Spring 2025
University of Pennsylvania, Department of City and Regional Planning
Instructor: Dr. Elizabeth Delmelle
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This course teaches advanced spatial analysis and an introduction to data science/machine learning in the urban planning and public policy realm. The class focuses on real-world spatial analysis applications and, in combination with introductory machine learning, provides students a modern framework for efficiently allocate limited resources across space. Unlike its private sector counterpart, data science in the public or non-profit sector isn't strictly about optimization - it requires understanding of public goods, governance, and issues of equity. We explore use cases in transportation, housing, public health, land use, criminal justice, and other domains. We will learn novel approaches for understanding and avoiding risks of "algorithmic bias" against communities/people of color as well as communities of different income levels
- Understand how to build a predictive model for public policy decision-making applications.
- Effectively evaluate the effectiveness, generalizability, and biases of models.
- Be proficient in the data science workflow – data wrangling, exploration, modeling, and communication.
- Understand how to incorporate spatial variables from various sources into predictive models.