TY - GEN
T1 - An unsupervised collaborative approach to identifying home and work locations
AU - Liu, Rong
AU - Buccapatnam, Swapna
AU - Gifford, Wesley M.
AU - Sheopuri, Anshul
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/20
Y1 - 2016/7/20
N2 - There is a growing interest in leveraging geo-spatial data to provide location-aware services. With a large amount of collected geo-spatial data, a crucial step is to identify important "base" locations (e.g., home or work) and understand users' behavior at these locations. In this paper, we propose an unsupervised collaborative learning approach to identifying home and work locations of individuals from geo-spatial trajectory data. Our approach transforms user trajectory records into intuitive and insightful user-location signatures, clusters these signatures, and then identifies location types based on cluster characteristics. This clustering model can be used to identify base locations for new users. We validate this approach using Open Street Map and Foursquare location tags and obtain an accuracy of 80%.
AB - There is a growing interest in leveraging geo-spatial data to provide location-aware services. With a large amount of collected geo-spatial data, a crucial step is to identify important "base" locations (e.g., home or work) and understand users' behavior at these locations. In this paper, we propose an unsupervised collaborative learning approach to identifying home and work locations of individuals from geo-spatial trajectory data. Our approach transforms user trajectory records into intuitive and insightful user-location signatures, clusters these signatures, and then identifies location types based on cluster characteristics. This clustering model can be used to identify base locations for new users. We validate this approach using Open Street Map and Foursquare location tags and obtain an accuracy of 80%.
KW - spatio-temporal analysis
KW - user mobility behavior
UR - http://www.scopus.com/inward/record.url?scp=84981713292&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84981713292&partnerID=8YFLogxK
U2 - 10.1109/MDM.2016.53
DO - 10.1109/MDM.2016.53
M3 - Conference contribution
AN - SCOPUS:84981713292
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 310
EP - 317
BT - Proceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016
A2 - Chow, Chi-Yin
A2 - Jayaraman, Prem
A2 - Wu, Wei
T2 - 17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
Y2 - 13 June 2016 through 16 June 2016
ER -