TY - GEN
T1 - Inferring Venue Visits from GPS Trajectories
AU - Gu, Qihang
AU - Mathioudakis, Michael
AU - Sacharidis, Dimitris
AU - Wang, Gang
N1 - Publisher Copyright:
' 2017 Copyright held by the owner/author(s).
PY - 2017/11/7
Y1 - 2017/11/7
N2 - Digital location traces can help build insights about how citizens experience their cities, but also oer personalized products and experiences to them. Even as data abound, though, building an accurate picture about citizen whereabouts is not always straightforward, due to noisy or incomplete data. In this paper, we address the following problem: given the GPS trace of a person’s trajectory in a city, we aim to infer what venue(s) the person visited along that trajectory, and in doing so, we use honest Foursquare check-ins as groundtruth. To tackle this problem, we address two sub-problems. The rst is groundtruthing, where we fuse GPS trajectories with Foursquare check-ins, to derive a collection of detected stops and truthful check-ins. The second sub-problem is designing an inference model that predicts the check-in venue given a stop. We evaluate variants of the model on real data and arrive at a simple and interpretable model with performance comparable to that of Foursquare recommendations.
AB - Digital location traces can help build insights about how citizens experience their cities, but also oer personalized products and experiences to them. Even as data abound, though, building an accurate picture about citizen whereabouts is not always straightforward, due to noisy or incomplete data. In this paper, we address the following problem: given the GPS trace of a person’s trajectory in a city, we aim to infer what venue(s) the person visited along that trajectory, and in doing so, we use honest Foursquare check-ins as groundtruth. To tackle this problem, we address two sub-problems. The rst is groundtruthing, where we fuse GPS trajectories with Foursquare check-ins, to derive a collection of detected stops and truthful check-ins. The second sub-problem is designing an inference model that predicts the check-in venue given a stop. We evaluate variants of the model on real data and arrive at a simple and interpretable model with performance comparable to that of Foursquare recommendations.
KW - Check-ins
KW - Geographic choice
KW - Stop detection
KW - Venue inference
UR - http://www.scopus.com/inward/record.url?scp=85040987853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040987853&partnerID=8YFLogxK
U2 - 10.1145/3139958.3140034
DO - 10.1145/3139958.3140034
M3 - Conference contribution
AN - SCOPUS:85040987853
SN - 9781450354905
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - GIS
A2 - Ravada, Siva
A2 - Hoel, Erik
A2 - Tamassia, Roberto
A2 - Newsam, Shawn
A2 - Trajcevski, Goce
A2 - Trajcevski, Goce
PB - Association for Computing Machinery
T2 - 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2017
Y2 - 7 November 2017 through 10 November 2017
ER -