View : 134 Download: 0

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

Title
Identifying Staying Places with Global Positioning System Movement Data Using 3D Density-based Spatial Clustering of Applications with Noise
Authors
Cho, NahyeKang, Youngok
Ewha Authors
강영옥
Issue Date
2019
Journal Title
SENSORS AND MATERIALS
ISSN
0914-4935JCR Link
Citation
SENSORS AND MATERIALS vol. 31, no. 10, pp. 3273 - 3287
Keywords
GPS log3D DBSCANmovement datastaying place
Publisher
MYU, SCIENTIFIC PUBLISHING DIVISION
Indexed
SCIE; SCOPUS WOS
Document Type
Article
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.
DOI
10.18494/SAM.2019.2410
Appears in Collections:
사범대학 > 사회과교육과 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE