STPam: Software for Intelligently Analyzing and Mining Spatiotemporal Processes Based on Multi-Source Big Data

Abstract

Analyzing and mining spatiotemporal processes refers to the extraction of geographic phenomena from spatiotemporal data and the analysis of available geographic knowledge and patterns. It finds applications in various fields such as natural disaster evolution, environmental pollution, and human behavior prediction. However, training spatiotemporal models based on big data is time-consuming, and the traditional physical models and static objects used in existing geographic data analysis software have limitations in mining efficiency and simulation accuracy for dynamic spatiotemporal processes. In this paper, we develop an intelligent spatiotemporal process analysis and mining software tool, called STPam, which integrates a plug-and-play artificial intelligence model by a service-oriented method, distributed deep learning framework, and multi-source big data adaptation. The floods in the middle reaches of the Yangtze River have perennially affected safety and property in surrounding cities and communities. Therefore, this article applies the software to simulate the flooding process in the basin in 2022. The experimental results correspond to the rare drought phenomenon in the basin, demonstrating the practicality of the STPam software. In summary, STPam aids researchers in visualizing and analyzing geospatial processes and also holds potential application value in assisting regional management authorities in making disaster prevention and mitigation decisions.

Keywords

• spatiotemporal process
• deep learning
• distributed computing
• big data analysis and mining