Spatiotemporal data model for network time geographic analysis in the era of big data
Spatiotemporal data model for network time geographic analysis in the era of big data
复制标题
大数据时代网络时间地理分析的时空数据模型
DOI：
10.1080/13658816.2015.1104317
复制
发表时间：
2016-06-02
影响因子：
5.7
通讯作者：
Chen, Xiaoling
中科院分区：
文献类型：
--
作者：
Chen, Bi Yu;Yuan, Hui;Chen, Xiaoling
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.
