To accurately identify residential home-work locations from mobile terminal location data, this paper proposed a home-work location identification method based on spatiotemporal constrained density clustering. Firstly, the method used a K-means based DBSCAN spatiotemporal stationary point clustering process to divide the original trajectory points of many days for each resident into different spatiotemporal stagnation point clusters. Then, it used a recognition process of residence point cluster and moving point cluster based on velocity threshold to recognize every spatiotemporal stationary point cluster of each resident as a stationary point cluster or a moving point cluster. After that, it used a DBSCAN important residence point clustering process based on K-nearest distance to divide the residence points of each resident into different important residence point clusters. Finally, it used a KNN home-work location identification process optimized by KD-tree to identify every important residence point of each resident as a home location, a work location, a home-work location or an interest location. The experimental results show that each process of this method is reasonable and effective, and the final recognition effect of home-work locations is better than the time threshold method, the cumulative time method and the information entropy method.

[2022-01-25] Accepted Paper /doc/2021.11.0628/v1

[2022-06-05] Printed Article /doc/2021.11.0628/v2