Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77497
Title: Toward space-time buffering for spatiotemporal proximity analysis of movement data
Authors: Yuan, H
Chen, BY
Li, Q
Shaw, SL
Lam, WHK 
Keywords: Movement data
Space-time buffering
Space-time overlapping
Spatiotemporal proximity
Time geography
Issue Date: 2018
Publisher: Taylor & Francis
Source: International journal of geographical information science, 2018, v. 32, no. 6, p. 1211-1246 How to cite?
Journal: International journal of geographical information science 
Abstract: Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.
URI: http://hdl.handle.net/10397/77497
ISSN: 1365-8816
EISSN: 1362-3087
DOI: 10.1080/13658816.2018.1432862
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

1
Citations as of Sep 18, 2018

Google ScholarTM

Check

Altmetric


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