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Automated Land Use Identification using Call Detail Records

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Automated Land Use Identification using Call Detail Records

  1. 1. <ul><li>Automated Land Use Identification
  2. 2. using Cell-phone Records </li></ul><ul>June 28, 2011 </ul><ul>Víctor Soto & Enrique Frías-Martínez </ul><ul>TELEFÓNICA I+D </ul>
  3. 3. <ul></ul><ul>Introduction </ul><ul>01 </ul><ul>Telefónica I+D </ul>
  4. 4. <ul></ul>
  5. 5. <ul>Goal: Land use of urban areas using Call Details Records. <li>Study Evolution, Evaluate Urban Zooning </li></ul><ul></ul>
  6. 6. <ul></ul><ul>Preliminaries </ul><ul>02 </ul><ul>Telefónica I+D </ul>
  7. 7. <ul>Cell Phone Network </ul><ul></ul>
  8. 8. <ul>CDR dataset </ul><ul><li>Our Dataset </li></ul><ul><ul><li>1 month of phone call interactions.
  9. 9. 1100 Base Transceiver Stations.
  10. 10. Each CDR contains: </li><ul><li>phone Source | phone Destiny | bts Source | bts Destiny | DD/MM/YYYY | hh:mm:ss | d </li></ul><li>Phone number are encrypted to anonymize user identities. </li></ul></ul><ul></ul>
  11. 11. <ul></ul><ul>Activity Signature </ul><ul>03 </ul><ul>Telefónica I+D </ul>
  12. 12. <ul>Representations </ul><ul></ul>
  13. 13. <ul></ul><ul>Land Use Identification </ul><ul>04 </ul><ul>Telefónica I+D </ul>
  14. 14. <ul></ul>
  15. 15. <ul>Methodology (I) </ul><ul><li>K-means was applied for k={3,4,...,8} for the three representations.
  16. 16. Validity index: maximizes the minimum inter-cluster distance and minimizes the average intra-cluster distance. </li></ul><ul><li>DTW also used but did not return good results. </li></ul><ul></ul>
  17. 17. <ul></ul>
  18. 18. <ul></ul><ul>Validation </ul><ul>06 </ul><ul>Telefónica I+D </ul>
  19. 19. <ul></ul>
  20. 20. <ul>Cluster 1: Industrial & Office </ul><ul></ul>
  21. 21. <ul>Cluster 2: Business & Commercial </ul><ul></ul>
  22. 22. <ul>Cluster 3: Nightlife </ul><ul></ul>
  23. 23. <ul>Cluster 4: Leisure </ul><ul></ul>
  24. 24. <ul>Cluster 5: Residential </ul><ul></ul>
  25. 25. <ul></ul><ul>Classification </ul><ul>06 </ul><ul>Telefónica I+D </ul>
  26. 26. Classification <ul><li>Cluster representatives can be used as class labels.
  27. 27. Proposed classification scheme: the class label that minimize the euclidean distance between a BTS signature and itself is assigned as the class of the area.
  28. 28. We validate the classification against the city of Barcelona: </li><ul><li>900 BTS towers.
  29. 29. Extension 100 km 2 . </li></ul></ul>
  30. 30. Classification: BCN
  31. 31. <ul></ul><ul>Conclusions & Future Work </ul><ul>07 </ul><ul>Telefónica I+D </ul>
  32. 32. <ul><li>Specific uses for City Halls. </li></ul><ul></ul>
  33. 33. “ Robust Land Use Characterization of Urban Landscapes using Cell Phone Data” V. Soto, E. Frias-Martinez The First Workshop on Pervasive Urban Applications (PURBA), in conjunction with the Ninth International Conference on Pervasive Computing in San Francisco, CA, USA on June 12-15, 2011. www.enriquefrias-martinez.info/publications [email_address]

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