CSE 8803 IUC, Spring 2023
Introduction to Urban Computing


This course introduces various computational approaches for addressing the challenges that arise in urban environments. The course will discuss algorithms for storing, processing and mining data collected from urban settings. The course will consist of a mixture of computational methodologies and urban computing applications. There will be a special focus on topics such as epidemiology, sustainability, transportation, social science, and urban economics. The following topics will be covered: Machine Learning Basics, Network Science, Spatial Modeling, Trajectory Data Mining, Time-Series Analysis, Visual Analytics, Computational Epidemiology, Public Health, Urban Transportation, Environment Monitoring, Computational Sustainability, Crowdfunding.

Here is a one-page flyer for the course.

Course Information

  • Instructor: Prof. B. Aditya Prakash. Please include CSE 8803 IUC in the subject line of all email messages that you send me.
  • Teaching Assistants: TBD
  • Class Time: Tuesdays and Thursdays, 2-3:15pm, Engineering Sci and Mechanics 201.
  • Discussion: Piazza link.
  • Grading and Policies: See here.

Textbooks and Resources

There is NO required textbook. Various handouts will be provided during the lectures. Recommended reading:
  • Yu Zheng. "Urban Computing". MIT Press, 2019.
  • David Easley and Jon Kleinberg, "Networks, Crowds, and Markets: Reasoning About a Highly Connected World", Cambridge University Press 2010 (Free PDF available). Book Website: here.
  • Jure Leskovec, Anand Rajaraman and Jeffery Ullman, "Mining Massive Datasets (2nd Ed.)", Cambridge University Press 2011 (Free PDF available). Book Website: mmds.org
  • Charu Aggarwal, "Data Mining", Springer 2015 (Free PDF available (accessible through the GT network only)). Book Website: here.
  • Deepayan Chakrabarti and Christos Faloutsos, "Graph Mining: Laws, Tools and Case Studies", Morgan Claypool, 2012
See other resources (pointers to datasets, code etc.) here.


  • Welcome to the class! First class on 01/10.

Schedule (tentative)

For lecture slides and readings, go here.
  1. Introduction
  2. Machine Learning - Basics
  3. Network Science
  4. Spatial Modeling
  5. Trajectory Data Mining
  6. Time-Series Analysis
  7. Visual Analytics
  8. Computational Epidemiology
  9. Public Health
  10. Urban Transportation
  11. Environment Monitoring
  12. Computational Sustainability
  13. Crowdfunding