Credit: both the logo and graphics above is created by ChatGPT with the author's instruction

• MUSA 5000 Spatial Statistics (with Haoyu Zhu & Kavana Raju)
• MUSA 5080 Public Policy Analytics
• MUSA 6110: JavaScript Programming for Planners and Designers
• MUSA 5500: Geospatial Data Science in Python

Zhaochao also works as a graduate researcher and research assistant supervised by Dr. Erick Guerra, Professor of Transportation Planning and Associate Dean for Research at the Weitzman School of Design. (Only include work at UPenn)

Research Project (TA):

• Changes in Travel Behavior and Mode Share in Mexico City Over the Past Decade: A Comparative Analysis of Household Travel Surveys Using Data Mining: repo link
• How do household perceptions of the Bogotá Metro differ between Line 1 (elevated) and Line 2 (underground)? Using data mining and machine learning approaches: repo link; Secondary analysis
• How Feeder Bus Changes Travel Behavior and Mode Share in Bogota Over the Past Two Decades: A Comparative Analysis of Household Travel Surveys Using Data Mining: repo link
• Traffic fatality rate analysis across top 30 MSAs across the United States Source code link; Rmd link
• The Effects of Curb Extensions on Traffic Collisions and Injuries (Work under peer review, ACSP accepted for presentation)
• SEPTA service cut impacts (Work in Progress)

Research Project (as PI, supervised by Faculty advisor):

• WFH (Work in Progress)
• Agent-based modeling (Work in Progress)
• GNN (Graph Neural Network) analysis for transit accessibility (Work in Progress)
• Anchor Institution impact analysis (with research team, CO-PI)

To safeguard ongoing research and maintain intellectual property integrity, we are unable to share work-in-progress project details on Observe. If you're a researcher interested in collaboration, please feel free to contact me at [email protected]

The Master of Urban Spatial Analytics program is training a new generation of data scientists to tackle complex public policy problems. Using geo-spatial computing methods and open source software tools, our students and faculty seek to create positive change through data-driven decision-making. Responsible, domain-savvy data scientists can enable governments to understand what works and decide how to deploy limited resources to benefit the public. In the MUSA program, we aren’t training engineers; we are empowering social science students to use technology to solve problems that they find meaningful.

Instructor: Prof. Eugene Brusilovskiy, Lecturer, Weitzman School of Design, UPenn

This hands-on course will cover a wide range of methods frequently used for analyzing urban and spatial data. These methods are drawn from a variety of fields, including traditional statistics, spatial econometrics, and machine learning

1. Regression analysis (OLS, ridge/lasso, logistic, multinomial logit);
2. Measures of spatial autocorrelation: (spatial lag, spatial error regression, and Geographically weighted regression (GWR)
3. Spatial regression (spatial lag, spatial error, geographically weighted regression);
4. Point pattern analysis;
5. An introduction to clustering methods (k-means, hierarchical clustering, DBSCAN);
6. Big data and GIS.

Students will learn the assumptions and limitations of each method, and assignments will focus on the implementation, presentation, and interpretation of the analyses. Students will use R and GeoDa in this course.

Class work summary: https://musa-zhanchao.github.io/MUSA5000_Showcase/

Related Work: Teaching Assistant, Fall 2025

Instructor: Dr. Elizabeth Delmelle, Associate Professor, Director of MUSA, Weitzman School of Design, UPenn

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.

The format of the class includes weekly lectures/in-class demos and labs. There are seven required assignments, including two projects. The class is conducted entirely in R. Having experience in R and the ‘tidyverse’ is helpful but not strictly required.

Class work summary: https://musa-zhanchao.github.io/MUSA5080showcase/

Related Work: Lead Teaching Assistant, Github Assistant, and grader, Fall 2025.
Course Github Organization: https://github.com/MUSA-5080-Fall-2025

Instructor: Prof. Mjumbe Poe, Senior Lecturer, Weitzman School of Design, UPenn

Dashboards, story maps, and other interfaces that enable the display, analysis, and generation of geospatial data are often the end product of data analysis processes. In this course, we'll focus on the interface and interaction aspects of creating these products. Students will learn to design and build interfaces to help users access the value promised by geospatial data, modeling, and analysis. We will cover the logic and syntax of the JavaScript programming language for use in data and map-oriented web applications. The "hands-on" uses of JavaScript in urban planning applications will be emphasized. Students will hone their skills through a series of complete application projects.

Instructor: Dr.Xiaojiang Li, Assistant Professor, Weitzman School of Design, UPenn

This course will provide students with the knowledge and tools to turn data into meaningful insights, with a focus on real-world case studies in the urban planning and public policy realm. Focusing on the latest Python software tools, the course will outline the "pipeline" approach to data science. It will teach students the tools to gather, visualize, and analyze datasets, providing the skills to effectively explore large datasets and transform results into understandable and compelling narratives. The course is organized into five main sections:

1. Exploratory Data Science: Students will be introduced to the main tools needed to get started analyzing and visualizing data using Python.
2. Introduction to Geospatial Data Science: Building on the previous set of tools, this module will teach students how to work with geospatial datasets using a range of modern Python toolkits.
3. Data Ingestion & Big Data: Students will learn how to collect new data through web scraping and APIs, as well as how to work effectively with the large datasets often encountered in real-world applications.
4. Geospatial Data Science in the Wild: Armed with the necessary data science tools, students will be introduced to a range of advanced analytic and machine learning techniques using a number of innovative examples from modern researchers.
5. From Exploration to Storytelling: The final module will teach students to present their analysis results using web-based formats to transform their insights into interactive stories.

Zhanchao Yang

Transportation Research Assistant
MUSA Teaching Assistant
Master of City Planning & Master of Urban Spatial Analytics
Department of City and Regional Planning
Weitzman School of Design
University of Pennsylvania

Feel free to visit my personal GitHub account