Teaching

Courses

Graduate courses on computational methods, spatial data, and applied machine learning for urban planning.

  1. CP 101 Fall Undergraduate

    Introduction to Urban Data Analytics

    Foundational methods for collecting, analyzing, and visualizing urban data to support evidence-based planning decisions.

  2. CP 255 Spring Graduate

    Urban Informatics & Visualization

    Computational thinking, programming with Python, data visualization, and geospatial analytics for understanding cities through spatial data and interactive visualization.

    Course website →
  3. CP 290 Fall Graduate Seminar

    Active Transportation and the Built Environment: Accessibility, Design, and Planning

    Examines how the built environment shapes active transportation (walking, cycling, and accessibility), with a focus on design standards, planning policy, and equity.