Measuring and Predicting
Departures from Routine
in Human Mobility
Dirk Gorissen | @elazungu
PyData London - 23 February 2...
Me?
www.rse.ac.uk
Human Mobility - Credits
 University of Southampton
 James McInerney
 Sebastian Stein
 Alex Rogers
 Nick Jennings
 B...
 Beijing Taxi rides
 Nicholas Jing Yuan (Microsoft Research)
Human Mobility
 London in Motion - Jay Gordon (MIT)
Human Mobility: Inference
 Functional Regions of a city
 Nicholas Jing Yuan (Microsoft Research)
Human Mobility: Inference
 Jay Gordon (MIT)
Human Mobility: Inference
 Cross cuts many fields: sociology, physics, network
theory, computer science, epidemiology, …
...
Project InMind
 Project InMind announced on 12 Feb
 $10m Yahoo-CMU collaboration on predicting human needs and
intentions
Human Mobility
 Human mobility is highly predictable
 Average predictability in the next hour is 93% [Song 2010]
 Dista...
Temporal Regularity
 [Herder 2012] [Song 2010]
Spatial Regularity
 [Herder 2012] [Song 2010]
Breaking the Habit
 However, regular patterns not the full story
 travelling to another city on a weekend break or while...
Applications
 Optimize public transport
 Insight into social behaviour
 Spread of disease
 (Predictive) Recommender sy...
Human Mobility: Inference
 London riots “commute”
Modeling Mobility
 Entropy measures typically used to determine regularity in
fixed time slots
 Well understood measures...
Bayes Theorem
Bayesian Networks
 Bottom up: Grass is wet, what is the most likely cause?
 Top down: Its cloudy, what is the probabilit...
Hidden Markov Model
 Simple Dynamic Bayesian Network
 Shaded nodes are observed
Probabilistic Models
 Model can be run forwards or backwards
 Forwards (generation): parameters -> data
 E.g., use a di...
Probabilistic Models
 Model can be run backwards
 Backwards (Inference): data -> parameters
Building the model
 We want to model departures from routine
 Assume assignment of a person to a hidden location
at all ...
Latent Locations
 Augment with temporal structure
 Temporal and periodic assumption to behaviour
 e.g., tend to be home...
Add Sequential Structure
 Added first-order Markov dynamics
 e.g., usually go home after work
 can extend to more compl...
Add Departure from Routine
 zn = 0 : routine
 zn = 1 : departure from routine
Sensors
 Noisy sensors, e.g., cell tower observations
 observed: latitude/longitude
 inferred: variance (of locations)
Reported Variance
 E.g., GPS
 observed: latitude/longitude, variance
Trustworthiness
 E.g., Eyewitness
 observed: latitude/longitude, reported variance
 inferred: trustworthiness of observ...
Full Model
Inference
Inference is Challenging
 Exact inference intractable
 Can perform approximate inference using:
 Expectation maximisati...
Variational Approximation
 Advantages
 Straightforward parallelisation by user
 Months of mobility data ~ hours
 Updat...
Model enables
 Inference
 location
 departures from routine
 noise characteristics of observations
 trust characteris...
Performance
 Nokia Dataset (GPS only) [McInerney 2012]
Performance
Performance
 Synthetic dataset with heterogeneous, untrustworthy
observations.
 Parameters of generating model learned f...
Performance
Implementation
 Backend inference and data processing code all python
 numpy
 scipy
 matplotlib
 UI to explore model ...
Map View: Observed
Map View: Inferred
Departures from Routine: Temporal
Departures from Routine: Spatial
Departures from Routine: Combined
Departures from Routine
Conclusion & Future Work
 Summary
 Novel model for learning and predicting departures from routine
 Limitations
 Need ...
Questions?
 Thank you.
 dirk.gorissen@baesystems.com | @elazungu
 Reference:
 J. McInerney, S. Stein, A. Rogers, and N...
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Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

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  • http://www.pnas.org/content/95/25/15145/F2.expansion.htmlhttps://cee.mit.edu/news/releases/2013/human-mobility-travel-configurations
  • http://ceur-ws.org/Vol-872/aum2012_paper_3.pdf
  • Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

    1. 1. Measuring and Predicting Departures from Routine in Human Mobility Dirk Gorissen | @elazungu PyData London - 23 February 2014
    2. 2. Me? www.rse.ac.uk
    3. 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. 4.  Beijing Taxi rides  Nicholas Jing Yuan (Microsoft Research)
    5. 5. Human Mobility  London in Motion - Jay Gordon (MIT)
    6. 6. Human Mobility: Inference  Functional Regions of a city  Nicholas Jing Yuan (Microsoft Research)
    7. 7. Human Mobility: Inference  Jay Gordon (MIT)
    8. 8. Human Mobility: Inference  Cross cuts many fields: sociology, physics, network theory, computer science, epidemiology, … © PNAS © MIT
    9. 9. Project InMind  Project InMind announced on 12 Feb  $10m Yahoo-CMU collaboration on predicting human needs and intentions
    10. 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. 11. Temporal Regularity  [Herder 2012] [Song 2010]
    12. 12. Spatial Regularity  [Herder 2012] [Song 2010]
    13. 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. 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. 15. Human Mobility: Inference  London riots “commute”
    16. 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. 17. Bayes Theorem
    18. 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. 19. Hidden Markov Model  Simple Dynamic Bayesian Network  Shaded nodes are observed
    20. 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. 21. Probabilistic Models  Model can be run backwards  Backwards (Inference): data -> parameters
    22. 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. 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. 24. Add Sequential Structure  Added first-order Markov dynamics  e.g., usually go home after work  can extend to more complex sequential structures
    25. 25. Add Departure from Routine  zn = 0 : routine  zn = 1 : departure from routine
    26. 26. Sensors  Noisy sensors, e.g., cell tower observations  observed: latitude/longitude  inferred: variance (of locations)
    27. 27. Reported Variance  E.g., GPS  observed: latitude/longitude, variance
    28. 28. Trustworthiness  E.g., Eyewitness  observed: latitude/longitude, reported variance  inferred: trustworthiness of observation  single latent trust value(per time step & source)
    29. 29. Full Model
    30. 30. Inference
    31. 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. 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. 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. 34. Performance  Nokia Dataset (GPS only) [McInerney 2012]
    35. 35. Performance
    36. 36. Performance  Synthetic dataset with heterogeneous, untrustworthy observations.  Parameters of generating model learned from OpenPaths dataset
    37. 37. Performance
    38. 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. 39. Map View: Observed
    40. 40. Map View: Inferred
    41. 41. Departures from Routine: Temporal
    42. 42. Departures from Routine: Spatial
    43. 43. Departures from Routine: Combined
    44. 44. Departures from Routine
    45. 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. 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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