Studies have found that individual’s movement pattern shows regularity and is highly predictable. Numerous studies have contributed to improve the prediction accuracy in individual’s movements. However, such regularity can be disrupted by different events. In this research focus, we aim to reveal and better understand human mobility patterns during disruptive events.
Population movements to and from Puerto Rico after Hurricane Maria
After a disaster, there is an urgent need for information on population mobility. Our analysis examines the suitability of Twitter data for measuring post-disaster population mobility using the case of Hurricane Maria in Puerto Rico. Among Twitter users living in Puerto Rico, we show how many were displaced, the timing and destination of their displacement, and whether they returned. Among Twitter users arriving in Puerto Rico after the disaster, we show the timing and destination of their trips.
We find that 8.3% of resident sample relocated during the months after Hurricane Maria and nearly 4% of were still displaced 9 months later. Visitors to Puerto Rico fell significantly in the year after Hurricane Maria, especially in tourist areas. While our Twitter data is not representative of the Puerto Rican population, it provides broad evidence of the effect of this disaster on population mobility and suggests further potential use.
Martín Y., Cutter S.L. Li Z, Emrich C., Mitchell, J.T. (2020) Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria. Population and Environment
Activity Space and Evacuation
People’s ability to evacuate out of their home county heavily relies on mobility, which also limits one’s activity space in the long-term. Twitter users’ geotagged tweets are one way to record their historical travel records. We tested whether long-term estimated activity spaces, reflected by Twitter data, are different between the evacuated group and non-evacuated group using the Hurricane Matthew as the study case. We found that evacuated people have larger long-term activity spaces than non-evacuated people. The following figures show the activity space comparing an evacuated and non-evacuated Twitter user.
Jiang Y., Li Z., Cutter S.,(2019) Social network, activity space, sentiment, and evacuation: what can social media tell us? Annals of the American Association of Geographers, DOI: 10.1080/24694452.2019.1592660
Mapping the human movements during the 2017 Solar Eclipse
Various cities along the 2017 Solar Eclipse’s path of totality are expected to draw record numbers of visitors for the historic event. In this project, we analyzed billions of geotagged tweets posted by more than 20 million global Twitter users to map their location before, during and after the eclipse, by using a series of technologies including Big Data mining, spatial analysis, cloud computing, and geographic visualization/mapping. The project’s goal is to pinpoint which cities or states along the path of totality attract the most people and identify the potential collective human movement patterns triggered by the event on a global scale.
The map below shows the difference of the number of unique Twitter users between Monday, 08/21/2017 and Monday, 08/14/2017 (a week before). Note that it took less than 1 minute to generate the world map (0.05 by 0.05 degrees) with our big data computing system.
The left figure below shows the global twitter users movement during a normal week (July 10th – 11th, 2017). The right figure below shows the
global twitter users movement during the eclipse week (Aug 10th – 11th, 2017). Clearly, move movements are observed that is likely caused by the solar eclipse.
Li Z., Jiang Y., Social Media, Human Movement, and the Eclipse, Public Mini-lecture on the Eclipse at Russel House Theater, August 21, 2017, USC (invited)