Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62310
Title: Spatiotemporal data model for network time geographic analysis in the era of big data
Authors: Chen, BY
Yuan, H
Li, QQ
Shaw, SL
Lam, WHK 
Chen, XL
Keywords: Spatiotemporal query
Spatiotemporal big data
Compressed linear reference
Spatiotemporal data model
Time geography
Issue Date: 2016
Publisher: Taylor & Francis
Source: International journal of geographical information science, 2016, v. 30, no. 6, p. 1041-1071 How to cite?
Journal: International journal of geographical information science 
Abstract: There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.
URI: http://hdl.handle.net/10397/62310
ISSN: 1365-8816
EISSN: 1362-3087
DOI: 10.1080/13658816.2015.1104317
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

8
Citations as of Oct 16, 2017

WEB OF SCIENCETM
Citations

8
Last Week
0
Last month
Citations as of Oct 16, 2017

Page view(s)

37
Last Week
1
Last month
Checked on Oct 22, 2017

Google ScholarTM

Check

Altmetric



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