Mining spatiotemporal association patterns from complex geographic phenomena. Issue 6 (2nd June 2020)

• Record Type: Journal Article
• Authors: He, Zhanjun; Deng, Min; Cai, Jiannan; Xie, Zhong; Guan, Qingfeng; Yang, Chao
• Is Part Of: International journal of geographical information science. Volume 34:Issue 6(2020)
• Page Start: 1162
• Page End: 1187
• Publication Date: 2020-06-02
• Subjects: Spatial data mining -- spatiotemporal association patterns -- complex geographic phenomena -- geographic dynamics; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1566549
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.