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Revisiting the Generality of the Rank-based Human Mobility Model

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Slides from the talk given at PURBA workshop at Ubicomp 2013

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Revisiting the Generality of the Rank-based Human Mobility Model

  1. 1. Revisiting the Generality of the Rank­based  Human Mobility Model Darshan Santani and Daniel Gatica­Perez Idiap Research Institute and EPFL 8 Sept 2013 PURBA @ Ubicomp 2013
  2. 2. 2 Motivation ● Human mobility models area of active research and debate! ● CDRs to infer aggregated mobility patterns [Song10] ● Human mobility patters using LBSN [Noulas12] ● Urban planning and management ● Disease Contagion ● How will be a pathogen, such as influenza, driven by physical proximity,  spread through urban population? ● A recent work has recently showed that rank­distance distribution  for human mobility follows a “universal” power­law. [Noulas12] [Song10] Limits of predictability in human mobility. Science [Noulas12] A tale of many cities: universal patterns in human urban mobility. PloS one 
  3. 3. 3 Research Questions RQ1: Does the rank­distance follow a power­law like  distribution, as suggested in earlier research? RQ2:  If  it  does  not  follow  a  power­law,  which  other  heavy­ tailed distributions can better describe place transitions?
  4. 4. 4 Foursquare 3 billion check­ins, 30 million users Large­scale access to a wider and diverse user­base Check­in
  5. 5. 5 Swiss and NYC Check­in Dataset NYC dataset generously provided by Texas A&M [Cheng11] Swiss data collection at Idiap since December 2011 [Cheng11] Exploring millions of footprints in location sharing services. ICWSM
  6. 6. 6
  7. 7. 7 Zurich
  8. 8. 8 Existing Human Mobility Models ● Distance­based Model ● Rank­based Model s d
  9. 9. 9 Existing Human Mobility Models ● Distance­based Model ● Rank­based Model Rank­based  model  is  inspired  by  Stouffer’s  theory  of  intervening  opportunities, which states that the probability of traveling from source to  destination is directly proportional to the number of opportunities closer  to source than destination. [Stouffer40] [Stouffer40] Intervening opportunities: a theory relating mobility and distance. American  Sociological
  10. 10. 10 Alternative Models ● Log Normal  ● Power Law with Exponential Cutoff
  11. 11. 11 Visual Inspection
  12. 12. 12 Visual Inspection
  13. 13. 13 Power Law Hypothesis Non Significant  (KS statistics) Our statistical analysis follows the seminal work by [Clauset09] Estimating the Scaling Exponent [Clauset09] Power­law distributions in empirical data. SIAM Review
  14. 14. 14 Power Law Hypothesis Non Significant  (KS statistics) Our statistical analysis follows the seminal work by [Clauset09] Estimating the Lower Bound Parameter [Clauset09] Power­law distributions in empirical data. SIAM Review
  15. 15. 15 Alternative Hypothesis Significant (Likelihood Ratio Test) Significant (Likelihood Ratio Test)
  16. 16. 16 Rank Definition ● Rank­1 s d
  17. 17. 17 Rank Definition ● Rank­1 ● Rank­0 s d
  18. 18. 18 Rank Definition ● Rank­1 ● Rank­0
  19. 19. 19 Visual Inspection Rank­1 Rank­0
  20. 20. 20 Visual Inspection Rank­1 Rank­0
  21. 21. 21 Conclusions RQ1: Does the rank­distance follow a power­law? ● We have not observed that the rank­distance follows a pure power law in  our data. Additional studies with other cities seem necessary RQ2:  If  it  does  not  follow  a  power­law,  which  other  heavy­tailed  distributions can better describe place transitions? ● Human transitions are better explained using a log­normal and power­ law with exponential cutoff model We  do  not  claim  a  cutoff  power­law  model  as  the  “universal”  mobility model to explain human transitions.
  22. 22. 22 Q & A Email: dsantani@idiap.ch Twitter: @SabMayaHai

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