Extracting urban patterns from location-based social networks

Alberto Rosi A · Marco Mamei · Franco Zambonelli

147 citations0/0 files storedWorkshop on Location-based Social Networks

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Human Mobility and Location-Based Analysis Urban Design and Spatial Analysis Data Management and Algorithms

Abstract

Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as well as produce services to take advantage of social commonalities between people. In this paper we set out to address the problem of extracting urban patterns from fragments of multiple and sparse people life traces, as they emerge from the participation to social network. To investigate this challenging task, we analyzed 13 millions Twitter posts (3 GB) of data in New York. Then we test upon this data a probabilistic topic models approach to automatically extract urban patterns from location-based social network data. We find that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.

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References

2009Eigenbehaviors: identifying structure in routine 2008Digital Footprinting: Uncovering Tourists with User-Generated Content 2009Eigenbehaviors: identifying structure in routine 2010Exploring place through user-generated content: Using Flickr to describe city cores 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network 2011Discovering routines from large-scale human locations using probabilistic topic models

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