URBAN ANALYSIS FOR THE XXI CENTURY: USING PERVASIVE INFRASTRUCTURES FOR MODELING URBAN DYNAMICS
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Transcript of URBAN ANALYSIS FOR THE XXI CENTURY: USING PERVASIVE INFRASTRUCTURES FOR MODELING URBAN DYNAMICS
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Urban Analysis for the XXI Century: Using Pervasive Infrastructures for Modeling Urban Dynamics
Enrique Frias‐MartinezTelefonica
Research, Madrid, [email protected]
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Índice
• Introducción• Pervasive
Infrastructure
• Hotspot
Detection
• Land Use Classification• Commuting
Patterns
• Conclusiones
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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
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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
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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.
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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|
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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.
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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.
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Hotspot
Detection
Weekdays Morning Weekdays Afternoon
Weekdays Evening Weekdays Night
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Land Use Classification
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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.
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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.
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Land Use Classification
• Industrial Parks / Office Areas
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Commuting Patterns
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Conclusions
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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