Big Data and Human Mobility

Human mobility during disruptive events

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.

Publication:
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 MariaPopulation 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.

Geotagged tweets of an evacuated user and non-evacuated user (Tweets time span for the evacuated user: May, 2016-Feb., 2018, for the non-evacuated user: Dec., 2011-Feb., 2018)

Activity space comparing a non-evacuated (a) and an evacuated (b) Twitter user

Publication:
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

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