Social homophily predicts evacuation destination choice and long-term displacement decisions after the Marshall Fire
Vaidehi Raipat and Takahiro Yabe
1 Department of Technology Management and Innovation, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA 2 Center for Urban Science and Progress, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA - corresponding author: takahiroyabe@nyu.edu
ABSTRACT
Rapid urbanization and climate change have contributed to a significant rise in the frequency and intensity of disasters, which have resulted in three million adults being displaced from their homes in the United States during the past year, according to the Census Bureau. Using large-scale mobility data, it is now possible to observe and analyze post-disaster mobility dynamics across a longer time period, compared to household surveys which are often limited in scale. However, much of the mobility data-driven research on evacuation and displacement destination choice behavior has focused on analyzing the effects of physical and spatial factors, often underemphasizing the role of social factors, including individual preferences and social connections. Motivated by the recent literature showing strong associations between social homophily and mobility behavior, we use large-scale data of anonymized GPS traces and online social connections from the Marshall Fires in Colorado, USA, to unravel the associations between social and behavioral factors and evacuation destinations and long-term displacement decisions. We find that behavioral characteristics and social homophily play a significant role in post-disaster mobility decisions. First, incorporating pre-disaster behavior characteristics increases the predictability of evacuated distance by more than five-fold, which is critical for wildfire evacuation. Second, given the same evacuation distance, evacuees chose locations that have a high sociodemographic homophily with their home locations, and locations that have more friendship connections, compared to the level of homophily they are spatially exposed to. The social homophily effect is stronger among White and educated populations than low income, and Black and Asian populations. This effect has significant implications for long-term disaster impacts and policymaking, as it is a significant predictor of displacement and return decisions. Our findings highlight the importance of incorporating the social, behavioral, and economic characteristics to better understand and predict evacuation and displacement dynamics after disasters. Introduction
An increase in disasters driven by rapid urbanization, climate change, and other environmental factors has created an urgent need to better understand and predict the behavior patterns of affected individuals. These unprecedented environmental changes have also increased the incidence of wildfires throughout the country, causing property destruction and affecting large populations[,] . The movement patterns of disaster-affected populations typically unfold in distinct phases, including evacuation, return, and long-term displacement, each representing complex processes influenced by a variety of social, environmental, economic, and logistical factors . A detailed understanding of these movement dynamics is crucial for designing resilient urban policies that enable effective disaster recovery, such as strategic prepositioning of resources and equitable allocation of post-disaster support to impacted communities . From a behavioral science perspective, disaster evacuation and displacement patterns – particularly following no-notice events such as wildfires – offer a valuable natural experiment for examining the decision-making process of individuals. These scenarios enable us to unravel the critical factors that people prioritize when making mobility decisions[,] . Disaster evacuation and displacement has been a long-studied topic in multiple fields, including civil and transportation engineering, social science, psychology, and complexity science . Household surveys and stated preference data have often been used to identify important factors for evacuation destination choice, including demographics, access to opportunities, existence of friends and family[,][–], transportation convenience, distance between origin and destination[–] . More recently, studies using social media and mobile phone GPS location data (‘mobility data’) have reinforced such findings with more granular and longitudinal data of human behavior[,][,] .
Studies have revealed that factors such as community median income, population, housing damage, race, and education explain the heterogeneity in the initial and long-term displacement rates between communities . However, due to the challenges of collecting large-scale social network and personal preference data, the social and behavioral dimensions of evacuation and displacement decisions have remained underexplored in mobility data-driven research. Contrary to expectations that disasters disrupt and substantially alter mobility patterns, analysis of displacement after the Haiti Earthquake and various disaster events across the world reveal that post-disaster mobility closely mirrors pre-disaster behavior and is highly predictable, underscoring the stability of movement even amid crises. This raises the question of whether social and behavioral characteristics, which have been revealed as strong predictors of mobility patterns during non-disaster situations, also apply to post-disaster mobility dynamics. For instance, studies leveraging geotagged online social media data demonstrate that friendship networks serve as strong predictors of mobility, emphasizing the critical role of social connections in shaping movement decisions[,] . Similarly, research on urban encounters reveals a strong tendency for social homophily in mobility dynamics, often described as ‘experienced segregation’[–] . Together, these findings – similarity between pre- and post-disaster mobility, and strong evidence of social effects on mobility behavior – motivate us to explore the role of social networks and their qualities, such as homophily, in influencing evacuation and displacement movement decisions. To this end, this study contributes to bridging this knowledge gap by leveraging large-scale, longitudinal, and anonymous mobile phone location data collected in the aftermath of a major disaster .
The Marshall Fire serves as a case study to test our hypotheses on post-disaster behavior. The Marshall Fire, a significant disaster in Colorado, began on December 30, 2021 as a forest fire and quickly spread into urban suburbs including Boulder, Louisville, and Superior. Over the course of three days, it displaced more than 3,000 residents and burned approximately 6,080 acres[,][,] . Overall, we find that both individual preferences and social connections significantly contribute to evacuation decisions and destination choice[,] . Compared to the simulated null model of evacuation behavior, we show that individuals choose more socially similar and connected regions as evacuation destinations. Further analysis shows that this tendency varies across sociodemographic groups, and that it has significant associations with long-term outcomes such as displacement and return[,] . These insights could inform urban policies for resource allocation and disaster management both in the short- and long-term after disasters.
Such predictions could also help reinforce and prepare infrastructure systems in affected regions and address immediate needs in destination areas in the event of an unprecedented influx . Results
Using a large and longitudinal dataset of anonymized individual GPS location records in Colorado from November 2021 to June 2022, we analyze the mobility patterns of individuals before, during, and after the Marshall Fire which occurred on December 30, 2021 to January 2, 2022. Anonymous, privacy-enhanced, and high-resolution mobile location ping data (‘mobility data’) of more than 200,000 devices in the State of Colorado was provided by Cuebiq. All devices within the study opted-in to anonymous data collection for research purposes under the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) compliant framework. For privacy purposes, Cuebiq obfuscates device home locations at the census block group level (CBG) by estimating night time stay locations during a period of four weeks before the disaster at night time. Supplementary Note 1.2 & 1.3, and Supplementary Figures S1 show that the mobility data samples have a high correlation with the census population data, indicating that the data are representative of the population.
Moreover, Supplementary Figure S2 & S3 shows that the sample rate has a negligible correlation with household median income and race proportions in the granularity of CBGs, further indicating that the sample is well balanced. To detect evacuation behavior, a similar approach was employed to estimate the night time stay locations during and after the disaster. Each week, the CBG where each individual spent the most time during the night time period was labeled as the night time stay location for the week. An individual was labeled as evacuated if they spent the most time in a CBG other than their home CBG during night time in the three days following the disaster. Supplementary Note 2.1 and Supplementary Figures S4 show the robustness of the results on evacuation behavior modeling against the selection of the minimum time threshold.
To enable a longitudinal analysis, we include individuals whose data is continuously observed and are not missing for at most three consecutive weeks. To focus our analysis on the impacts of the fire, we focus on individuals whose home location was within a 100-kilometer radius of the epicenter. Social determinants of evacuation behavior
Using the weekly night time stay locations of each individual before, during, and after the fire, we first analyze the social and spatial determinants of evacuation dynamics. Figure 1a shows the evacuation rates, defined as the proportion of individuals in the data who evacuated in the first 3 days after the fire, in census block groups (CBGs) within 100 kilometers from the epicenter of the fire.
As expected, we observe a strong spatial pattern of evacuation behavior, where CBGs along the path of the fire (red dotted line) or very close to the fire (red polygon) have higher evacuation rates. To understand the factors associated with evacuation decisions, we tested a logistic regression model with the specification: Pr ∼ logit [−] [G u + SD u + Bu ], where y u = 1 indicates the user u evacuated and y u = 0 otherwise. Independent variables include G u, SD u, and Bu, which are groups of
2/12 geographical, sociodemographic, and behavioral characteristics, respectively. We find that, in addition to the distance from the individual’s home to the epicenter of the fire, and the sociodemographic characteristics of the individual’s home location CBG, pre-disaster mobility behavior patterns played a significant role in determining their evacuation decisions, as shown in Figure 1b. More specifically, metrics such as the individual’s mobility entropy H, which measures the diversity of visits across different locations based on their total dwell time in each location, and the radius of gyration R g, which measures the typical distance a user travels based on their visited locations and the time spent at each location, were strong predictors of evacuation decisions (see Methods for more details on the pre-disaster mobility metrics). The full regression tables are shown in Supplementary Table S1. For effective planning and pre-positioning of resources, understanding how individuals choose evacuation destinations is of substantial importance.
Figure 1c maps the percentages of individuals evacuated to each census block group. We find that, by plotting the histogram of evacuation distances in Figure 1d, that the majority of evacuees move to areas between 20 and 60 km from the epicenter of the fire, indicating a preference to stay relatively close to their home location. To understand the factors associated with the individuals’ evacuation distances r u, we tested a simple linear regression model with the specification: log 10 (r u ) ∼ G u + SD u + Bu, where r u indicates the user u ’s evacuation distance, and G u, SD u, and Bu, are groups of geographical, sociodemographic, and behavioral characteristics, respectively, similar to the previous logistic regression in Figure 1b. By comparing the incremental increase in R-squared with the inclusion of independent variable groups G u, SD u, and Bu, we find that the inclusion of pre-disaster mobility behavior Bu substantially increases the model’s predictability, by more than five-fold. The coefficient of the radius of gyration is highly significant and positive, suggesting that evacuees with a higher radius of gyration tend to travel further distances when evacuating. The full regression tables are shown in Supplementary Tables S6, S7 & S8. These results suggest that the behavioral factors of individuals are strong determinants of evacuation decisions. Social homophily between home and evacuation destinations
The results on evacuation behavior so far indicate that the evacuation behavior of individuals is strongly associated with the individuals’ pre-disaster behavioral traits. Similarly, studies suggest that the individuals’ preferences and social ties are important in evacuation destination choice[,][,][,][,] . Here, we leverage the high granularity of the mobility data to assess the effects of social homophily on destination choice. More specifically, we analyze the effect of social homophily on evacuation destination choice by measuring i) sociodemographic similarity (“Social Similarity” Si j ) and ii) the number of friendship connections (“Social Connectedness” Ci j ) between the destination j and the home location (origin) i, as shown in Figure 2a. The similarity index Si j is computed as the cosine similarity between the vectors of sociodemographic characteristics of the origin and destination CBGs. Sociodemographic characteristics include population density, race proportions, level of education, unemployment rates, and the median income of the CBG. Supplementary Note 3.1 - 3.4 contains the robustness checks on the selection of the sociodemographic characteristics. The social connectedness Ci j is measured using Facebook’s Social Connectedness Index (SCI), which measures the probability of a friendship connection on Facebook between two locations (zipcode). Compared with the physical distance d i j between CBGs i and j, we find that both the similarity and connectedness metrics decay with distance, as shown in Figure 2b. This suggests that individuals who evacuate longer distances are more likely to stay in less similar and connected neighborhoods, which could lead to fewer social connections and support, which are suggested in the literature to be essential ingredients for effective disaster response and recovery . Studies have used the gravity model as the standard model to characterize and model evacuation patterns after disasters[,] . However, behavioral preferences and the characteristics of the destination could also play a crucial role in evacuation patterns. Despite the availability of large scale mobility datasets, such dynamics have been rarely quantified. To investigate the significance of behavioral preferences, we construct a null model to simulate evacuation destinations for each individual . In the null model, illustrated in Figure 2c, counterfactual destinations j [′] are randomly simulated for each individual with probabilities proportional to the gravity model pi j = M j /d i j [, which ignores any social and behavioral preferences. The randomly] simulated counterfactual destinations j [′] consider the spatial configurations of surrounding cities and communities and their sizes, allowing us to quantify the level of social similarity and connectedness to which individuals are spatially exposed. Figure 2d shows the distribution of social similarity Si j ′ and social connectedness Ci j ′ for actual and simulated destinations. The distribution of the similarity and connectedness measures of the actual destinations is skewed towards higher values, compared to the simulated destinations using the null model.
The gray vertical lines in both figures show the mean values of the simulated destinations, and the blue and orange vertical lines show the mean values of actual destinations. The differences in the means are statistically significant in both cases with a p < 0.001, indicating that social homophily is an important factor in evacuation destination choice decision, and that leveraging a simple gravity model could overlook the complexity of decision making during disaster evacuation. 3/12 Figure 1. Determinants of evacuation decisions after Marshall Fire. a) Spatial distribution of the percentage of people that evacuate from each census block group. Higher rates of evacuation were observed near the epicenter of the fire (red polygon) and along the path of the fire (red dotted line). b) Logistic regression coefficients for predicting individuals’ evacuation behavior. Significant and positive coefficient for entropy suggests that individuals having exploratory pre-disaster mobility behavior is the strongest predictor for evacuation behavior. The error bars show 95% confidence intervals.
Results suggest that individuals with higher radius of gyration before the disaster tend to travel further distances for evacuation. Maps were produced in Python using the TIGER shapefiles from the U.S. Census Bureau . Demographic inequity in social homophily of destinations
The counterfactual simulations developed in Figure 2c have shown that evacuees tend to prefer destinations that have higher sociodemographic similarity and social connections, conditioned on the spatial configuration of cities and their sizes. However, it is unclear whether this tendency is homogeneous between different sociodemographic groups . The potential inequity between demographic groups is important to quantify, due to the possible difficulties individuals from more vulnerable populations may face when evacuating to areas that are unfamiliar or communities lacking social ties[,] . To measure this heterogeneity, we use linear regression to model the association between the evacuees’ demographic characteristics SD i and
4/12 Figure 2. Social homophily and destination choice a) Two metrics for social homophily are studied: Similarity Si j is defined as the cosine similarity between origin (i) and destination ( j) census block groups, using demographic attributes including median income, population density, racial composition, and percentage of educated population. Connectedness Ci j between i and j is defined by the number of friendship connections between these locations as represented on Facebook. b) Similarity and connectedness decay with distance between i and j, indicating that individuals who evacuate longer distance are more likely to stay in less similar and connected neighborhoods. c) The null model for evacuation destination simulation. For each individual, the destination is randomly selected proportional to the probability pi j = M j /d i j [derived from the gravity model. This null] model assumes that individuals choose evacuation destinations based on distance and population density, ignoring social factors. d) Similarity Si j (blue box plots) and connectedness Ci j (orange box plots) are calculated for both actual and simulated destinations.
Results show higher similarity and connectedness for actual destinations compared to the null model, indicating that social homophily substantially affects the choice of destinations during evacuation. Figures 2a and 2c were designed using icons from the Noun Project created by pictranoosa. the social homophily of realized destinations Si j,Ci j, controlled on the evacuation distance d i j . Thus, the regression model is: {Si j,Ci j } ∼ d i j + SD i . As shown in Figure 3a, the regression coefficients for White percentage and median household income are positive and significant, indicating that evacuees who live in census block groups (CBG) with higher median household income and higher percentage of white population have a greater tendency to select connected and similar destinations, respectively. On the other hand, individuals with higher proportions of Black populations had a higher tendency to evacuate to areas with lower levels of sociodemographic similarity and social connectivity. The full regression tables are shown in Supplementary Table S9 to S14. The observed inequity in realized social homophily during evacuation, shown in Figure 3a, is a significant and substantial difference, however, could be due to the lack of options for Black populations (i.e., there are fewer communities that are sociodemographically similar and socially connected with such communities).
To test whether this inequity is the result of behavioral preferences or due to the lack of opportunities, we leverage the simulated evacuation patterns from Figure 2. For each individual, we compute the difference in similarity ∆Si j = Si j − Si j ′ and connectedness ∆Ci j = Ci j − Ci j ′ . We regress the differences in similarity and connectedness between the actual and simulated destinations using a linear regression: {∆Si j, ∆Ci j } ∼ d i j + SD i, where SD i are sociodemographic variables including White percentage, Black percentage, and the median income of the origin census block group. The results, shown in Figure 3b, reveal significant and positive coefficients for the White population percentage, indicating that evacuees from predominantly White CBGs selected destinations that were more similar and connected than predicted by the gravity-based model. These results advocate for the incorporation of behavioral preferences in predictive evacuation models, enhancing their ability to accurately reflect observed patterns of movement and providing a more robust framework for disaster response planning. Supplementary Tables S15 to S20 show the
5/12 full regression results. Figure 3. Sociodemographic inequity in social homophily after evacuation a) Regression coefficients for {Si j,Ci j } ∼ d i j + SD i, where SD i are sociodemographic variables including White percent, Black percent, and median income of the origin census block group. Controlling for evacuation distance d i j, we find that white and median-income populations are more likely to evacuate to destinations that have high social homophily with their origin. b) Regression coefficients for {∆Si j, ∆F i j } ∼ d i j + SD i, where SD i are sociodemographic variables including White percent, Black percent, and median income of the origin census block group. Evacuees from neighborhoods with higher proportions of White residents chose destinations significantly more similar and connected compared to simulated destinations that were based on distance and population density only, indicating the significant effects of behavioral preferences. In both plots, the error bars show 95% confidence intervals. Social homophily during evacuation is associated with long-term displacement outcomes Understanding and predicting long-term patterns of individual movements, including displacement and return, is essential to ensure resilience and recovery of disaster-affected areas[,] . The results in Figures 1 to 3 show the importance of utilizing behavioral preferences in modeling evacuation decisions and destination choice, however, how do these insights inform long-term outcomes? To answer this question, we investigate whether the social homophily of the evacuation destination has any association with the decision to return or not in the long term. Figure 4a shows the percentage of individuals who returned to their original home location CBG after evacuation, across 22 weeks after the disaster. The red curve shows the return percentage of individuals who were directly impacted (home was located in the CBGs that were recorded as burnt) and evacuated. Their return rate is slower and lower. The gray curve shows the return percentage of those who were living outside the directly impacted zone (experienced the fire) and evacuated. These individuals have a shorter period until return and an overall higher return percentage. Bootstrapping was used to compute the confidence intervals for each week’s return percentage of precautionary and directly impacted evacuees to show the statistical significance of the difference in return percentages. Figure 4b shows the regression coefficients of similarity and connectedness of destination locations on long-term displacement outcomes. More specifically, we conducted a logistic regression with the specification: Pr ∼ logit [−] [Si j +Ci j ], where r u = 1 indicates that the user u returned and r u = 0 indicates displacement. Independent variables Si j and Ci j are the social similarity and social connectedness between the user’s pre-disaster home CBG and evacuation destination CBG. The results suggest that similarity and connectedness of the evacuation destinations in the long-term had significant impacts on their displacement outcomes. Evacuees who chose destinations with high social connectedness had higher rates of return. Choosing a destination with high social similarity had the opposite effect for evacuees, decreasing their likelihood of returning to their original home locations. The mixed results of social
6/12 homophily on return and displacement provide nuanced insights on how we may be able to predict long-term post-disaster movement outcomes by assessing the social characteristics of where the individual evacuated to. The full regression tables are shown in Supplementary Tables S21 to S28. Figure 4. Social homophily during evacuation is associated with long-term displacement outcomes. a) Weekly percentage of evacuees who returned after the fire. The return rate is consistently higher for evacuees located in the burned regions compared to those outside. 84.1 percent of evacuees from the non-burned areas and 64.1 percent of evacuees from the burned areas returned by the 8th week. The recovery rate slows significantly after week 8. The error bars show 95% confidence intervals estimated using bootstrapping method. b) Regression coefficients of long-term displacement outcomes on the similarity and connectedness of destination locations. The similarity and connectedness of evacuation destinations in the long-term had significant impacts on their displacement outcomes. Evacuees who chose connected destinations returned more in the long-term. Choosing a similar destination location had the opposite effect for evacuees from non-burned areas, decreasing the likelihood of returning to their original home locations.
The error bars show 95% confidence intervals. Discussion
Cities and regions worldwide are increasingly exposed to the risks of climate change-induced disasters. Understanding how individuals make short- and long-term decisions during evacuation, return, and displacement is critical to developing resilient urban policies . While extensive research has explored the relationships between social, demographic, geographic factors, hazard characteristics (e.g., seismic intensity), and evacuation behaviors, these studies often rely on household surveys . However, such surveys are frequently constrained by limited spatial and temporal coverage and issues of representativeness. The availability of passively collected, large-scale behavioral datasets, such as mobile phone GPS location data and social media data, has provided new opportunities to analyze evacuation dynamics . Despite this progress, there has been limited effort to develop computational approaches that test hypotheses about the social and behavioral determinants of evacuation and displacement. Furthermore, there remains a critical gapin understanding how these characteristics influence long-term outcomes for affected populations. In this context, this study makes three contributions to understanding the social and behavioral determinants of evacuation and displacement dynamics, using large-scale mobile phone GPS location data collected from individuals affected by the Marshall Fire in Colorado. First, our analysis reveals that pre-disaster mobility characteristics are the strongest predictors of both the likelihood of evacuation and the distance traveled during evacuation. Specifically, individuals with less predictable movement patterns before the disaster were more likely to evacuate, and those with a greater radius of gyration tended to travel farther distances. This suggests that individuals who typically travel farther distances may be more accustomed to managing unfamiliar environments, making them more likely to relocate further during crises. These findings align with and extend prior research on mobility profiles, such as the classification of explorers and returners, by showing that these characteristics also have implications on mobility behavior during emergencies. This finding also adds more nuance to the previous findings about how post-disaster mobility mirrors pre-disaster mobility[,] . From a practical perspective, linking
7/12 individual-level mobility patterns before and after the disaster can enhance our ability to anticipate evacuation behavior and identify populations that are more likely to evacuate to further locations. Second, our analysis of evacuation destination choices based on social connectedness and similarity reveals that evacuees, when controlled for distance, tend to select destinations that have higher social homophily. White and educated populations had a stronger tendency to select destinations with high similarity, while lower-income and minority populations were less likely to choose connected destinations. These findings mirror the social homophily observed in non-disaster mobility patterns using Twitter data from US cities[,] . The observed differences in the tendency for social homophily during evacuation between sociodemographic groups suggest potential disparities in access to social capital[,] . This could help prioritize resource allocation in improving access to shelters, especially for the vulnerable population who lack social capital. Finally, evacuees who relocated to destinations with high social connectedness or demographic similarity exhibited slower return rates or, in some cases, did not return at all. This could reflect the influence of social ties and support systems that provide stability, making it less urgent or necessary for evacuees to return to their original homes and also less stressful in cases where their home faces tremendous damages . The findings underline the role of homophily – the preference for social interactions with similar others – in shaping both immediate evacuation decisions and long-term recovery processes. This finding suggests the importance of disaster relief programs to consider the role of community bonds in supporting displaced individuals. It also reinforces the potential benefits of enabling evacuees to resettle in socially supportive environments.[,]
Our study has several limitations. Although the GPS location data used in the study allow us to observe and analyze how social homophily is associated with evacuation patterns, we are not able to observe the actual social interactions (e.g., support and altruistic behavior) between the evacuees and the recipient population.
Combining this analysis with data collected from household surveys, interviews, and focus groups in this context could enhance our understanding of the mechanism of how evacuating to an area with high social homophily affects the post-disaster recovery outcomes of individuals and households. The social connectedness index data provided by Meta captures the relationships between users on Facebook. Although numerous studies have used this dataset to analyze social-spatial network structures, this could overlook non-digital forms of social capital or networks outside of this platform.
Integrating alternative data sources to triangulate social-spatial connectivity could further strengthen the insights obtained in this study. The findings on the social and behavioral determinants of evacuation, displacement, and return mobility decisions highlight several directions for future research. In this study, we analyzed post-disaster mobility patterns within the specific context of a single major disaster. While we do not claim or hypothesize that these findings are universally applicable across other disasters in different geographies and contexts, our results provide concrete evidence of the critical role that social and behavioral determinants play in shaping post-disaster mobility dynamics. Furthermore, this research offers a computational and analytical framework that can be applied to datasets collected from other disasters to explore similar questions. A valuable next step would be to classify disaster typologies based on whether social and behavioral determinants have positive or negative effects on mobility decisions.
Such a comprehensive approach could deepen our understanding of the universality and variability of how different factors affect post-disaster mobility patterns, and inform more targeted and effect disaster resilience strategies. Methods
Evacuation behavior detection
We leverage a large and longitudinal dataset of anonymized individual GPS location mobility data provided by Cuebiq to analyze evacuation, destination choice, return and displacement patterns among individuals residing in Colorado, focusing on nighttime dwelling patterns pre- and post-disaster. We gather daily stop data within the specified period, organizing and processing it into weekly segments to identify nighttime location across census block groups (CBGs). For each day, we retain the top three CBGs where the user spends the most nighttime dwell time, enabling a high-resolution view of their primary recurring locations. If a user consistently has a maximum nighttime dwell time in the same CBG for four weeks pre-disaster, this CBG is defined as their home location. To detect evacuation behavior, individuals were classified as evacuated if they spent more than 480 minutes of nighttime at a census block group (CBG) that was not their home CBG during the three days of the disaster event. Similarly, post-disaster home locations were identified for individuals residing within 100 km of the disaster epicenter by analyzing nighttime dwell durations exceeding 480 minutes. The dataset spans four weeks pre-disaster, the disaster period, and twenty-two weeks post-disaster. To ensure robust location assignment, only individuals with consistently observed data allowing for a maximum of three consecutive weeks of missing data were included in the analysis.
Nighttime dwell time exceeding the specified threshold of 480 minutes served as the primary criterion for determining both evacuation status and home locations. 8/12 Pre-disaster mobility behavior We use two metrics to analyze the pre-disaster mobility behavior of selected evacuees by calculating two metrics: entropy Hand radius of gyration (R g ) . These are computed based on all available stops for each user recorded mobility data from November 1st to 30th, 2021 and capture the spatial and temporal patterns of an individuals movement across visited locations. We use entropy H to quantify the diversity (or unpredictability of) an individual’s mobility. Higher entropy values indicate visits spread more evenly across multiple locations, suggesting greater diversity and low predictability, while lower entropy values indicate more concentrated movement patterns and high predictability. Shannon entropy, H = − ∑(pk log(pk )), where pk represents the probability of visiting a specific location k, is computed using the `scipy.stats.entropy` function in Scipy . We define pk as the proportion of total dwell time spent at a location relative to the total dwell time across all locations visited by the user. Shannon entropy thereby provides a metric for assessing the variability or concentration of an individual’s location choices over time. The radius of gyration ( R g ) represents the typical distance a user travels on an average
day, capturing spatial dispersion. The R g weighted by dwell time is computed as R g = � N1 [∑] [i][∈][L] [ n] [i] [(][r] [i] [ −] [r] [cm] [)] [, where] [ L] [ is the set]
of locations, r i the geographic coordinates of location i, n i the time spent at location i, and r cm the center of mass of all visited locations.
SCI measures the relative probability of Facebook friendship links between two locations i and j because it is scaled from maximum value of 1,000,000,000 and a minimum value of 1 and a random noise is added to round the number to the nearest integer. Counterfactual simulations of destinations
To investigate the significance of behavioral preferences in evacuation destination choice, we construct a null model to simulate evacuation destinations for each individual. In the null model, each evacuee’s counterfactual evacuation destination j [′] is randomly simulated for each individual with probabilities proportional to the gravity model pi j = M j /d i j [, among a set of] candidate CBGs that are located at a similar distance from the home location as the actual evacuation destination. This weighted sampling method reflects the idea that destinations with larger populations exert a greater pull on evacuation behavior. Using these probabilities, one destination per individual was sampled. The connectedness and similarity levels between the actual and simulated destinations by calculating the mean similarity and familiarity for both. Linear regression was employed to examine the relationship between evacuees’ demographic characteristics ( SD i ) and the social homophily of their chosen destinations ( Si j,Ci j ), while controlling for evacuation distance ( d i j ). Furthermore, the differences between actual and simulated destinations in social similarity and social connectedness {∆Si j, ∆Ci j }, was regressed to measure the roles of structural and behavioral factors in shaping evacuation choices. 9/12 Data Availability
The data that support the findings of this study are available from Cuebiq through their Social Impact program, but restrictions apply to the availability of these data, which were used under the license for the current study and are therefore not publicly available. Information about how to request access to the data and its conditions and limitations can be found in `[https://cuebiq.com/social-impact/](https://cuebiq.com/social-impact/)` . Data access requests should be submitted through Cuebiq’s Social Impact customer page `https://cuebiq.com/demo/`, where the Sales team at Cuebiq may be contacted. Other data including the American Community Survey is available for download at `[https://data.census.gov/](https://data.census.gov/)`, and Tiger shapefiles can be downloaded from the US Census Bureau `[https://www.census.gov/programs-surveys/geography/](https://www.census.gov/programs-surveys/geography/guidance/tiger-data-products-guide.html)` `[guidance/tiger-data-products-guide.html](https://www.census.gov/programs-surveys/geography/guidance/tiger-data-products-guide.html)` . Code Availability
The analysis was conducted using Python. Code to reproduce the main results in the figures from the aggregated data is publicly available on GitHub `` .