Abstract
Individuals react very differently to evacuation orders as their decisions depend on various factors. Identifying key contributing factors and understanding how they affect individuals’ evacuation decisions can help emergency response organizations improve evacuation plans and communication strategies. Conventionally, researchers have studied human evacuation behaviors by conducting post-disaster surveys, which could be costly, be limited by sampling methods, and be dependent on respondent availability resulting in non-timely responses. Social media, becoming an important communication channel during a disaster, can provide alternative data to examine evacuation behavior in near real-time at a relatively low cost. This study explores how social media data can be used to gain insight on human evacuation behavior. We designed a conceptual model, developed a codebook to classify Twitter communications, and employed a Bayesian Network approach to build a model to inductively learn dependence relationships of evacuation decision making factors from tweets. In analyzing tweets during the Lilac Fire in San Diego, CA, the learned Bayesian Network highlighted two key factors, risk perception and received information source, that jointly influenced the individual’s evacuation decision making. This case study also implied that factors related to individual/family situations, evacuation situations, knowledge, and previous experience may not be primary decision-making factors.
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Acknowledgements
This work was supported by the National Science Foundation under Grant No. 1634641, IMEE project titled “Integrated Stage-Based Evacuation with Social Perception Analysis and Dynamic Population Estimation” and the San Diego State University Summer Undergraduate Research Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation or the San Diego State University. We would also like to thank Christian Mejia for his hours spent to support data preprocessing.
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Appendix: Tweets Codebook
Appendix: Tweets Codebook
Variable | Code | Description |
|---|---|---|
English | No = 0 | Contained English words |
Yes = 1 | ||
Retweet | No = 0 | Contained “RT @ USERNAME” at the beginning of the text |
Yes = 1 | ||
Twitter user account type | Individual = 1 | Type of the Twitter user account |
Law enforcement/Fire Dept. = 2 | ||
CalFire = 3 | ||
211 San Diego = 4 | ||
American Red Cross = 5 | ||
Office of Emergency Services = 6 | ||
Media = 7 | ||
Community group = 8 | ||
Local government agency = 9 | ||
Other government agency = 10 | ||
Other non-profit = 11 | ||
Other = 12 | ||
Evacuated | No = 0 | Contained any sign or mention that the Twitter user evacuated |
Yes = 1 | ||
Evacuating | No = 0 | Contained any sign or mention that the Twitter user was evacuating |
Yes = 1 | ||
Pre-evacuation | No = 0 | Contained any sign or mention that the Twitter user was in pre-evacuation status (e.g., in preparation) |
Yes = 1 | ||
Not evacuated | No = 0 | Contained any sign or mention that the Twitter user decided not to evacuate |
Yes = 1 | ||
Attitude toward evacuation | Negative = 0 | Contained any sign or mention whether the Twitter user’s attitude toward evacuation was positive (likely to evacuate), negative (unlikely to evacuate), neutral (undecided/uncertain), or not stated |
Positive = 1 | ||
Neutral = 2 | ||
Not stated = −1 | ||
Perceived risk/threat | Low = 0 | Contained any sign or mention that the Twitter user perceived high/low risk of the incident |
High = 1 | ||
Not stated = −1 | ||
Information received | No = 0 | Contained any sign or mention that the user received information from other(s) or shared other URLs regarding evacuation or wildfire |
Yes = 1 | ||
Information source | Family/relative = 0 | The source of received information |
Other individual = 1 | ||
Governmental/official agency = 2 | ||
Non-profit organization = 3 | ||
Media = 4 | ||
Other = 5 | ||
Unknown source = 6 | ||
Not stated = −1 | ||
Information type | Official evacuation order = 0 | Emergency notification type |
Official evacuation warning = 1 | ||
Other evac. notifications = 2 | ||
Others = 3 | ||
Not stated = −1 | ||
Information credibility | Negative = 0 | Contained any sign or mention of the Twitter user’s negative or positive attitude toward the information source |
Positive = 1 | ||
Not stated = −1 | ||
Previous experience | No = 0 | Contained any sign or mention of the Twitter user’s previous experience with evacuation |
Yes = 1 | ||
Evacuation knowledge | No = 0 | Contained any sign or mention of the Twitter user’s knowledge about evacuation |
Yes = 1 | ||
Pet ownership | No = 0 | Contained any sign or mention of pet ownership |
Yes = 1 | ||
Medical issue | No = 0 | Contained any sign or mention of medical issue |
Yes = 1 | ||
Home ownership | No = 0 | Contained any sign or mention of home ownership |
Yes = 1 | ||
Mobile home | No = 0 | Contained any sign or mention that the user is living in a mobile house |
Yes = 1 | ||
Job responsibility | No = 0 | Contained any sign or mention of job responsibility |
Yes = 1 | ||
Family safety | No = 0 | Contained any sign or mention of concerning family safety |
Yes = 1 | ||
Elderly in household | No = 0 | Contained any sign or mention of senior citizen(s) in a household |
Yes = 1 | ||
Children in household | No = 0 | Contained any sign or mention of children in a household |
Yes = 1 | ||
Disability | No = 0 | Contained any sign or mention of individual/family member with disabilities in household |
Yes = 1 | ||
Place to evacuate | No = 0 | Contained any sign or mention of having a place to evacuate |
Yes = 1 | ||
Traffic congestion | No = 0 | Contained any sign or mention of the traffic congestion in the Twitter user’s neighborhood |
Yes = 1 | ||
Means of transportation | No = 0 | Contained any sign or mention of the possession of reliable means of transportation to evacuate |
Yes = 1 | ||
Time availability | No = 0 | Contained any sign or mention of the time availability to evacuate |
Yes = 1 |
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Nara, A. et al. (2021). Learning Dependence Relationships of Evacuation Decision Making Factors from Tweets. In: Nara, A., Tsou, MH. (eds) Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-83010-6_7
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