Skip to main content Link Menu Expand (external link) Document Search Copy Copied

  1. Overview
  2. Learning Objectives
  3. Pre-requisites
  4. Readings and Class Materials
    1. Reference Text
  5. Computing
  6. Course Website and Community Forum
  7. Course Requirements
    1. Assignments (45%)
    2. In-class Quizzes (20%)
    3. Participation (5%)
    4. Final Exam (30%)
  8. SPSS or R/RStudio?
  9. Course Evaluation
  10. Course Waiver/Substitution
  11. Academic Integrity
  12. Use of AI
  13. Disability Resources for Students (DRS)
  14. Campus Safety
  15. Religious Accommodation Policy
  16. Future Statistics and Data Science @ UW

cover image

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, the 2nd lab room: Gould Hall 007
Instructor: Haoyu Yue, [email protected]. Office Hours: Monday 9:20-10:20 AM at Gould Hall 012C; or book online
Teaching Assistant: Sun Ho Hwang, [email protected]. Office Hours: Friday 10:00-11:00 AM at Gould Hall 432

Home Notes Labs Assignments Syllabus Resources Canvas Purple AI

Syllabus

Haoyu Yue

he/his

[email protected]

Office Hours
Monday 9:20-10:20 AM at Gould Hall 012C; or book online

Sun Ho Hwang

she/her

[email protected]

Office Hours
Friday 10:00-11:00 AM at Gould Hall 432 (Urban Ecology lab)

Overview

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.

Learning Objectives

After completing this course, students are expected to understand the full process of quantitative analysis, from research design to choosing methods to informing public decisions, and be able to prepare a preliminary research design:

  1. Understand the types, sources, and features of quantitative data in planning.
  2. Understand the language and thinking behind indicators, probability, estimation, hypothesis testing, and regression with both independent and dependent data.
  3. Know the essential concepts in spatial statistics, causal inference, machine learning, and artificial intelligence with their use cases in urban planning.
  4. Be able to use R or SPSS to conduct reproducible statistical analysis.
  5. Be able to create clear and honest data visualizations using Tableau, R/SPSS to explore data and communicate quantitative findings.
  6. Critically evaluate quantitative claims in planning documents, including their limitations and social consequences.

Pre-requisites

Readings and Class Materials

All readings can be accessed via the links on the course site; some may require a UW NetID login.

Reference Text

Computing

No previous programming experience is required. All required analytics applications will be in SPSS, R/RStudio, Excel, and/or Tableau. We will have an installation session during the first class.

Bring your computer (Windows or MacBook is acceptable) to each class. If you have any trouble with having a computer, you may check the computing resources from the college, Student Technology Loan Program, or UW libraries computer service. Students are also welcome to conduct any needed computer-based work in the Digital Commons in the basement of Gould Hall or other computer labs on campus.

Course Website and Community Forum

All class materials will be published on the course website. Canvas will be a place for discussion board, assignment submission, and grading purposes. Zoom will be used for recording a few sessions and hosting online office hours.

You can reach us via Canvas Discussions, email, and in person during class and office hours. Please use the Canvas Discussions as the first place to ask general questions. If you have a question about the course material or assignment, other students may have the same question. If you email us with a question like this, we will ask you to post it on the discussion board. We will review the discussion board at least once a day on weekdays. We also encourage students to answer each other’s questions on the discussion board. For emails, we try to reply to emails within 24 hours, 48 hours over a weekend, and the workday following a holiday unless otherwise noted. Simple questions will be answered by Canvas Discussions or email, but students may be asked to schedule a meeting for more complex discussions.

Course Requirements

Assignments45 %
In-class Quizzes20 %
Final Exam30 %
Participation5 %

According to the estimates for UW courses, it should take about 15 hours of work to complete a five-credit class each week. If you spend more than 9 hours beyond the classroom, please let us know, and we will adjust the study plans for you. The total scores will be curved and transformed into the UW numerical grading system for graduate courses, ranging from 4.0 to 1.7 in 0.1 increments as the final grade.

Late days: You will have 6 penalty-free late days in total for assignments (max 3 late days per submission). Any delayed submission after the first 3 days will be penalized 10% per day for that specific assignment (but will not count towards your used late days). Note: Late days CANNOT be used for the quizzes, final exam, or extra credit.

Assignments (45%)

There will be a total of 4 assignments, and the expected finish time for each is 7-9 hours after class. You need to submit them via Canvas. Each student is expected to submit their own assignments, but study groups are allowed. But you’re expected to acknowledge the names of collaborators along with a short description of the types of collaborations being done at the beginning of each submission. You may use generative AI tools, but please check the AI policy section.

All assignments will be due at 11:59 pm Pacific Time and should be submitted on Canvas. The submission should include PDF, RMD, HTML, or any other files required to rerun the code.

In-class Quizzes (20%)

There will be 5 in-class, closed-book quizzes over the course of the quarter. Quiz dates are listed on the Schedule page. Each quiz will last approximately 30 minutes and will consist of multiple-choice, fill-in-the-blank, and short-answer questions. It will assess your understanding of the concepts covered in the preceding week, drawn from both the lecture notes (required sections) and the required readings. Quizzes will not involve complex calculations and trivial details. Each quiz is graded on a 4-point scale based on the percentage of questions answered correctly:

100% - 80%79% - 60%59% - 40%39% - 0%
4 pt3 pt2 pt1 pt

You may earn 1 point back (up to the 4-point maximum for each quiz) by submitting your notes reviewing the concepts for the previous week within 48 hours of the graded quiz being returned. Notes should also explain the correct reasoning for your missing points in your own words. The lowest quiz score of the quarter will be dropped (after any points earned back applied).

Participation (5%)

There will be several surveys and in-class activities, which will be graded on completion. It is highly recommended that students attend the course regularly, as the sessions will be offered synchronously and will not be recorded. We will use class time to do the necessary activities. Class attendance and participation are integral parts of this course; much of the key material will be introduced and discussed in lectures.

Final Exam (30%)

The final exam will be an open-book exam scheduled on December 14, 4:30-6:00 PM at Gould Hall 110. You are allowed to take any lecture notes, book, or reference material, but any electronic devices, except calculators, are not allowed. The final exam will not focus on basic concepts and complex calculations but on deep understanding of quantitative methods and research design.

SPSS or R/RStudio?

Coding is terrifying for some people, but it is easier than ever because of generative AI. We strongly encourage you to use R (a free, open-source software environment for statistical computing and graphics) and RStudio (an open-source integrated development environment) as your main tools for quantitative analysis this quarter, because:

We will, meanwhile, offer an Excel + IBM SPSS (Statistical Package for the Social Sciences) option for most lab sessions. SPSS is a commercial, point-and-click package long established in social science statistics, and UW provides licenses to students through UWare. You can also attend some sessions in R while others are in SPSS.

This class is not a software or coding class, so we will not evaluate you based on your proficiency with certain tools. Therefore, if you have already learned other software or languages, such as Stata or Python, you are welcome to use them for the assignments, but the instructor and TA will not provide platform-specific assistance.

Course Evaluation

Please give us any anonymous suggestions about the lectures, labs, or anything using the anonymous suggestions box. Formal course evaluation occurs at the end of the quarter, university-widely. If you are experiencing a problem with the class, please let me know as soon as possible, as I might be able to make changes if needed within the course of the class.

Course Waiver/Substitution

Please check the departmental policy for course waiver and substitution and discuss with the instructor as early as possible.

Academic Integrity

The University of Washington expects students to know their responsibilities and maintain the highest academic conduct standards (WAC 478-121). Students are held responsible for any violation of the University of Washington Student Code, irrespective of whether the violation was intentional or not. Students suspected of cheating or otherwise violating the misconduct code will be referred to the College disciplinary process. Behaving with integrity is part of our responsibility to our shared learning community. If you’re uncertain about whether something is academic misconduct, ask me. I am willing to discuss questions you might have. Acts of academic misconduct may include, but are not limited to:

Use of AI

In this course, students are ONLY permitted to use UW’s AI-based tools: Purple AI on some assignments. The instructions for each assignment will include information about whether and how you may use AI-based tools to complete the assignment. You need to submit the exported txt conversion history if you use AI in completing the assignments.

You should use AI tools as you would use a human collaborator. This means that you may NOT directly ask generative AI tools for answers or copy solutions. You’re required to acknowledge generative AI tools as collaborators. However, we encourage students to use generative AI tools to assist with coding, debugging, and understanding class content.

All sources, including AI tools, must be properly cited. Detailed citation guidelines can be found at the UW Law Library, MIT Libraries, and the UMD Libraries. Using AI in ways inconsistent with the parameters above will be considered academic misconduct and subject to investigation. If you have any questions about citations or about what constitutes academic integrity in this course or at the University of Washington, please feel free to contact me to discuss your concerns.

Please note that AI results can be biased and inaccurate. It is your responsibility to ensure that the information you use from AI is accurate. Additionally, pay attention to the privacy of your data. Many AI tools will incorporate and use any content you share, so be careful not to unintentionally share copyrighted materials, original work, or personal information. Details about Purple AI, please read Purple and FERPA-Protected Data Usage Guidelines.

Disability Resources for Students (DRS)

Your experience in this class is important to me. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law. If you have already established accommodations with Disability Resources for Students (DRS), please activate your accommodations via myDRS so we can further discuss.

If you have not yet established services through DRS but have a temporary health condition or permanent disability that requires accommodations (conditions include but are not limited to mental health, attention-related, learning, vision, hearing, physical, or health impacts), contact DRS directly to set up an Access Plan. DRS facilitates the interactive process that establishes reasonable accommodations.

Campus Safety

Call SafeCampus at 206-685-7233 to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.

Religious Accommodation Policy

Washington state law requires that UW develop a policy for the accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at the Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Future Statistics and Data Science @ UW

Seattle is the home of many tech giants, and the University of Washington is the leading institution in statistics and data science in the Pacific Northwest, in the United States, and in the world. We are lucky to be here and have the chance to enjoy the invaluable resources from the university and the Seattle region. Here are some resources if you hope to learn more about statistics and data science after this class. Feel free to reach out to me if you have further thoughts or any questions.


Back to Top
Built on Just the Class developed by Kevin Lin at Allen School