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Characterizing Social Response to Urban Earthquakes using Cell-Phone Network Data: The 2012 Oaxaca Earthquake
 

Characterizing Social Response to Urban Earthquakes using Cell-Phone Network Data: The 2012 Oaxaca Earthquake

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The data generated by pervasive infrastructures, and ...

The data generated by pervasive infrastructures, and
specially cell-phone networks, has been used in the past
to improve responses to emergency events such as natural
disasters or disease outbreaks. However, very little
work has focused on analyzing the social response to
an urban earthquake as it takes place. In this paper
we present a preliminary study of the social response
using the information collected from a cell-phone network
during the 2012 Oaxaca earthquake in Mexico.
We focus our analysis on four urban environments located
between 100-200km away from the epicenter of
the earthquake. The social response is analyzed using
four different variables: call volume, call duration, social
activity and mobility. Initial results indicate a social response
characterized by an increase in the number of
calls, a decrease in call durations, a moderate increase
in the number of people contacted by highly connected
citizens and a moderate increase in the mobility.

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    Characterizing Social Response to Urban Earthquakes using Cell-Phone Network Data: The 2012 Oaxaca Earthquake Characterizing Social Response to Urban Earthquakes using Cell-Phone Network Data: The 2012 Oaxaca Earthquake Presentation Transcript

    • Characterizing Social Response to Urban Earthquakes using Cell-Phone Network Data: The 2012Oaxaca Earthquake Enrique Frias-Martinez Telefonica Research, Madrid, Spain efm@tid.es
    • Introduction  Data of pervasive infrastructures has beed used to improve responses to emergency events.  Eartquakes, virus spreading  CDRs, GPS, twitter, airplane connections  The goal is to lear lessons for the future
    • Unprecedented Historic Moment Digital Footprints For the first time in human history, we have access to large-scale human behavioral data at varying levels of spatial and temporal granularities
    • Related Work  Bengtsson – in Haiti using CDRs  New Zealand Government using CDRs  Bagrow – uses CDRs – focuses on Emergencies – Social Protests and Terrorist attacks  Tatem – CDRs and malaria  Frias-Martinez et al. - CDRs and H1N1
    • Goal We present saome preeliminary results on the social behavioral changes that took place during the 2012 Oaxaca earthquake in Mexico:  From the network perspective (aggregation)  During the Earthquake  On Cities
    • 2012 Oaxaca Earthquake 7.4 magnitude and struck at 12:02 local time on Tuesday, 20 March 2012 Two people were killed and over 30,000houses were damaged or destroyed Around 800 aftershockswere reported in the Guerrero and Oaxaca region, the strongest of them with a magnitude of 6.0.
    • 2012 Oaxaca Earthquake
    • Call Detail Records 2233445566|3E884DB|15/02/2011|23:02:35|... 2233445567|3E884DC|16/02/2011|23:02:35|... 2233445568|3E884DD|17/02/2011|23:02:35|... 2233445569|3E884E5|18/02/2011|23:02:35|... URBAN 1-4km²
    • CDRs  Two weeks of data for the affected area  Also 2 weeks for the city of Monterre  Original records are encrypted  No contract or demographic data  None of the authors of this paper collaborated in the extraction of the data  Just five towers were affected by the earthquake.
    • Methodology Behavioral changes in five urban areas: Mexico City, Oaxaca, Veracruz, Acapulco and Monterrey. Evaluate how call volume, call duration, social activity and mobility change during the earthquake. For each city and behavioral parameter we generate two signals: an ’earthquake’behavior signal and a baseline behavior signal
    • Methodology In order to understand changes in behavior, we will report quantitative differences between the earthquake and baseline behaviors We apply the Lilieffors test to test for normal distributions followed by t-test or Wilcox rank sum and Kolmogorov-Smirnoff (K-S)tests respectively.
    • Call Volume Time series containing the normalized number of calls initialized each minute.
    • FROM 12:00 to 12:10 -All four cities rejected the Wilcox Rank- Sum and K-S tests indicating that the increase in call volumes across these cities is statistically significant.
    • Call Duration Call duration for each city as a time series containing the average duration of the calls initialized at each minute.
    • Call Duration FROM 12:00 to 12:10 Wilcox Rank-Sum and K-S tests rejected the null hypothesis for all cities under study, with the exception of Monterrey indicating that the differences between baseline and earthquake signals were statistically significant for all four cities affected by the earthquake.
    • Social Activity Increase in acitivity corresponds to people reaching to friends outside their typical contacts or the usual contacts are contacted multiple times? Increase of activity in high-active users and dicrease in low-active users. No statistical signifcance of results (12:00-18:00)
    • Mobility Number of different BTS towers used by the individuals within a city between 12:00 and 18:00 Distance traveled: low granularity of BTS towers in certain parts of the cities implied that distances introduced a lot of noise We build the mobility distributions with users that have at least five calls during both the baseline andhe earthquake periods.
    • Conclusions Three stages: (1) five minutes after the earthquake, consists of one spike of high activity characterized by a large number of short calls (2) lasts between one and two hours, is characterized by a reduced activitycompared to the baseline and also short calls (3)the last stage, that lasts around 5 hours, has a moderate increase in both call and duration volumes Increase in volumes tends to be larger as the city gets closer to the epicenter of the earthquake
    • efm@tid.es www.enriquefrias-martinez.info Thanks!