This document summarizes research on detecting emergency events using mobile phone data from Cote d'Ivoire. A Markov modulated Poisson process model is used to detect anomalous hourly activity patterns from time series data of calls between 970 cell towers over 7 weeks. Preliminary results show the model detected 8 of 19 known emergency events and 8 of 11 non-emergency events. Future work could analyze how events propagate by studying mobility and activity patterns during dissemination. The research aims to help governments and organizations respond faster to security incidents by predicting emergencies from mobile phone usage data.

1. Didem Gündoğdu
16 September 2016
Emergency Event Detection
Using Mobile Phone Data
Symposium on Big Data and Human
Development, Oxford, September 2016

2. 2010 Post-Election Crisis in Cote d’Ivoire

3. 3
600.000 Displaced people
3.000 Civilian dead
More than

4. Prob.Stmt.
Research Question
• Where is the anomalous event?
• What time?
• What type of event?
• Social
• Emergency
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ConclusionEvaluationMethodologyProb.Def.Background
}Event Detection
( Mobile phone
usage activity )

5. Prob.Stmt.
How?
• Data -> Mobile Phone Dataset
• Data for Development (D4D) - Ivory Coast (Whole
country)
• Data -> Validation
• United Nations Security Reports and newspapers
• Methodology
• Markov modulated Poisson Process
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EvaluationMethodologyProb.Def.Background Conclusion

6. Prob.Stmt.
Call Detail Records (CDR)
• Collected for billing issues by mobile phone operators
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EvaluationMethodologyProb.Def.Background
TimeStamp
Originating
Cell Tower
Terminating
Cell Tower
Number of
VoiceCall
Duration
(sec) Voice
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
Conclusion

7. Prob.Stmt.
• Backward analysis, knowing an anomaly and exploit. [1]
• Aggregated daily anomalies; coarse. [2]
• Track individual change in behaviour; computational cost. [2, 3, 4]
• Supervised learning methods; not adaptable. [5, 6]
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EvaluationMethodologyProb.Def.Background
[1] L. Gao, C. Song, Z. Gao, A.-L. Barabási, J. P. Bagrow, and D. Wang. Quantifying information flow during emergencies. Scientific
reports, 4, 2014.
[2] Dobra, N. E. Williams, and N. Eagle. Spatiotemporal detection of unusual human population behavior using mobile phone data. PLoS
ONE, 2015.
[3] L. Akoglu and C. Faloutsos. Event detection in time series of mobile communication graphs. In Army Science Conference, 2010.
[4] 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.
[5] 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.
[6] 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.
Current Works
Conclusion

8. Prob.Stmt.
Novelty
• 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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EvaluationMethodologyProb.Def.Background Conclusion

9. Problem Definition : Spatial Behavioural Understanding from Time Series
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EvaluationMethodologyProb.Def.Background
• 970 Antennae
• 7 x 24 time slice
• 7 Weeks
Conclusion

10. Example: Weekly data from a cell tower
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EvaluationMethodologyProb.Def.Background Conclusion

11. MMPP Model for detecting time varying events
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Taken from: Adaptive Event Detection
with Time–Varying Poisson Processes, Ihler et al.
EvaluationMethodologyProb.Def.Background Conclusion

12. Ground Truth Data from United Nations, News…
Date
Incident
Locations
Subprefec
Subprefec
Name
Antennae
4. Jan.2012 Peite Guiglio 237 Guiglou 521 524
4. Jan.2012
Béoumi near
Bouaké 29 BEOUMI 1119 186
5.Jan.2012 Dobia 150 ISSIA 555 556
6.Jan.2012
Toa Zeo near
Duékoué 165 Duékoué 426 884
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EvaluationMethodologyProb.Def.Background Conclusion

13. Preliminary Results Summary
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Baseline MMPP
Emergency
Events
8/19 15/19
Non-Emergency
Events
7/11 8/11
EvaluationMethodologyProb.Def.Background Conclusion
• 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.

14. Prob.Stmt.
Lessons Learned
• 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
country scale.
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EvaluationMethodologyProb.Def.Background Conclusion

15. Prob.Stmt.
Future Work
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}Event Propagation
• Where is it spreading?
• What type of event?
( Mobility & Activity)
EvaluationMethodologyProb.Def.Background Conclusion

16. Prob.Stmt.
Conclusions
• 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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EvaluationMethodologyProb.Def.Background Conclusion

17. –Didem Gündoğdu
gundogdu@fbk.eu
“Thank You.”