Understanding Urban Dynamics with Community Behaviour Modelling ! Future Communities Future Communities ! Afra Mashhadi ! Network Applications and Devices !
Understanding Urban Dynamics with Community Behaviour Modelling ! Infrastructural sensors ! Mobile Communication ! Crowds ! Afra Mashhadi ! Network Applications and Devices !
! Citizen Behaviour Modelling with Transportation Data ! Can we leverage Transportation Data to understand the urban dynamics Challenge ! better, e.g., who, when, (and how often) go to a particular area and why? ! Better Crowd Management, Improved Infrastructure Planning, Adaptive Impact ! Transport Scheduling. ! Dataset ! Results ! Location Profiling Models with Different Ranking Algorithms for ! " - Spatio-Temporal Travel Trajectory with Activity Inference. ! " - Significant Place Detection (Home, Work, Favourite Dining etc.) ! " - Citizen Behaviour Analysis : Diversity, Introversion, etc. ! Given ONLY transportation data, our model can infer the profile of the location and the purpose of the travel. !
Understanding Urban Crowd-sourcing ! Challenge ! Can we rely on the quality of the crowd-sourcing to deal with accuracy and coverage issues? ! Enabling crowd-sourcing for practical and important problems with Impact ! massive social impact. ! 6 YEARS OF CROWDSOURCING 6 Dataset ! CONTRIBUTIONS ! 35 COUNT 35 OUNTRIES OpenStreetMap ! Census ! Results ! A model model that can predict the growth and accuracy of crowd-sourced information. ! " - Accuracy is high and sometimes higher than proprietary data. ! " - Coverage is low and non-uniformly distributed. ! " - Participation is highly affected by the cultural factors. " " " "! Given a set of users and profile of an area (e.g., population) we can predict the future crowd-sourcing participation in that area. !
Understanding Socio-Economic Structure through Communication Patterns ! Challenge ! Can we leverage mobile communication meta data to understand a community better? Can we model their level of socio-economic? ! Radical minimization of the cost and complexity of traditional methods Impact ! (household surveys, Census) ! 1 YEAR aggregated CDR data ! 1 Dataset ! Ivor ory coas coast Global-Pulse ! Orange Telecom ! Results ! A set of models that can infer the socio-economic level of areas in fine granularity ! " -Based on models from physics ( gravity ) and economy ( diversity , introversion ). " " " "! With meta communication data we can infer the state and change of socio-economic level of a community in fine granularity that was not possible before. !
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