We present an approach to the discovery and characterization of relevant locations and related mobility patterns in symbolic trajectories built on call detail records - CDRs - of mobile phones (telco trajectories). While the discovery of relevant locations has been widely investigated for continuous spatial trajectories (e.g., stay points detection methods), it is not clear how to deal with the problem when the movement is defined over a discrete space and the locations are symbolic, noisy and irregularly sampled, such as in telco trajectories. In this paper, we propose a methodological approach structured in two steps, called trajectory summarization and summary trajectories analysis, respectively, the former for removing noise and irrelevant locations; the latter to synthesize key mobility features in a few novel indicators. We evaluate the methodology over a dataset of approx 17,000 trajectories with 55 million points and spanning a period of 67 days. We find that trajectory summarization does not compromise data utility, while significantly reducing data size. Moreover, the mobility indicators provide novel insights into human mobility behavior.

Location relevance and diversity in symbolic trajectories with application to telco data / M.L. Damiani, C. Quadri, F. Hachem, S. Gaito - In: SSTD '19 : Proceedings[s.l] : ACM, 2019. - ISBN 9781450362801. - pp. 41-50 (( Intervento presentato al 16. convegno International Symposium on Spatial and Temporal Databases tenutosi a Wien nel 2019 [10.1145/3340964.3340980].

Location relevance and diversity in symbolic trajectories with application to telco data

M.L. Damiani;C. Quadri;F. Hachem;S. Gaito
2019

Abstract

We present an approach to the discovery and characterization of relevant locations and related mobility patterns in symbolic trajectories built on call detail records - CDRs - of mobile phones (telco trajectories). While the discovery of relevant locations has been widely investigated for continuous spatial trajectories (e.g., stay points detection methods), it is not clear how to deal with the problem when the movement is defined over a discrete space and the locations are symbolic, noisy and irregularly sampled, such as in telco trajectories. In this paper, we propose a methodological approach structured in two steps, called trajectory summarization and summary trajectories analysis, respectively, the former for removing noise and irrelevant locations; the latter to synthesize key mobility features in a few novel indicators. We evaluate the methodology over a dataset of approx 17,000 trajectories with 55 million points and spanning a period of 67 days. We find that trajectory summarization does not compromise data utility, while significantly reducing data size. Moreover, the mobility indicators provide novel insights into human mobility behavior.
Cellular data; Spatio-temporal clustering; Symbolic trajectories
Settore INF/01 - Informatica
ESRI
Google
Oracle
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/676212
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