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Event detection using mobile phone data

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Emergency event detection using mobile phone data, Oxford Big Data symposium, September 2016

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Event detection using mobile phone data

  1. 1. Didem Gündoğdu 16 September 2016 Emergency Event Detection Using Mobile Phone Data Symposium on Big Data and Human Development, Oxford, September 2016
  2. 2. 2010 Post-Election Crisis in Cote d’Ivoire
  3. 3. 3 600.000 Displaced people 3.000 Civilian dead More than
  4. 4. Prob.Stmt. Research Question • Where is the anomalous event? • What time? • What type of event? • Social • Emergency 3 / 15 ConclusionEvaluationMethodologyProb.Def.Background }Event Detection ( Mobile phone usage activity )
  5. 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 4 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  6. 6. Prob.Stmt. Call Detail Records (CDR) • Collected for billing issues by mobile phone operators 5 / 15 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. 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] 6 / 15 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. 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 7 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  9. 9. Problem Definition : Spatial Behavioural Understanding from Time Series 8 / 15 EvaluationMethodologyProb.Def.Background • 970 Antennae • 7 x 24 time slice • 7 Weeks Conclusion
  10. 10. Example: Weekly data from a cell tower 9 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  11. 11. MMPP Model for detecting time varying events 10 / 15 Taken from: Adaptive Event Detection with Time–Varying Poisson Processes, Ihler et al. EvaluationMethodologyProb.Def.Background Conclusion
  12. 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 11 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  13. 13. Preliminary Results Summary 12 / 15 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. 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. 13 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  15. 15. Prob.Stmt. Future Work 14 / 15 }Event Propagation • Where is it spreading? • What type of event? ( Mobility & Activity) EvaluationMethodologyProb.Def.Background Conclusion
  16. 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. 15 / 15 EvaluationMethodologyProb.Def.Background Conclusion
  17. 17. –Didem Gündoğdu gundogdu@fbk.eu “Thank You.”

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