Quantifying human mobility resilience to extreme events using geo-located social media data

Roy, Kamol Chandra; Cebrián, Manuel; Hasan, Samiul

doi:10.1140/epjds/s13688-019-0196-6

Cited by 72 publications

(35 citation statements)

References 53 publications

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“…Therefore, we aimed to observe the evacuation behaviors of residents in these states. Previous studies [14,15] show that evacuation mobility follows the Levy-Walk model, where short distance trips are the majority. Thus, we add the states that share a border with FL, GA and SC; and purchased all tweets with spatial bounds set to cover the states of North Carolina (NC), Tennessee (TN), Southern Carolina (SC), Georgia (GA), Alabama (AL), and Florida (FL).…”

Section: Data Collectionmentioning

confidence: 96%

“…Large-scale spatiotemporal data such as GPS, cell phone data and geotagged social media data enable researchers to model population evacuation behaviors at societal scales [13,14]. Most of recent studies aim to reveal the common patterns of evacuation trajectories, for example the distance and regularity of evacuees' mobility trajectories [14,15]. However, these studies fail to provide detailed evacuation behaviors with specific spatial information, such as evacuation destinations, which is necessary to improve evacuation management.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and predicting evacuation flows during hurricane Irma

Hong

Frías-Martínez

2020

EPJ Data Sci.

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Evacuations are a common practice to mitigate the potential risks and damages made by natural disasters. However, without proper coordination and management, evacuations can be inefficient and cause negative impact. Local governments and organizations need to have a better understanding of how the population responds to disasters and evacuation recommendations so as to enhance their disaster management processes. Previous studies mostly examine responses to evacuations at the individual or household level by using survey methods. However, population flows during disasters are not just the aggregation of individuals’ decisions, but a result of complex interactions with other individuals and the environment. We propose a method to model evacuation flows and reveal the patterns of evacuation flows at different spatial scales. Specifically, we gathered large-scale geotagged tweets during Hurricane Irma to conduct an empirical study. First, we present a method to characterize evacuation flows at different geographic scales: the state level, considering evacuation flows across southern states affected by Irma; the urban/rural area level, and the county level. Then we demonstrate results on the predictability of evacuation flows in the most affected state, Florida, by using the following environmental factors: the destructive force of the hurricane, the socioeconomic context, and the evacuation policy issued for counties. Feature analyses show that distance is a dominant predictive factor with counties that are geographically closer generally having larger evacuation flows. Socioeconomic levels are positively related to evacuation flows, with popular destinations associated to higher socioeconomic levels. The results presented in this paper can help decision makers to better understand population evacuation behaviors given certain environmental features, which in turn will aid in the design of efficient and informed preparedness and response strategies.

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Section: Data Collectionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Modeling and predicting evacuation flows during hurricane Irma

Hong

Frías-Martínez

2020

EPJ Data Sci.

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“…In addition, the power of social media to connect a large group within a population has drawn significant attention toward using social media platforms for disaster management (Keim & Noji, 2010;Sadri, Hasan, & Ukkusuri, 2019;Tang, Zhang, Xu, & Vo, 2015). Studies have analyzed social media data for understanding human mobility and resilience during a disaster (Roy, Cebrian, & Hasan, 2019;Wang & Taylor, 2014). Kryvasheyeu et al (2015) proposed that social media users can be considered as early-warning sensors in detecting and locating disasters.…”

Section: Literature Reviewmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

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“…Misra et al (2014) studied the best practices of using social media data and found that almost every state department of transportation, many public transit agencies, and airports have a social media presence, indicating a dramatic shift on how agencies interact with its customers. Tweets posted during various disasters have been analyzed to extract useful information about the disaster (Imran et al, 2013;Roy, 2018;Roy and Hasan, 2018), individual user behavior (Rashidi et al, 2017;Roy et al, 2019), address the reliability of posted messages (Mendoza et al, 2010), relate with established statistics (Culotta, 2010), increase awareness (Huang et al, 2010), assess damage (Kryvasheyeu et al, 2016), and even to detect earthquake (Earle et al, 2011). Recent studies have investigated on how to understand the interplay among user activities, network properties, and the attention received in social media and to identify factors for successful crisis communication in emergency situations (Sadri et al, 2017a,b).…”

Section: Related Workmentioning

confidence: 99%

Sharing Real-Time Traffic Information With Travelers Using Twitter: An Analysis of Effectiveness and Information Content

Rahman

Roy

Abdel-Aty

et al. 2019

Front. Built Environ.

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Ubiquitous smartphone technologies and virtual social networks offer us a unique opportunity to instantly share information to a large number of people. Online social media platforms facilitate easy and rapid communication of real-time information by producing a huge amount of digital content. In this paper, we present an analysis of the data collected from 14 Florida Department of Transportation (FDOT) Twitter accounts created for sharing real-time traffic information. We analyze the activities, influence, attention received, and the effectiveness of gaining attention by these accounts. We propose several metrics in disseminating real-time traffic information. Using topic models, we also analyze the content of the shared information given in the tweets. Finally, we estimate an ordered logit model to determine the information value of a shared content based on its chance of getting retweeted. Based on the study, we propose a framework called Social Media-based Adaptive Real-time Traffic feed (SMART-Feed) that will significantly improve the effectiveness of real-time traffic information sharing through social media. Moreover, it will help to assess the value of real-time traffic information to travelers and developing social media strategies for sharing information.

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Quantifying human mobility resilience to extreme events using geo-located social media data

Cited by 72 publications

References 53 publications

Modeling and predicting evacuation flows during hurricane Irma

Modeling and predicting evacuation flows during hurricane Irma

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Sharing Real-Time Traffic Information With Travelers Using Twitter: An Analysis of Effectiveness and Information Content

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