Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility

Published in (pre-print), 2018

In this paper, we propose a method to extracting significant places or places of interest (POIs) using individuals’ spatio-temporal data for human mobility analysis. General clustering methods – such as DBSCAN - were often used for detecting POIs from human mobility data. But these methods do not well use the temporal information in human mobility data. In related work, the only applied temporal information is the time interval of consecutive location data for stay-point detection. Considering temporal constraints in human mobility, we propose a POI clustering approach – namely POI clustering with temporal constraints (PC-TC) – to extract POIs from spatio-temporal data of human mobility. Following human mobility nature in modern society, our approach aims to extract both global POIs (e.g., workplace or university) and local POIs (e.g., library, lab, and canteen). Based on two publicly available datasets including 193 individuals, our evaluation results show that PC-TC has advanced features in POI granularity and the potential of sequential POI prediction. We also tested PC-TC in a real-world mobile application: the user study results show high precision of our method for POI extraction in the university environment.

Recommended citation:
Yunlong Wang, Bjoern Sommer, Falk Schreiber, and Harald Reiterer: Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility. 2018, arXiv:1807.00546.

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