TY - GEN
T1 - Mining frequent trajectory patterns from online footprints
AU - Huang, Qunying
AU - Li, Zhenlong
AU - Li, Jing
AU - Chang, Charles
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online footprints captured through a social media site (i.e., Twitter). Regions of frequent visits representing daily activity areas at which an individual appears are derived using the DBSCAN clustering algorithm. A trajectory pattern mining algorithm is then applied to discover ordered sequences of these spatial regions that the individual visits frequently. To illustrate and test the effectiveness of the proposed methods, we analyze the activity patterns of a selected Twitter user using the geo-tagged tweets posted by the user for an extended period. The preliminary assessment indicates that our approach can be applied to mine individual frequent trajectory patterns from online footprints that are of relatively low and irregular spatial and temporal resolutions.
AB - Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online footprints captured through a social media site (i.e., Twitter). Regions of frequent visits representing daily activity areas at which an individual appears are derived using the DBSCAN clustering algorithm. A trajectory pattern mining algorithm is then applied to discover ordered sequences of these spatial regions that the individual visits frequently. To illustrate and test the effectiveness of the proposed methods, we analyze the activity patterns of a selected Twitter user using the geo-tagged tweets posted by the user for an extended period. The preliminary assessment indicates that our approach can be applied to mine individual frequent trajectory patterns from online footprints that are of relatively low and irregular spatial and temporal resolutions.
UR - http://www.scopus.com/inward/record.url?scp=85001943978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85001943978&partnerID=8YFLogxK
U2 - 10.1145/3003421.3003431
DO - 10.1145/3003421.3003431
M3 - Conference contribution
AN - SCOPUS:85001943978
T3 - Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2016
BT - Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2016
A2 - Zhang, Chengyang
A2 - Banaei-Kashani, Farnoush
A2 - Hendawi, Abdeltawab
PB - Association for Computing Machinery, Inc
T2 - 7th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2016
Y2 - 31 October 2016
ER -