An unsupervised approach for semantic place annotation of trajectories based on the prior probability

Semantic place annotation can provide individual semantics, greatly helping the field of trajectory data mining. Most existing methods rely on annotated or external data and require retraining models following a region change, thus preventing their large-scale applications. Herein, we propose an unsupervised method denoted as UPAPP for the semantic place annotation of individual trajectories using spatiotemporal information. The Bayesian Criterion is specifically employed to decompose the spatiotemporal probabil-ity of visiting the candidate place into spatial probability, duration probability, and visiting time probability. Spatial information in two geospatial data sources is comprehensively integrated to calculate the spatial probability. In terms of the temporal probabilities, the Term Frequency-Inverse Document Frequency weighting algorithm is used to count the potential visits to different place types in the trajectories and to generate the prior proba-bilities of the visiting time and duration. Finally, the spatiotemporal probability of the can-didate place is then combined with the importance of the place category to annotate the visited places. Experimental results in a trajectory dataset collected by 709 volunteers in Beijing showed that our method achieved an overall and average accuracy of 0.712 and 0.720, respectively, indicating that the visited places can be annotated accurately without any annotated data.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

In this paper, an unsupervised method called UPAPP is proposed for the semantic place annotation of individual trajectories using spatiotemporal information. The method decomposes the spatiotemporal probability into spatial probability, duration probability, and visiting time probability, and integrates spatial information from two geospatial data sources. The method achieves accurate place annotation without any annotated data, as demonstrated by experimental results.