10.5061/DRYAD.88354
Wang, Qi
Harvard University
Taylor, John E.
Virginia Tech
Data from: Patterns and limitations of urban human mobility resilience
under the influence of multiple types of natural disaster
Dryad
dataset
2017
Natural disasters
Geo-Social Networking
2014
2013
Twitter
2017-01-08T00:00:00Z
2017-01-08T00:00:00Z
en
https://doi.org/10.1371/journal.pone.0147299
44130784 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Natural disasters pose serious threats to large urban areas, therefore
understanding and predicting human movements is critical for evaluating a
population’s vulnerability and resilience and developing plans for
disaster evacuation, response and relief. However, only limited research
has been conducted into the effect of natural disasters on human mobility.
This study examines how natural disasters influence human mobility
patterns in urban populations using individuals’ movement data collected
from Twitter. We selected fifteen destructive cases across five types of
natural disaster and analyzed the human movement data before, during, and
after each event, comparing the perturbed and steady state movement data.
The results suggest that the power-law can describe human mobility in most
cases and that human mobility patterns observed in steady states are often
correlated with those in perturbed states, highlighting their inherent
resilience. However, the quantitative analysis shows that this resilience
has its limits and can fail in more powerful natural disasters. The
findings from this study will deepen our understanding of the interaction
between urban dwellers and civil infrastructure, improve our ability to
predict human movement patterns during natural disasters, and facilitate
contingency planning by policymakers.
Patterns and Limitations of Urban Human Mobility Resilience under the
Influence of Multiple Types of Natural Disaster (Original Data)The file
includes the location data from 15 natural disaster events that are used
for this research.
World