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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

  1. 1. Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics Enrique Frias-Martinez Telefonica Research, Madrid, Spain [email_address]
  2. 2. Índice <ul><li>Introducción </li></ul><ul><li>Pervasive Infrastructure </li></ul><ul><li>Hotspot Detection </li></ul><ul><li>Land Use Classification </li></ul><ul><li>Commuting Patterns </li></ul><ul><li>Conclusiones </li></ul>
  3. 3. Introducción
  4. 4. Introducción “ The 19 th century was a century of empires, the 20 th century was a century of nation states, the 21 st century will be a century of cities” Wellington E. Webb, former mayor of Denver
  5. 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. 6. Pervasive Infrastructure
  7. 7. Pervasive Infrastructure
  8. 8. Pervasive Infrastructure
  9. 9. Hotspot Detection
  10. 10. Hotspot Detection <ul><li>What is a hotspot? </li></ul><ul><ul><li>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 . </li></ul></ul><ul><li>Interesting for urban planning, emergency relief, public health, context-aware services </li></ul><ul><li>Approach </li></ul><ul><ul><li>Greedy clustering algorithm seeded with local maxima </li></ul></ul><ul><ul><li>Hotspots based on activity or on number of people. </li></ul></ul>
  11. 11. Hotspot Detection <ul><li>Data: </li></ul><ul><ul><li>CDR from Mexico for a period of 4 months. </li></ul></ul><ul><li>Output: </li></ul><ul><ul><li>At a national level: cities. At an urban level: city blocks. Evolution of dense areas for urban planning. </li></ul></ul>
  12. 12. Hotspot Detection
  13. 13. Hotspot Detection Weekdays Morning Weekdays Afternoon Weekdays Evening Weekdays Night
  14. 14. Land Use Classification
  15. 15. Land Use Classification <ul><li>Aggregate and clean data for each BTS. </li></ul><ul><ul><li>Obtain signature of each BTS (total number of calls every hour: 24 hours average week day and 24 hours average weekend day) </li></ul></ul><ul><ul><li>BTS based Voronoi gives the tessellation for land classification. </li></ul></ul><ul><ul><li>Automatic Identification of clusters with similar behaviour that maximize the compactness of the groups identified. </li></ul></ul>
  16. 16. Land Use Classification
  17. 17. Land Use Classification <ul><li>Industrial Parks / Office Areas </li></ul>
  18. 18. Land Use Classification <ul><li>Commercial - Residential </li></ul>
  19. 19. Land Use Classification <ul><li>Night Life Areas </li></ul>
  20. 20. Land Use Classification <ul><li>Weekend Activities </li></ul>
  21. 21. Land Use Classification <ul><li>Residential </li></ul>
  22. 22. Commuting Patterns
  23. 23. Commuting Patterns
  24. 24. Commuting Patterns
  25. 25. Conclusions
  26. 26. Conclusiones <ul><li>Traditional approaches are costly and based on questionnaires. </li></ul><ul><li>Urban Dynamics can be modelled using pervasive infrastructures </li></ul><ul><li>Reduction in cost, increment of the flexibility </li></ul><ul><li>Possibility of real-time modelling </li></ul><ul><li>Personalized studies (elder, young, tourists…) </li></ul>

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