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Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

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- 1. Measuring and Predicting Departures from Routine in Human Mobility Dirk Gorissen | @elazungu PyData London - 23 February 2014
- 2. Me? www.rse.ac.uk
- 3. Human Mobility - Credits University of Southampton James McInerney Sebastian Stein Alex Rogers Nick Jennings BAE Systems ATC Dave Nicholson Reference: J. McInerney, S. Stein, A. Rogers, and N. R. Jennings (2013). Breaking the habit: measuring and predicting departures from routine in individual human mobility. Pervasive and Mobile Computing, 9, (6), 808-822. Submitted KDD paper
- 4. Beijing Taxi rides Nicholas Jing Yuan (Microsoft Research)
- 5. Human Mobility London in Motion - Jay Gordon (MIT)
- 6. Human Mobility: Inference Functional Regions of a city Nicholas Jing Yuan (Microsoft Research)
- 7. Human Mobility: Inference Jay Gordon (MIT)
- 8. Human Mobility: Inference Cross cuts many fields: sociology, physics, network theory, computer science, epidemiology, … © PNAS © MIT
- 9. Project InMind Project InMind announced on 12 Feb $10m Yahoo-CMU collaboration on predicting human needs and intentions
- 10. Human Mobility Human mobility is highly predictable Average predictability in the next hour is 93% [Song 2010] Distance little or no impact High degree of spatial and temporal regularity Spatial: centered around a small number of base locations Temporal: e.g., workweek / weekend “…we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.”
- 11. Temporal Regularity [Herder 2012] [Song 2010]
- 12. Spatial Regularity [Herder 2012] [Song 2010]
- 13. Breaking the Habit However, regular patterns not the full story travelling to another city on a weekend break or while on sick leave Breaks in regular patterns signal potentially interesting events Being in an unfamiliar place at an unfamiliar time requires extra context aware assistance E.g., higher demand for map & recommendation apps, mobile advertising more relevant, … Predict future departures from routine?
- 14. Applications Optimize public transport Insight into social behaviour Spread of disease (Predictive) Recommender systems Based on user habits (e.g., Google Now, Sherpa) Context aware advertising Crime investigation Urban planning … Obvious privacy & de-anonymization concerns -> Eric Drass’ talk
- 15. Human Mobility: Inference London riots “commute”
- 16. Modeling Mobility Entropy measures typically used to determine regularity in fixed time slots Well understood measures, wide applicability Break down when considering prediction or higher level structure Model based Can consider different types of structure in mobility (i.e., sequential and temporal) Can deal with heterogeneous data sources Allows incorporation of domain knowledge (e.g., calendar information) Can build extensions that deal with trust Allows for prediction Bayesian approach distribution over locations enables use as a generative model
- 17. Bayes Theorem
- 18. Bayesian Networks Bottom up: Grass is wet, what is the most likely cause? Top down: Its cloudy, what is the probability the grass is wet?
- 19. Hidden Markov Model Simple Dynamic Bayesian Network Shaded nodes are observed
- 20. Probabilistic Models Model can be run forwards or backwards Forwards (generation): parameters -> data E.g., use a distribution over word pair frequencies to generate sentences
- 21. Probabilistic Models Model can be run backwards Backwards (Inference): data -> parameters
- 22. Building the model We want to model departures from routine Assume assignment of a person to a hidden location at all time steps (even when not observed) Discrete latent locations Correspond to “points of interest” e.g., home, work, gym, train station, friend's house
- 23. Latent Locations Augment with temporal structure Temporal and periodic assumption to behaviour e.g., tend to be home each night at 1am e.g., often in shopping district on Sat afternoon
- 24. Add Sequential Structure Added first-order Markov dynamics e.g., usually go home after work can extend to more complex sequential structures
- 25. Add Departure from Routine zn = 0 : routine zn = 1 : departure from routine
- 26. Sensors Noisy sensors, e.g., cell tower observations observed: latitude/longitude inferred: variance (of locations)
- 27. Reported Variance E.g., GPS observed: latitude/longitude, variance
- 28. Trustworthiness E.g., Eyewitness observed: latitude/longitude, reported variance inferred: trustworthiness of observation single latent trust value(per time step & source)
- 29. Full Model
- 30. Inference
- 31. Inference is Challenging Exact inference intractable Can perform approximate inference using: Expectation maximisation algorithm Fast But point estimates of parameters Gibbs sampling, or other Markov chain Monte Carlo Full distributions (converges to exact) But slow Variational approximation Full distributions based on induced factorisation of model And fast
- 32. Variational Approximation Advantages Straightforward parallelisation by user Months of mobility data ~ hours Updating previous day's parameters ~ minutes Variational approximation amenable to fully online inference M. Hoffman, D. Blei, C. Wang, and J. Paisley. Stochastic variational inference. arXiv:1206.7051, 2012
- 33. Model enables Inference location departures from routine noise characteristics of observations trust characteristics of sensors Exploration/summarisation parameters have intuitive interpretations Prediction Future mobility (given time context) Future departures from routine
- 34. Performance Nokia Dataset (GPS only) [McInerney 2012]
- 35. Performance
- 36. Performance Synthetic dataset with heterogeneous, untrustworthy observations. Parameters of generating model learned from OpenPaths dataset
- 37. Performance
- 38. Implementation Backend inference and data processing code all python numpy scipy matplotlib UI to explore model predictions & sanity check flask d3.js leaflet.js kockout.js Future Gensim, pymc, bayespy, … Probabilistic programming
- 39. Map View: Observed
- 40. Map View: Inferred
- 41. Departures from Routine: Temporal
- 42. Departures from Routine: Spatial
- 43. Departures from Routine: Combined
- 44. Departures from Routine
- 45. Conclusion & Future Work Summary Novel model for learning and predicting departures from routine Limitations Need better ground truth for validation Finding ways to make the model explain why each departure from routine happened. Needs more data (e.g., from people who know each other, using weather data, app usage data, …). Future Work Incorporating more advanced sequential structure into the model e.g., hidden semi-Markov model, sequence memoizer Supervised learning of what “interesting" mobility looks like More data sources Online inference Taxi drivers
- 46. Questions? Thank you. dirk.gorissen@baesystems.com | @elazungu Reference: J. McInerney, S. Stein, A. Rogers, and N. R. Jennings (2013). Breaking the habit: measuring and predicting departures from routine in individual human mobility. Pervasive and Mobile Computing, 9, (6), 808-822.

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