Understanding Urban Dynamics with Community Behaviour Modelling Understanding our cities and citizens Dr. Afra Mashhadi Bell Laboratories Alcatel-Lucent January 2015
The world is in the midst of an immense population shift – 50% living in cities; 1.2 billion people to live in urban areas (2050)
The world is in the midst of an immense population shift – 50% living in cities; 1.2 billion people to live in urban areas (2050) How to keep up with changes in the urban cities?
Understanding Urban Dynamics with Community Behaviour Modelling Crowd-sourcing: leveraging the power of citizens to contribute dynamic information about the city. Directly from the citizens through explicit reporting.
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 Points pf Interests Dataset (POIs) CROWDSOURCED in 35 COUNTRIES Census OpenStreetMap Results • Accuracy is high and sometimes higher than proprietary data. • Coverage is low and non-uniformly distributed. • Coverage is impacted by socio-economic factors. • 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 Urban Dynamics with Community Behaviour Modelling Crowd-Sensing through infrastructure: Leveraging the citizens footprint through infrastructural sensors. E.g., Oyster cards.
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 Dynamics with Community Behaviour Modelling Learning more about the urban cities through other sources of data. E.g., Call Data Records. Mobile Communication
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 minimisation of the cost and complexity of traditional methods Impact (household surveys, Census) 1 YEAR aggregated CDR data Dataset Ivory coast Orange Telecom Global-Pulse UN research lab A set of models based on physics ( gravity ) and Results economy ( diversity , introversion ) that can infer the socio-economic level of areas in fine granularity 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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