Quantitative Methods in Urban Design and Planning
URBAN 520, Department of Urban Design and Planning, University of Washington, Autumn 2026
Tuesday & Thursday, 1:30 - 4:20 pm, Gould Hall 110
Instructor: Haoyu Yue, [email protected], Schedule Office Hours
Teaching Assistant: XXX, email, Schedule Office Hours
Planning has always been built on the facts we observed, the future we anticipated, the good scenarios we wished for, and the correct interventions we relied on. But are the facts really facts? Will that future actually arrive? Is a number a good way to define the good? And are the correct interventions actually correct?
Quantitative Methods is a core course for Master of Urban Planning students at the University of Washington. It intends to serve as a foundation for taking on these questions with data, and a stepping stone toward advanced quantitative methods in the broader social sciences. We will work together this quarter, 6 hours per week, starting from where every number begins and spending the quarter making numbers, doubting them, connecting them, and deciding with them. You may not leave with an answer, but you will certainly carry away questions worth asking.
September 16, 2026
Welcome to Quantitative Methods in Urban Design and Planning in Autumn 2026. We are looking forward to meeting you in person on our first day of class. The course website will be the main place for all course information and materials. We will use Canvas as a place for submitting assignments and for grading purposes. Ed Discussion will be used for announcements, discussion, and technical questions.
Before the first class, please:
- Finish the pre-class survey.
- Look through the readings for this class on the course website.
- Look through the Lab 1 Part A and try to install R and RStudio in advance. No worries if you encounter any trouble, we will install them in the first class.
- Bring your laptop, no matter whether Windows or Mac, to each class!
Schedules
Go to the recent section. This schedule is subject to change, and please check back regularly for updates. All readings and materials can be directly accessed via the links below, although some may require a UW NetID login. Some readings and links about R/coding are on each lab session page. Please give us any anonymous suggestions about the lectures, labs, or anything using the anonymous suggestions box.
I - From Planning Concepts to Numbers
- Oct 01
- Introduction, Epistemology, Math Review
- Pre-class Survey
- LAB 1 Basic of R/RStudio and Markdown
- Oct 06
- Measurements, Data, and Descriptive Statistics
- OptionalChapter 1-3, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
OptionalChapter 5: Market, Place, Interface, All Data Are Local. Yanni Alexander Loukissas. 2019.
OptionalComputers and Decision Making. Journal of the American Planning Association. Richard Langendorf. 1985. - OptionalChapter 1-3, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
- Oct 08
- Numbers in Planning, Indicators, Census and ACS (Online)
- OptionalTidyverse Style Guide. The Tidyverse Team.
OptionalUnderstanding and Using American Community Survey Data. United States Census Bureau. 2020.
OptionalWhen Planners Lie with Numbers. Journal of the American Planning Association. Martin Wachs. 1989. - Oct 13
- Exploratory Data Analysis (EDA)
- OptionalExploratory Data Analysis. United States Environmental Protection Agency.
- Oct 15
- Visualizing Numbers
REQUIREDDefense Against Dishonest Charts. Nathan Yau.
OptionalData Viz Project by ferdio.
OptionalFrom Data to Viz.
OptionalVisual and Statistical Thinking: Displays of Evidence for Making Decisions. Edward Tufte. 1997.
OptionalAutomating the Design of Graphical Presentations of Relational Information. Jock Mackinlay. 1986.
OptionalChapter 9 Designing with Purpose, Visualize This. Nathan Yau. 2024.
OptionalHow to Lie with Charts. Gerald Everett Jones. 2018.
OptionalLearning Tableau Desktop by Tableau
OptionalTableau Tutorial by GeeksforGeeks
OptionalTableau: An Introduction. Princeton University.
OptionalTableau Viz Gallery.
II - Trusting the Numbers?
- Oct 20
- Basic Probability
- OptionalBayes Theorem, the Geometry of Changing Beliefs. 3Blue1Brown. 2019.
- Assignment 1 DUE 11:59 PM
- OptionalBayes Theorem, the Geometry of Changing Beliefs. 3Blue1Brown. 2019.
- Oct 22
- Distributions and Sampling
- LAB 4 Visualization using Tableau
- OptionalBut What is the Central Limit Theorem? 3Blue1Brown. 2023.
- LAB 4 Visualization using Tableau
- Oct 27
- Estimates and Confidence Intervals
- Oct 29
- Logic, Experiment, and the Scientific Method
- LAB 5
- Nov 03
- Statistical Testing, t-tests, Cross-tabulations, Chi-square tests, ANOVA
- Assignment 2 DUE 11:59 PM
III - Connecting the Numbers
- Nov 05
- Correlation; Cause and Effect, Confounding Variables; Simple Linear Regression
- LAB 6 Regression
- OptionalChapter 1-6, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
- LAB 6 Regression
- Nov 10
- Multivariate Regression
- Nov 12
- Spatial Regression
- LAB 7 Regression
- Nov 17
- Linear Regression of Dependent Data (cluster, time series, spatial)
- Assignment 3 DUE 11:59 PM
- Nov 19
- Causal Inference
- LAB 8 Regression
- Nov 24
- Causal Inference
IV - Make Decisions with Numbers
- Nov 26
- Thanksgiving Holiday
- Dec 01
- Decision Making
- Dec 03
- Money and Time
- LAB 10
- Dec 08
- Meachine Learning for Prediction + Presentation
- Urban Informatics in the Science and Practice of Planning JPER
- OptionalStatistical Modeling: The Two Cultures. Leo Breiman. 2001.
- Assignment 4 DUE 11:59 PM
- Urban Informatics in the Science and Practice of Planning JPER
- Dec 10
- Introduction to Artificial Intelligence + Presentation
- OptionalAI in Planning: Opportunities and Challenges and How to Prepare. American Planning Association. 2022.
OptionalA Golden Decade of Deep Learning: Computing Systems & Applications. Jeffrey Dean (UW Alumni, Google). 2022.
OptionalWhat is a Neural Network. 3Blue1Brown. 2017.
OptionalChapter 11 Supervised Learning, Modern Data Science with R. Benjamin S. Baumer et al. 2024.
OptionalChapter 12 Unsupervised Learning, Modern Data Science with R. Benjamin S. Baumer et al. 2024. - OptionalAI in Planning: Opportunities and Challenges and How to Prepare. American Planning Association. 2022.
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Built on Just the Class developed by Kevin Lin at Allen School