Identifying Staying Places with Global Positioning System Movement Data Using 3D Density-based Spatial Clustering of Applications with Noise

Abstract

In this study, we visualize and analyze global positioning system (GPS) data to identify the spatiotemporal characteristics of moving and staying patterns. As a case study, we collect and process GPS data generated by students participating in inquiry-based fieldwork. Space-time path (STP) analysis is applied to visualize movement, while density-based spatial clustering of applications with noise (DBSCAN) is used to identify spatial clusters or staying places (sites where people spend time, such as homes and workplaces). We find that some clusters derived by DBSCAN are not actual clusters, and the times spent in some clusters are overestimated when we investigate the time spent in each cluster. To resolve this, 3D DBSCAN is used to find precise clusters. The results show that the 3D DBSCAN method is effective in finding clusters of spatiotemporal data. The 3D DBSCAN methodology proposed in this study can be applied effectively in movement data analysis, such as tourist travel patterns through SNS, trajectories of cars, vessels, or wildlife, and the movement of visitors in parks.