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 Classification
R 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.
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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