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.

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

“…Raw-data-based trajectory mining approaches mainly attempt to resolve three major issues: frequent path search (Lee et al, 2007 ), trajectory route partition (Gaffney & Smyth, 1999 ), and stay area detection (Cho & Kang, 2019 ; Zhou et al, 2019 ). Regarding frequent path search, the basic analysis process is to segment the trajectories based on important directional turning points, where each segment represents an approximately linear path (see Fig.…”

“…The geographical information system (GIS) and relevant visualization methods also contribute to time and space behavioral studies (Ahas et al, 2008 ; Wu & Carson, 2008 ; Kwan et al, 2015 ; Xu et al, 2020 ). One of the emerging subjects in movement behavioral studies is connected with GPS trajectory mining and spatial-temporal sequence analysis (Cho & Kang, 2019 ; Li et al, 2021 ; Tang et al, 2018 ; Yuan et al, 2017 ; Brum-Bastos et al, 2018 ). GPS trajectory mining has great potential for applications in space-time behavioral studies in buildings and urban spaces, though there are various restrictions and inaccuracies when applied in indoor spaces.…”