a short period of time, to investigate the effects of various risk communication methods, such as a simulated evacuation experience with learning about flood damage, flood-related information distribution including information about the evacuation behavior of others, and hazard maps. The results of a fixed effects analysis of the panel data suggest that the manner in which risk communication is affected varies depending on the initial evacuation attitude. In particular, we found that the distribution of hazard maps had a negative effect on the evacuation behavior of those who initially responded that they would evacuate. This suggests that residents in non-flood-prone areas may have acquired correct hazard perceptions from the hazard maps. However, for those who initially chose not to evacuate, confirmation of the delivered content had a positive effect on their evacuation behavior 12 hours before the typhoon hit. This suggests that those who initially chose not to evacuate may have reconsidered their decision. The risk communication presented in this study may help future risk communication by reducing congestion at evacuation sites due to excessive evacuation, while increasing the evacuation rate of those who should evacuate.
The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022.
Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. (2021a, 2021b, 2022) examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. Compared to the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% compared to 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.
Household relocation decisions after disasters are influenced by many factors. Among others, these include pre- and post-event community conditions, disaster experience, available financial and social resources, place attachment, risk perceptions, and demographics. This paper provides a synthesis of the body of knowledge surrounding voluntary household relocation decisions. Simply stated, we are focused on better understanding what influences the decision to stay somewhere that has been affected by disaster or permanently leave it. This work provides two main contributions by characterizing and synthesizing research exploring relocation drivers. First, we provide several new directions for the study of this issue by proposing theoretical models not commonly used in this area of research with potential to provide insight. Second, we critically discuss the need for improvements in the conceptualization and measurement of these concepts.
This study examines risk perceptions and evacuation planning for those residents affected by Hurricane Laura–the first major hurricane evacuation during the COVID-19 pandemic–and Hurricane Sally, prior to the widespread availability of vaccines. Research on hurricane evacuation behavior and risk perceptions during a pandemic is critical for quantifying the intersect of these compounding threats. Analyses captured how people perceive public shelters and whether evacuation choices changed in light of the pandemic. Many study participants considered themselves vulnerable to COVID-19 (39.4%) and two-thirds believed it would be “very serious” if they or their loved ones contracted COVID-19, but this had no impact on their actual evacuation decision-making. Approximately 75% of the sample stayed at home during Hurricanes Laura or Sally, and of these, just over 80% indicated that COVID-19 was a somewhat important deciding factor. This reflects the partial role that COVID-19 played in balancing individual and household protective action decision-making during complex disasters. Whereas 15.5% wanted to evacuate but waited until it was too late. For those who evacuated to a hotel, many found that staff and guests wore masks and socially distanced in common spaces. Of particular interest is that individuals have a continued negative perception of public shelters’ ability to safeguard against COVID-19 which was coupled with a significant decrease in the number of respondents that would potentially use shelters in 2020 compared to before the COVID-19 pandemic. These results have and will inform future hazard mitigation planning during the current or future pandemic, or infectious disease outbreaks.
Existing studies on disaster-risk management from the humanistic perspective have focused on disaster-risk perception as the starting point and ignored the important role of risk communication in shaping individual risk perception and changing behavioural responses. Taking Sichuan Province―a typical disaster-prone province in China―as an example and selecting rural residents in mountainous areas threatened by multiple disasters as interviewees, this study measured the characteristics of interviewees’ disaster-risk communication in the four dimensions of content preference, channel selection, communication frequency and communication form, and appraised the levels of their disaster-risk perceptions in the four dimensions of possibility, threat, self-efficacy and response efficacy. Additionally, econometric models were used to explore the chain of disaster-risk communication, perceptions and relocation decisions in different scenarios in the context of multiple disasters. The results revealed two main findings. (1) In the scenario where social relations promoted the relocation decision, interviewees derived their relocation decisions by two action paths: disaster-risk communication indirectly influenced relocation decisions through risk perception and disaster-risk communication directly influenced relocation decisions. For the indirect action path, the disaster-risk communication of interviewees had a significant impact on their risk perceptions, and self-efficacy and response efficacy played effective roles in their relocation decisions. For the direct action path, some channels of access to information and indicators of the communication form were significantly correlated with the relocation decision. (2) In the relocation-decision scenario promoted by the government, interviewees derived their relocation decision only by their disaster-risk communication. In risk communication, some channels of access to information and the communication forms of residents were significantly correlated with their relocation decisions, while the role of disaster-risk perceptions was ineffective.
The objective of this paper is to analyze the household response behavior of road users during community evacuations in response to the 2018 Attica wildfires in Greece. To achieve this objective, empirical data were obtained from a questionnaire survey completed by residents from the affected areas by the wildfires of the East Attica in 2018. The design of the survey questionnaire was guided by the Protective Action Decision Model – PADM. Logistic regression models and machine learning techniques such as random forests were developed to identify the critical factors influencing the decision to evacuate or not, as well as the mode choice during evacuation. Findings reveal that the perception of risk, age, years in residence, the number of adults up to 65 years of age in residence, the attempt to obtain information before evacuation, gender, the existence of a prior warning, the number of minors in residence, the level of education and income were the most critical factors to the decision to evacuate during the wildfire. Regarding the mode choice, the most critical factors were the existence of an available vehicle, age, the attempt to obtain information before the evacuation and the perception of risk.
Why do some communities evacuate long-distances in higher rates than others after disaster? This mixed-methods study
uses a new dataset of long-distance evacuation rates after Hokkaido’s Eastern Iburi Earthquake in September 2018, aggregated to the
city level from geolocated Facebook user movement. We found that communities with stronger linking and bridging social capital
tended to see much lower evacuation rates to distant towns. We used statistical models, fieldwork, and content analysis of 12 interviews,
finding that despite rumors on social media, communities with stronger linking social networks had greater trust in government and
decided to stay in local evacuation shelters. This was especially the case if these communities also had stronger bridging social networks,
helping them access key information, especially among vulnerable communities. Meanwhile, residents with weaker linking or bridging
networks may have believed rumors of extreme water, food, and power shortages and left town for good. This study highlights the
importance of trust in local officials when managing evacuation after disasters
While research relating to hurricane evacuation behavior and perceptions of risk has grown throughout the years, there is very little understanding of how these risks compound during a pandemic. Utilizing the U.S. territories of Puerto Rico and the U.S. Virgin Islands (PRVI) as a study region, this work examines risk perceptions and evacuation planning during the first hurricane season following the COVID-19 pandemic before vaccines were widely available. Analyses of how people view public shelters and whether evacuation choices will change in light of COVID-19 concerns were conducted, and results reflect major changes in anticipated evacuation behavior during the 2020 hurricane season. Key findings include that over half of the sample considered themselves vulnerable to COVID-19. When asked about their intended actions for the 2020 hurricane season, a significant number of individuals who would have previously evacuated to a shelter said that they would choose not to during the pandemic, reflecting that public shelter usage has the potential to decrease when the decision is coupled with COVID-19 threats. Additionally, individuals were shown to have a negative perception of public shelter options. Approximately half of the respondents had little faith in shelters’ ability to protect them, and three-quarters of respondents found the risks of enduring a hurricane to be less than those posed by public shelters. These results will inform future hazard mitigation planning during a disease outbreak or pandemic.
This article examines the evacuation behavior of residents in two South Carolina communities, Hilton Head and Myrtle Beach, during the 1996 hurricane season. Two hurricanes that approached South Carolina but hit in North Carolina allowed us to study the impact of repeated “false alarms”; (evacuations ordered based on expectations of a hurricane landfall that proved to be wrong). Differences in evacuation behavior, specific information and concerns prompting evacuation, and the reliability of information sources between hurricane events are examined to determine the impact of false alarms on the credibility of warning systems. Data were derived from a face‐to‐face survey of residents 2 weeks after Hurricane Fran in September 1996. We found that the role of official advisories was more limited than reported in previous research as people sought information on more diverse sets of concerns in their decision making. Reliance on the media and the Weather Channel, in particular, for storm characteristics and advisories was an important factor in evacuation decision making during both hurricane events. The perceived lack of reliability of gubernatorial warnings coupled with dependence on the media suggests that residents find other sources of information more personally relevant. Thus, while residents do not find that officials are “crying wolf,”; they are searching elsewhere for information to assess their own risk—what does it mean to me if there is a wolf? This increased attention toward individual differences in perceived threat may become more pronounced in future evacuations from hurricanes.
This series is dedicated to serving the growing community of scholars and practitioners concerned with the principles and applications of environ mental management. Each volume is a thorough treatment of a specific topic of importance for proper management practices. A fundamental ob jective of these books is to help the reader discern and implement man's stewardship of our environment and the world's renewable resources. For we must strive to understand the relationship between man and nature, act to bring harmony to it, and nurture an environment that is both stable and productive. These objectives have often eluded us because the pursuit of other in dividual and societal goals has diverted us from a course of living in balance with the environment. At times, therefore, the environmental manager may have to exert restrictive control, which is usually best applied to man, not nature. Attempts to alter or harness nature have often failed or backfired, as exemplified by the results of imprudent use of herbicides, fertilizers, water, and other agents. Each book in this series will shed light on the fundamental and applied aspects of environmental management. It is hoped that each will help solve a practical and serious environmental problem. Robert S. DeSanto East Lyme, Connecticut Acknowledgments Compilation of the materials reviewed in this inventory was facilitated greatly by several staff members of the Disaster Research Center, University of Delaware (formerly at The Ohio State University) and the Natural Haz ards Research and Applications Information Center, University of Colorado.
Researchers have conducted sample surveys following at least twelve hurricanes from 1961 through 1989 in almost every state from Texas through Massachutts. The resulting database is larger than that for any other hazard, and many generalizations are feasible concerning factors accounting for variation in response to hurricane threats.
Risk area and actions by public officials are the most important variables affecting public response. When public officials are aggressive in issuing evacuation notices and disseminate the messages effectively. over 90 percent of the residents of high-risk barrier islands and open coasts evacuate. People hearing, or believing they hear, official evacuation advisories or orders are more than twice as likely to leave in most locations. A greater percentage of mobile home dwellers evacuate than occupants of other housing, especially in modelate-risk and low-risk areas. General knowledge about hurricanes and hurricane safety is weakly related or unrelated to evacuation, but belief that one's own home is subject to flooding is strongly associated with whether the occupant leaves. Length of residence in hurricane prone areas and hurricane experience are not good predictors of response. The great majority of people who evacuate unnecessarily in one hurricane will still leave in future threats.
Reflecting a series of converging international trends, the tourist industry represents a vulnerability of catastrophic potential. Interview and questionnaire data obtained from 185 owners or managers in nine U.S.A. communities, provide answers to five questions: (1) What is the extent of disaster evacuation planning? (2) What factors account for the variations in this planning? (3) What behavioral patterns occur during actual evacuations? (4) What factors account for these pattern variations? and (5) What are the policy implications of these behavioral assessments? While many larger firms managed by more professional staff have completed extensive disaster evacuation planning, the overall record in very spotty. Hence, major initiatives both within the industry, and by emergency managers at all levels of government, are needed to reduce this rapidly expanding vulnerability.
In response to a massive flood which struck the metropolitan area of Denver, Colorado, June 16, 1965, approximately 3,700 families were evacuated from their homes. Interviews with a random sample of 278 of these families indicated that the initial response to warnings was marked disbelief regardless of warning source. Families evacuated as units, and data indicated a strong tendency for them to take refuge in homes of relatives rather than in official centers. This tendency was significantly affected by social class. Data further suggested that interaction between relatives during the warning period increased the likelihood that relative homes would be selected as evacuation points.
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