New data sources are a potential goldmine for urban planners and policy makers. But sometimes they are large, sometimes they are messy, sometimes they are awkward to access, and often they are all of these things. In this hands-on course, we’ll develop skills in scraping, processing, and managing urban data, and using tools such as natural language processing, geospatial analysis, and machine learning. We’ll use examples from transit, housing, and equity planning, and build competence in open-source tools and languages such as Python and SQL. We’ll also consider the limits to data science, and the biases and pitfalls that “big data” can entail.
Learning Objectives
- Expand your urban data analysis, visualization, and Python skills, regardless of your starting point
- Identify applications of these techniques to urban planning challenges
- Know how to read API documentation and where to get more information
- Understand how to collaborate using git and other software tools
- Critically analyze the constraints to data science methods, particularly in terms of ethics and causal inference
