Leveraging mobile network big data for urban planning

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How mobile network big data can be used to understand land usage in Sri Lanka

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Leveraging mobile network big data for urban planning

  1. 1. Leveraging  mobile  network  big  data     for  urban  planning   Kaushalya  Madhawa,  LIRNEasia   Responsible  use  of  mobile  meta-­‐data  to  support  public  purposes   Jetwing  Lagoon,  Negombo   08  August  2014   This  work  was  carried  out  with  the  aid  of  a  grant  from  the  InternaHonal  Development  Research  Centre,  OMawa,  Canada.    
  2. 2. Some  urban  planning  challenges   •  Understanding  ciHzen’s  actual  use  of  urban   environments   •  Monitoring  urban  evoluHon  over  Hme   •  Understanding  reasons  why  people   congregate  at  different  locaHons   •  Assessing  the  impact  of  development   acHviHes     1
  3. 3. Some  urban  planning  challenges   •  Understanding  ciHzen’s  actual  use  of  urban   environments   •  Monitoring  urban  evoluHon  over  Hme   •  Understanding  reasons  why  people   congregate  at  different  locaHons   •  Assessing  the  impact  of  development   acHviHes     2
  4. 4. Can  we  use  mobile  network  data  to   understand  land  use?   •  People  leave  digital  traces  when  they  use   communicaHon  devices.   •  Mobile  communicaHon  paMerns  at  different   locaHons  can  be  leveraged  to  classify  them  into  land-­‐ use  categories.     3
  5. 5. User  signatures  at  two  different   base  staHons   4 Base station 1 Base station 2
  6. 6. Methodology   •  Diurnal  paMern  of  users  is  profiled  at  different   base  staHons  during  weekdays  and  weekends   •  Time  series  of  each  base  staHon  is  normalized   to  a  (0-­‐1)  range     •  Euclidean  distance  between  two  Hme  series  is   used  to  cluster  base  staHons  in  an   unsupervised  manner  using  k-­‐means   algorithm       5
  7. 7. DistribuHon  of  base  staHons  in   Colombo  district   6
  8. 8. A  closer  look  at  base  staHons  in  each   cluster   7 Cluster 1 Cluster 2
  9. 9. What  does  this  reveal?   •  Cluster  1  exhibits  paMerns   consistent  with  a   commercial  area     •  Cluster  2  exhibits  paMerns   consistent  with  less   commercial  and  more   residenHal  area  (or   possibly  mixed)       8
  10. 10. The  Central  Business  District  (CBD)  seems  to  have   expanded   9
  11. 11. North  Colombo  is  Colombo’s  inner  city   10 Photo: ©Senanayake Bandara - Panoramio
  12. 12. Seethawaka  Export  Processing   Zone  (EPZ)   11 Seethawaka EPZ Photo ©Senanayaka Bandara - Panoramio
  13. 13. Future  work   •  InvesHgate  more  fine  grained  land  use   categorizaHon     •  Use  this  methodology  to  monitor  the  evoluHon  of   urbanizaHon  of  a  region     12

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