Spatiotemporal Graph Learning on Urban Environments

Area of Science:

• Environmental Science
• Computer Science
• Urban Planning

Background:

• Urban environments exhibit complex, nonlinear dynamics involving water, soil, air, and infrastructure.
• Traditional modeling approaches struggle to capture these intricate spatiotemporal interactions.
• Spatiotemporal graph learning (STGL) presents a powerful framework for analyzing urban complexity.

Purpose of the Study:

• To provide the first comprehensive review of Spatiotemporal Graph Learning (STGL) specifically tailored for urban environments.
• To synthesize recent advances in STGL, including graph construction, modeling, and fusion strategies.
• To examine the diverse applications of STGL across various urban systems and challenges.

Main Methods:

• Systematic review of Spatiotemporal Graph Learning (STGL) literature focused on urban applications.
• Analysis of graph construction techniques, spatial and temporal modeling approaches, and data fusion strategies.
• Case study examination of prominent STGL implementations in urban environmental intelligence.

Main Results:

• STGL effectively models nonlinear, non-Euclidean dynamics in urban systems.
• Advances in graph construction and modeling enhance forecasting accuracy and decision support.
• Successful applications demonstrated in urban water, soil, air quality, and risk management.

Conclusions:

• STGL is a foundational technology for environmental intelligence in urban settings.
• Future directions include federated learning, machine unlearning, and meta-learning for enhanced STGL.
• Next-generation STGL frameworks will support more resilient and adaptive urban environments.