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URBAN ANALYSIS FOR THE XXI CENTURY: USING PERVASIVE INFRASTRUCTURES FOR MODELING URBAN DYNAMICS
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URBAN ANALYSIS FOR THE XXI CENTURY: USING PERVASIVE INFRASTRUCTURES FOR MODELING URBAN DYNAMICS

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  • 1. Urban Analysis for the XXI Century: Using Pervasive Infrastructures for  Modeling Urban Dynamics Enrique Frias‐Martinez Telefonica Research, Madrid, Spain efm@tid.es
  • 2. Índice• Introducción• Pervasive Infrastructure• Hotspot Detection• Land Use Classification• Commuting Patterns• Conclusiones
  • 3. Introducción
  • 4. Introducción “The 19th century was a century of empires, the 20th century was a century of nation states, the 21st century will be a century of cities” Wellington E. Webb, former mayor of Denver
  • 5. Introducción Digital Footprints  For the first time in human history, we have  access to large‐scale human behavioral  data at varying levels of spatial and  temporal granularities
  • 6. Pervasive Infrastructure
  • 7. Pervasive Infrastructure Ce ll Phone N e t w ork Cell Phone networks are built using Base Transceiver Stations (BTS). Each BTS will be characterized by a feature vector that describes the calling behavior area. 1
  • 8. Pervasive Infrastructure CDR da t a se t Our Dataset • 1 month of phone call interactions. • 1100 Base Transceiver Stations. • Each CDR contains: › phoneSource | phoneDestiny | btsSource | btsDestiny | DD/MM/YYYY | hh:mm:ss | d • Phone number are encrypted to anonymize user identities. Traffic M b o ility alg rith s o m Subscribers sample 2233445566|15/02/ 2008| 2233445567|15/01/ 2008| 2233445568|15/07/ 2008|25/07/2010 2233445569|15/09/ 2008| Cell catalogue 1
  • 9. Hotspot Detection
  • 10. Hotspot Detection• What is a hotspot? – In this context a hotspot is understood as a  concentration of people (or activities) over a  specific period of time and a specific geographic  area.• Interesting for urban planning, emergency relief,  public health, context‐aware services• Approach – Greedy clustering algorithm seeded with local maxima – Hotspots based on activity or on number of people.
  • 11. Hotspot Detection• Data: – CDR from Mexico for a period of 4 months. • Output:  – At a national level: cities. At an urban level: city  blocks. Evolution of dense areas for urban  planning.
  • 12. Hotspot Detection
  • 13. Hotspot Detection Weekdays Morning Weekdays Afternoon Weekdays Evening Weekdays Night
  • 14. Land Use Classification
  • 15. Land Use Classification• Aggregate and clean data for each BTS. – Obtain signature of each BTS (total number of  calls every hour: 24 hours average week day and  24 hours average weekend day) – BTS based Voronoi gives the tessellation for land  classification. – Automatic Identification of clusters with similar  behaviour that maximize the compactness of the  groups identified.
  • 16. Land Use ClassificationR e p r e s e n t a t io n s Activity signature vectors are built: each component contains the number of managed calls by the BTS in 5-minute intervals. 1
  • 17. Land Use Classification• Industrial Parks / Office Areas
  • 18. Land Use Classification• Commercial ‐ Residential
  • 19. Land Use Classification• Night Life Areas
  • 20. Land Use Classification• Weekend Activities
  • 21. Land Use Classification• Residential
  • 22. Commuting Patterns
  • 23. Commuting Patterns
  • 24. Commuting Patterns
  • 25. Conclusions
  • 26. Conclusiones• Traditional approaches are costly and based on questionnaires.• Urban Dynamics can be modelled using pervasive infrastructures• Reduction in cost, increment of the flexibility• Possibility of real‐time modelling