Mining spatiotemporal association patterns from complex geographic phenomena

TLDR

Spatiotemporal association pattern mining uncovers relationships among geospatial data, but current methods model phenomena as simple point events and thus fail to capture the continuously changing properties, shapes, or locations of complex geographic phenomena such as storms and air pollution. This study proposes a novel complex event–based spatiotemporal association pattern mining framework to fully reveal the dynamic characteristics of complex geographic phenomena and uncover their associated factors. The framework hierarchically models complex geographic events as directed spatiotemporal routes, applies sequence mining to uncover their spread patterns, uses an adaptive episode pattern mining algorithm to identify candidate driving factors, and is evaluated on Beijing‑Tianjin‑Hebei air‑pollution data. Experiments demonstrate that the approach effectively captures the geographic dynamics of complex phenomena, revealing their spatial spreading patterns and spatiotemporal interactions with candidate driving factors.

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.