Paper Info
Title
Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics
Abstract
The recent adoption of ubiquitous computing technologies (e.g. GPS, WLAN networks) has enabled capturing large amounts of spatio-temporal data about human motion. The digital footprints computed from these datasets provide complementary information for the study of social and human dynamics, with applications ranging from urban planning to transportation and epidemiology. A common problem for all these applications is the detection of dense areas, i.e. areas where individuals concentrate within a specific geographical region and time period. Nevertheless, the techniques used so far face an important limitation: they tend to identify as dense areas regions that do not respect the natural tessellation of the underlying space. In this paper, we propose a novel technique, called DADMST, to detect dense areas based on the Maximum Spanning Tree (MST) algorithm applied over the communication antennas of a cell phone infrastructure. We evaluate and validate our approach with a real dataset containing the Call Detail Records (CDR) of over one million individuals, and apply the methodology to study social dynamics in an urban environment.
Year
DOI
Venue
2010
10.1109/SocialCom.2010.41
SocialCom/PASSAT
Keywords
Field
DocType
human motion,dense areas region,social dynamic,human dynamic,urban environment,urban planning,maximum spanning tree,social dynamics,wlan network,characterizing dense urban areas,mobile phone-call data,dense area,call detail records,data mining,ubiquitous computing,databases,spanning tree
Data discovery,Data mining,Computer science,Human dynamics,Phone,Urban planning,Social dynamics,Global Positioning System,Ubiquitous computing,Mobile phone
Conference
Citations 
PageRank 
References 
4
0.58
0
Authors
4
Name
Order
Citations
PageRank
Marcos R. Vieira134620.26
Vanessa Frias-Martinez221317.79
Nuria Oliver34368357.22
Enrique Frias-Martinez423817.11