event detection using mobile phone data
TRANSCRIPT
Didem Gündoğdu 16 September 2016
Emergency Event Detection Using Mobile Phone Data
Symposium on Big Data and Human Development, Oxford, September 2016
2010 Post-Election Crisis in Cote d’Ivoire
3
600.000 Displaced people
3.000 Civilian deadMore than
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 )
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 4 / 15
EvaluationMethodologyProb.Def.Background Conclusion
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
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
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
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
Example: Weekly data from a cell tower
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EvaluationMethodologyProb.Def.Background Conclusion
MMPP Model for detecting time varying events
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Taken from: Adaptive Event Detectionwith Time–Varying Poisson Processes, Ihler et al.
EvaluationMethodologyProb.Def.Background Conclusion
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
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.
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
Prob.Stmt.
Future Work
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}Event Propagation• Where is it spreading?
• What type of event? ( Mobility & Activity)
EvaluationMethodologyProb.Def.Background Conclusion
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
