Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics

1. Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics Enrique Frias-Martinez Telefonica Research, Madrid, Spain [email_address]

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. Introducción

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. 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

9. Hotspot Detection

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. 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. Hotspot Detection

13. Hotspot Detection Weekdays Morning Weekdays Afternoon Weekdays Evening Weekdays Night

14. Land Use Classification

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. Land Use Classification

17. Land Use Classification <ul><li>Industrial Parks / Office Areas </li></ul>

18. Land Use Classification <ul><li>Commercial - Residential </li></ul>

19. Land Use Classification <ul><li>Night Life Areas </li></ul>

20. Land Use Classification <ul><li>Weekend Activities </li></ul>

21. Land Use Classification <ul><li>Residential </li></ul>

22. Commuting Patterns

25. Conclusions

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>