Urban Analysis for the XXI Century: Using Pervasive Infrastructures for

Modeling Urban Dynamics

Enrique Frias-MartinezTelefonica Research, Madrid, Spain

efm@tid.es

Índice

• Introducción

• Pervasive Infrastructure

• Hotspot Detection

• Land Use Classification

• Commuting Patterns

• Conclusiones

Introducción

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

Pervasive Infrastructure

1

C e ll Phone Ne twork

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

C DR da tas e 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.

T r a f f i c

S u b s c r i b e r s s a m p l e

C e l l c a t a l o g u e

M o b i l i t y a l g o r i t h m s

| / / |2 2 3 3 4 4 5 5 6 6 1 5 0 2 2 0 0 8| / / |2 2 3 3 4 4 5 5 6 7 1 5 0 1 2 0 0 8| / / | / /2 2 3 3 4 4 5 5 6 8 1 5 0 7 2 0 0 8 2 5 0 7 2 0 1 0| / / |2 2 3 3 4 4 5 5 6 9 1 5 0 9 2 0 0 8

Hotspot Detection

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

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

Repres enta 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

Land Use Classification

• Commercial - Residential

Land Use Classification

• Night Life Areas

Land Use Classification

• Weekend Activities

Land Use Classification

• Residential

Commuting Patterns

Commuting Patterns

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

• Personalized studies (elder, young, tourists…)