This project aims at supporting research for all aspects of spatiotemporal data modeling with machine learning and solving many scientific, mathematical, industrial, and engineering problems in:
- Urban Science
- Human Mobility Modeling
- Geospatial Data Analysis
- Intelligent & Sustainable Urban Systems
- Optimization & Decision Making
- Data Standardization & Valorization
- Signal Processing
- Network Science
- Social Learning
This project presents some spatiotemporal methods with data-driven machine learning:
- (Chen et al., 2024) Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression. IEEE Transactions on Knowledge and Data Engineering. 36 (2): 504-517. [Blog post]
To advance the development of spatiotemporal data modeling in the research community, this project handles various spatiotemporal data:
- Analyzing millions of taxi trips in the City of Chicago
- Utilizing international import and export trade data from WTO Stats
and provides a series of tutorials on visualizing spatiotemporal data in Python:
- Global water vapor patterns
- Germany energy consumption
- Service-level mobile traffic data