COURSE

INTR6000J

Spatio-Temporal Data Mining and Analytics

Systems Hub / Intelligent Transportation · 3 Credits

As urbanization continues to grow, there is an increasing demand for data-driven solutions to address urban challenges, such as transportation, environment, climate, and public health. This interdisciplinary course aims to provide graduate students with a comprehensive understanding of spatio-temporal data mining concepts, techniques, and their applications in urban computing scenarios. The course will cover topics including data sources, modeling techniques, analytics, and advanced topics, with hands-on exercises using real-world datasets. Targeting students from various disciplines like computer science, transporation, urban planning, geography, and environmental science, the course will promote collaboration and knowledge exchange. By equipping students with the necessary skills to analyze, model, and visualize spatio-temporal data, this course will contribute to the development of a new generation of professionals adept at harnessing the power of spatio-temporal data to create smarter, more sustainable cities and foster interdisciplinary research in smart cities and beyond.