revisiting the generality of the rank-based human mobility model
DESCRIPTION
Slides from the talk given at PURBA-13 workshop at Ubicomp 2013TRANSCRIPT
Revisiting the Generality of the Rankbased Human Mobility Model
Darshan Santani and Daniel GaticaPerezIdiap Research Institute and EPFL
8 Sept 2013
PURBA @ Ubicomp 2013
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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
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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 heavytailed distributions can better describe place transitions?
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Foursquare
3 billion checkins, 30 million users
Largescale access to a wider and diverse userbase
Checkin
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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
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Zurich
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Existing Human Mobility Models
● Distancebased Model
● Rankbased Model
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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
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Alternative Models
● Log Normal
● Power Law with Exponential Cutoff
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Visual Inspection
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Visual Inspection
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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
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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
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Alternative Hypothesis
Significant(Likelihood Ratio Test)
Significant(Likelihood Ratio Test)
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Rank Definition
● Rank1
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Rank Definition
● Rank1
● Rank0
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Rank Definition
● Rank1
● Rank0
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Visual Inspection
Rank1 Rank0
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Visual Inspection
Rank1 Rank0
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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 powerlaw with exponential cutoff model
We do not claim a cutoff powerlaw model as the “universal” mobility model to explain human transitions.
