PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Rinzivillo S., Mainardi S., Pezzoni F., Coscia M., Pedreschi D., Giannotti F. Discovering the geographical borders of human mobility. In: KI - Künstliche Intelligenz, vol. 26 (3) pp. 253 - 260. Springer, 2012.
 
 
Abstract
(English)
The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.
URL: http://link.springer.com/article/10.1007%2Fs13218-012-0181-8
DOI: 10.1007/s13218-012-0181-8
Subject Community discovery
Mobility data mining
H.2.8 Database Applications. Data mining


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