Title
Spatiotemporal data model for network time geographic analysis in the era of big data
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.
Year
DOI
Venue
2016
10.1080/13658816.2015.1104317
International Journal of Geographical Information Science
Keywords
Field
DocType
MOVING-OBJECTS,INFORMATION-SYSTEMS,ROAD NETWORKS,MOVEMENT DATA,SPACE,TRAJECTORIES,ALGORITHM,OPPORTUNITIES,UNCERTAINTY,PRISMS
Data mining,Geographic analysis,Road networks,Computer science,Time geography,Artificial intelligence,Big data,Data model,Spatiotemporal database,Machine learning,Computation
Journal
Volume
Issue
ISSN
30
6
1365-8816
Citations 
PageRank 
References 
14
0.63
24
Authors
6
Name
Order
Citations
PageRank
bi yu chen1545.59
Hui Yuan2522.82
Qingquan Li31181135.06
Shih-Lung Shaw434123.87
William H. K. Lam517420.40
xiaoling chen615527.17