Spring 2024
Professor Wenfei Xu (wenfeixu@cornell.edu)
Class details in Cornell Classes page
Office Hours: Tuesday 3 – 4:30 pm, Thursday 12 – 1:30 pm in Sibley Hall 221
Rifqi Maulana (ram545@cornell.edu)
Office hours: 10 – 11 am on Tuesdays in Sibley 318
Houpu Li (hl927@cornell.edu)
Office hours: 10 – 11 am on Thursdays in Sibley 318
Urban data science is an emergent practice in geography and urban planning that combines: 1) the set of data analysis tools and methods used to understand a wide array of big data and big spatial data sources and, 2) questions of urban development, structure, complexity, theory, policy, dynamics, and outcomes. These approaches enable more spatiotemporally dynamic and granular analyses of cities and allows researchers new insight into urban dynamics.
This course will provide a toolkit to speak through data, code, statistics, and visualization. Using open-source data and computational tools in Python and the Jupyter Notebook environment, we will learn how to design testable research questions, collect and prepare data, apply relevant analytical techniques, present our process and results in an engaging and informative way, and identify the limitations of quantitative analysis. A personal laptop will be required.
The goal of this course is to provide an introduction to a wide range of tools and concepts that will enable future, deeper exploration of urban dynamics. In other words, this course provides a “sampler” of the foundations of urban data science through coding, statistical analysis, visualization/narrative-building, and critique.
The core learning objectives are:
- Use code to clean, analyze, and visualize spatial data
- Implement a descriptive or predictive analysis using appropriate data and statistical and/or computational methods
- Clearly communicate your process and results as a data narrative through visualizations, context, textual description, and presentation
- Identify the limitations and potential biases in the data, data-generating processes, and tools and methods in addressing your research topic
Weeks 1-12: Every class will include a brief lecture and a tutorial at the end of the class to practice the concept you just learned. There are five coding homeworks during this period. There will be oral quizzes administered throughout the semester.
Most of the materials from this course will be on the course Github repo here.
Weeks 13-15: The last three weeks of the class will be devoted to a final project of your choosing that addresses an urban development question. The aim is to synthesize and further develop the skills you have learned throughout the course of the semester. The proposals for the project will be due Week 10. In class for Weeks 14 and 15, each project group or individual will meet with me to discuss your progress, technical or conceptual roadblocks, and next steps. Our last two classes of the semester will be devoted to final presentations. Your final projects will be due on May 17 at 11:59 pm.