600.000 Displaced people
3.000 Civilian dead
• Where is the anomalous event?
• What time?
• What type of event?
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( Mobile phone
usage activity )
• Data -> Mobile Phone Dataset
• Data for Development (D4D) - Ivory Coast (Whole
• Data -> Validation
• United Nations Security Reports and newspapers
• Markov modulated Poisson Process
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Call Detail Records (CDR)
• Collected for billing issues by mobile phone operators
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2012-04-28 23:00:00 1236 786 2 96
2012-04-28 23:00:00 1236 804 1 539
2012-04-28 23:00:00 1236 867 3 1778
• Backward analysis, knowing an anomaly and exploit. 
• Aggregated daily anomalies; coarse. 
• Track individual change in behaviour; computational cost. [2, 3, 4]
• Supervised learning methods; not adaptable. [5, 6]
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 L. Gao, C. Song, Z. Gao, A.-L. Barabási, J. P. Bagrow, and D. Wang. Quantifying information ﬂow during emergencies. Scientiﬁc
reports, 4, 2014.
 Dobra, N. E. Williams, and N. Eagle. Spatiotemporal detection of unusual human population behavior using mobile phone data. PLoS
 L. Akoglu and C. Faloutsos. Event detection in time series of mobile communication graphs. In Army Science Conference, 2010.
 V. A. Traag, A. Browet, F. Calabrese, and F. Morlot. Social event detection in massive mobile phone data using probabilistic location
inference. In IEEE Third Int. Conf. on Social Computing, 2011.
 M. Faulkner, M. Olson, R. Chandy, J. Krause, K. M. Chandy, and A. Krause. The next big one: Detecting earthquakes and other rare
events from community-based sensors. In 10th International Conference on Information Processing in Sensor Networks (IPSN), 2011.
 P. Paraskevopoulos, T. Dinh, Z. Dashdorj, T. Palpanas, and L. Seraﬁni. Identiﬁcation and characterization of human behavior patterns
from mobile phone data. In International Conference the Analysis of Mobile Phone Datasets (NetMob 2013), Special Session on the Data
for Development (D4D) Challenge, 2013.
• Hourly prediction of the anomalous events in spatial data
• Detecting the signature of the event type from the
dissemination velocity and direction
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Problem Deﬁnition : Spatial Behavioural Understanding from Time Series
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• 970 Antennae
• 7 x 24 time slice
• 7 Weeks
Example: Weekly data from a cell tower
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MMPP Model for detecting time varying events
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Taken from: Adaptive Event Detection
with Time–Varying Poisson Processes, Ihler et al.
Ground Truth Data from United Nations, News…
4. Jan.2012 Peite Guiglio 237 Guiglou 521 524
Bouaké 29 BEOUMI 1119 186
5.Jan.2012 Dobia 150 ISSIA 555 556
Toa Zeo near
Duékoué 165 Duékoué 426 884
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Preliminary Results Summary
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• Gundogdu, D., Incel, O. D., Salah, A. A., & Lepri, B. (2016).
Countrywide arrhythmia: emergency event detection using mobile
phone data. EPJ Data Science, 5(1), 25.
• Understand the data. ( Visualise, have background
information for the analysed period for that country e.g.
there was a civil war in CIV ).
• Data pre-processing is important.
• Missing and/or not reliable periods (e.g. 37 days
western part of CIV very low call volume + 5 days
deleted for keeping weekly periodicity ).
• Evaluating the model: Obtaining ground truth for events in
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• Where is it spreading?
• What type of event?
( Mobility & Activity)
• Early detection of security incidents can be predicted
through mobile phone data.
• Temporal dissemination of the events can be predicted.
• Governments, international organisations can beneﬁt to
create secure cities for the human well being.
• Another implication can be the veriﬁcation of
misinformation dissemination in social networks.
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