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

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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

Home Syllabus Notes Labs Critical Report Resources Canvas Ed Discussion UW Purple AI

Syllabus

Haoyu Yue

he/his

[email protected]

Office Hours
Schedule an appointment

TA

he/his

[email protected]

Office Hours
Schedule an appointment

Overview

Real estate decision-making requires the assessment of interdisciplinary datasets, which include socioeconomic, financial, and environmental data. Determining evolving patterns, analyzing and visualizing them, is critical in holistically assessing an area and a real estate decision to be made. This course aims to provide you with an opportunity to improve your coding ability and demonstrate that using R is more replicable and efficient than Excel. We will work with generative AI to solve tricky data analytics & visualization problems. Of course, we will encounter a lot of new commands and new ways of thinking about how data is organized. However, we will most importantly learn how to understand the process of data analysis and how to best inform our audience and honestly describe the underlying data. The course is developed based on materials from Dr. Feiyang Sun at UC San Diego, Siman Ning, and Christian Phillips.

Learning Objectives

After successfully completing this course, you should be able to:

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

Overall, 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.

Pre-requisites

Readings and Class Materials

All readings can be access via the links on course website, although some may require a UW NetID login.

Reference Text

Computing

No previous programming experience is required, but appreciated. All required analytics applications will be in SPSS, R/RStudio, 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 Requirements

Assignments30 %
In-class Quizzes20 %
Critical Data Report (CDR)15 %
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.

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.

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 and Critical Data Report (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 credits.

Assignments (30%)

There will be a total of 4 assignments. The assignments will take approximately two weeks, and the expected finish time is around 5-7 hours after class for each assignment. 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 labs will be due at 11:59 pm Pacific Time and should be submitted on Canvas or GitHub. The submission should include PDF, Rmd, HTML, or any other files required to rerun the code.

In-class Quizzes (20%)

There will be ten in-class, closed-book quizzes over the course of the quarter. Quiz dates are listed on the Schedule page. Each quiz will last approximately 20-30 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 and the required readings. Quizzes will not involve complex calculations. Each quiz is graded on a 2-point scale based on the percentage of questions answered correctly:

100% - 80%2 pt
79% - 50%1 pt
49% - 0%0 pt

You may earn 1 point back (up to the 2-point maximum) by submitting 1 page of notes (based on required and optional readings) reviewing the concepts behind the questions you missed, within 24 hours of the graded quiz being returned. Notes should explain the correct reasoning in your own words, not copy the answer key. The lowest quiz score of the quarter is dropped (after any points earned back are applied).

Critical Data Report (CDR) (15%)

Please see the Critical Data Report (CDR) page.

Participation (5%)

Final Exam (30%)

The final exam will be open-book exam and you are allowed to take any lecture notes, book, or reference material. Eletrinoic deivces, except calculators, are not allowed.

SPSS or R/RStuio?

R - A free, open-source software environment for statistical computing and graphics http://www.r-project.org

RStudio - An open-source integrated development environment (IDE) https://posit.co/products/open-source/rstudio

GitHub Copilot in RStudio https://docs.posit.co/ide/user/ide/guide/tools/copilot.html

Tableau: https://www.tableau.com/academic/students

SPSS:

Communication and Community Forum

You can reach us via Ed Discussion, email, and in person during class and office hours. Please use the Ed Discussion 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 me with a question like this, I will ask you to post it on the discussion board. I will review the discussion board at least once a day (weekdays). I 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 Ed Discussion or email, but students may be asked to schedule a meeting for more complex discussions.

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.

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 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 may 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.

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 at UW. Be free to reach out to me if you have further thoughts or any questions.


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