Mining spatiotemporal association patterns from complex geographic phenomena

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.

KEYWORDS:

• Spatial data mining
• spatiotemporal association patterns
• complex geographic phenomena
• geographic dynamics

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Key Program of National Natural Science Foundation of China[41730105]; National Key Research and Development Program of China [2017YFB0503600; 2017YFB0503601; 2018YFB0505500; 2018YFB0505504], National Natural Science Foundation of China [41801309] and Fundamental Research Funds for the Central Universities [G1323541874].

Notes on contributors

Zhanjun He

Zhanjun He is currently a lecturer at China University of Geosciences (Wuhan) and his research interests focus on spatio-temporal data mining and pattern recognition.

Min Deng

Min Deng is currently a professor at Central South University and Dean of Geo-informatics department. His research interests are map generalization, spatio-temporal data analysis and mining.

Jiannan Cai

Jiannan Cai is a Ph.D. candidate at Central South University and his research interests focus on spatio-temporal clustering, association rule mining and statistics.

Zhong Xie

Zhong Xie is professor at China University of Geosciences (Wuhan) and his research interests focus on spatio-temporal data mining and spatial information services.

Qingfeng Guan

Qingfeng Guan is professor at China University of Geosciences (Wuhan) and his research interests focus on spatio-temporal analysis and high performance computation.

Chao Yang

Chao Yang is currently a lecturer at China University of Geosciences (Wuhan) and his research interests focus on social media big data mining.