Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics Enrique Frias‐Martinez Telefonica Research, Madrid, Spain [email protected]
Jul 06, 2015
Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics
Enrique Frias‐MartinezTelefonica
Research, Madrid, [email protected]
Índice
• Introducción• Pervasive
Infrastructure
• Hotspot
Detection
• Land Use Classification• Commuting
Patterns
• Conclusiones
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
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
Pervasive
Infrastructure
1
Cell Phone Netw 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.
Pervasive Infrastructure
1
CDR dataset
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
Subscribers sample
Cell catalogue
Mobility algorithms
2233445566|15/02/2008|2233445567|15/01/2008|2233445568|15/07/2008|25/07/20102233445569|15/09/2008|
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.
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.
Hotspot
Detection
Weekdays Morning Weekdays Afternoon
Weekdays Evening Weekdays Night
Land Use Classification
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.
Land Use Classification
1
Re pre se nt a t ions
Activity signature vectors are built: each component contains the number of managed calls by the BTS in 5-minute intervals.
Land Use Classification
• Industrial Parks / Office Areas
Commuting Patterns
Conclusions
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