URSP688Y
Spring 2025
Urban Studies and Planning
School of Architecture, Planning, and Preservation
University of Maryland, College Park
Instructor
Chester Harvey
National Center for Smart Growth
[email protected]
This repository contains files and other course content for URSP688Y, Urban Data Science and Smart Cities, in Spring 2025. It will be updated regularly throughout the semester. Official announcements, readings, and grades will be handled on ELMS-Canvas.
Students should submit all assignments as pull requests to this repository. All submitted materials will be public.
| Week | Topic | Format | Due | Discussion Leader |
|---|---|---|---|---|
| Week 1: Jan 27 | Course Introduction; Programming Fundamentals | Zoom | ||
| Week 2: Feb 3 | Development Environment; Programming Fundamentals | In-Person | Exercise 0 | Chester |
| Week 3: Feb 10 | Tabular Analysis | Zoom | Exercise 1 | |
| Week 4: Feb 17 | Modular Programming and Generalizability | Zoom | Exercise 1 Code Review | |
| Week 5: Feb 24 | Accessing and Wrangling Data | Zoom | ||
| Week 6: Mar 3 | Data Visualization | In-Person | Exercise 2 | |
| Week 7: Mar 10 | Geospatial Data | Zoom | Exercise 2 Code Review | |
| Spring Break | 🏄 🌴 🏄 🌴 🏄 🌴 🏄 🌴 🏄 🌴 🏄 🌴 | NO CLASS | ||
| Week 8: Mar 24 | Final Project Proposal Workshop | Zoom | Final Project Proposal | |
| Week 9: Mar 31 | Network Analysis | Zoom | Exercise 3 | |
| Week 10: Apr 7 | Spatial Visualization | In-Person | Exercise 3 Code Review | |
| Week 11: Apr 14 | Dashboards | Zoom | ||
| Week 12: Apr 21 | Machine Learning | Zoom | Exercise 4 | |
| Week 13: Apr 28 | TBD | Zoom | Exercise 4 Code Review | |
| Week 14: May 5 | Final Project Presentations | In-Person | ||
| Week 15: May 12 | Final Project Due | NO CLASS | Final Project Materials |
GitHub (Coding demos and exercises)
ELMS-Canvas (Readings, grades, and course communications)
You will need a Mac, Windows, or Linux laptop with the ability install software and join a Zoom meeting, preferably with a camera. If you have challenges accessing appropriate technology, please let me know and I will try to help.
Novel data and computational tools are reshaping planning, development, operation, and understanding of urban systems. These may enable more efficient and equitable distribution of resources but may also reproduce injustices and divert attention away from more straightforward solutions. This course will introduce students to basic tools and applications in data science for examining urban systems while also challenging them to critique the role of technology in improving cities. What are data science’s strengths and weaknesses? Where does it belong (or not) in our planning toolkits? Have planners and technologists appropriately espoused the capabilities of data science and smart cities? How have these technologies failed to live up to their advertised capabilities? What questions can big data answer, and what issues does it raise? These overarching questions will guide parallel technical and theoretical threads throughout the semester.
The technical thread will use coding demos and short exercises to introduce students to programming logic and Python for urban data science. Demos will be aimed at beginners: students who have never coded before. Exercises will give beginners opportunities to practice new skills and more advanced coders opportunities to address urban applications they may not have previously encountered.
The theoretical thread will use reading seminars to examine the emergence, capabilities, and limitations of smart cities, big data, and urban data science.
The threads will converge on a final project that asks students to use data science to address a contemporary planning issue and critique its capabilities and limitations. Students may either implement their project in code or write a detailed proposal for it.
This course will prepare you to:
- Use programming logic and Python to address analytical questions, implement analyses, and share them reproducibly with industry-standard tools
- Understand the technical and ethical limitations of digital technologies in urban contexts
- Be professionally conversant with urban technologies, either as a coder or collaborator in design, implementation, and interpretation of urban analyses
Each class will include an interactive coding demo. In addition to introducing core techniques, these will be an opportunity to roll up our sleeves and program collaboratively as a class. They will show how programming is sometimes (or often) messy and frustrating, but achievable with a bit of grit and ingenuity. Demos will be a welcoming place for beginner coders to embrace uncertainty, ask what might feel like dumb questions, and recognize that many others are facing the same challenges.
Each class will also include a discussion-based reading seminar. You should complete readings ahead of each class and be prepared to discuss them. Readings demonstrate the use of urban data science techniques in research and discuss theoretical issues around the use of urban data science and smart cities technologies. They are meant to show both best practices and opportunities for critique and improvement.
Everyone will be assigned to lead discussion for at least one class. Discussion leaders should pose questions to classmates and manage discussion about issues raised by the readings. Leaders should not give presentations summarizing the readings.
Short exercises and complementary code reviews will give you opportunities to practice coding and review how your classmates address the same problems. Each exercise will be structured around a question addressable with the techniques you have learned so-far. You are encouraged to outline solutions with pseudocode. Good pseudocode will get 90% credit, even without runnable code.
A “clean result” means that I can rerun your code and arrive at the same number, table, figure, or other final output that you did, and that this output addresses the question posed by the exercise. There will rarely be entirely right or wrong answers.
Each exercise will be followed by a code review assignment in which you will be assigned to review the solution submitted by a classmate. Here are some guidelines for code review, adapted from John Ousterhout’s Software Design Studio at Stanford University:
- Write approximately 5-15 comments, depending on code complexity and issues/strengths.
- Focus on red flags, including issues with flow, logic, documentation.
- Is the code easily understandable? Could you use or adapt it? Identify the most complicated parts.
- How effective are variables and functions? Too generalized? Not generalized enough?
Late submissions will automatically receive a 10% penalty for each day they are overdue with a maximum late penalty of 50%.
Exercises and code reviews will be submitted as pull requests and comments on the course GitHub repository.
Note that all pull requests and comments on the course repo will be publicly viewable.
Exercises [and code reviews] will be graded out of 10 points based on this rubric:
- 0: nothing handed in
- 6: sloppy or illogical code or pseudocode with no clean result
[offers perfunctory praise or identifies basic issues] - 9: sloppy code with a clean result OR neat and logical pseudocode
[identifies key strengths and issues] - 10: neat and logical code or pseudocode with a clean result
[thorough evaluation of strengths and issues] - 11: wow
The course will culminate with a final project delivered in three stages—a short proposal (10%), presentation (10%), and final product (30%)—that uses data science to address a real-world planning problem. You may do the final project independently or with one partner.
The project will ask you to address a request from an imaginary planning agency for analysis of a potential equity gap within their city or region. You will choose which agency you are working for and the question you are asking about equitable outcomes. In designing your analysis and discussing the results, you should also consider the theoretical strengths and weaknesses of using data science and smart city approaches to examine equity and address inequities.
Projects may take two forms: (1) a functioning analysis with input data and a codebase that yield reproducible results, or (2) a proposal for an analysis that could reasonably be executed.
You will develop a well-documented repository of data and code, along with a short accompanying narrative describing the project’s motivation, central question, approach, results, and discussion of their meaning. The narrative may be either a traditional paper or a customized webpage/site/app that combines text and graphics. Narratives for this option are expected to be 1,000–1,500 words (2–3 pages, single spaced).
You will write a paper with sections similar to the narrative described above, but with considerably more detail about the proposed approach. In lieu of conducting the analysis, you must convincingly portray how it will be conducted (when it is funded, of course), including proposed data sources and tools. The proposal should also include expanded discussion of smart cities theory to support and critique how your approach relates to equity, both in the substantive question it addresses and opportunities or issues it raises methodologically. This will likely draw on literature outside of what is assigned for the course. Narratives for this option are expected to be 4,000–6,500 words (8–12 pages, single spaced).
Convincing proposals are crucial in both research and practice. Imagine you are applying for a grant to fund a research project or responding to an RFP issued by a public agency. In both cases, you need to convince the reader that your approach is actionable. It should also be intelligible for a non-expert audience. Writing with this combination precision and clarity is a valuable skill to hone.
This is a hands-on, discussion-oriented course. Please come to class on time having read assigned materials and ready to engage with your fellow students. Lack of engagement in and preparedness for discussion, or substantial absence, will impact your participation grade.
We will read large portions of Jennifer Clark’s book on smart cities, Uneven Innovation. You may want to buy a hard copy, or you can read the ebook through the UMD library. All other readings are either available at links in the schedule or on ELMS-Canvas.
Clark, J. (2020). Uneven Innovation: The Work of Smart Cities. Columbia University Press. (UMD Link)
Please use this course as an opportunity to practice professional communication with me and your student colleagues. Follow professional etiquette in email correspondence. Grammarly has an excellent guide. Please call me “Chester”; note that other professors may prefer more formal titles. Introduce your preferred pronouns—mine are he/him/his—and refer to others by their preferred pronouns.
I typically read and send email during regular business hours: 9am to 5pm ET on weekdays. I aim to address time-sensitive email within one business day but may take longer. If you send me an email on Friday, I may not get back to you until the next week. Please plan ahead.
On Zoom, we will have a ‘cameras on’ norm to promote focus and collegiality. Many of us will have unideal environments or technological hiccups. This is okay. Nonetheless, please do your best to minimize distractions for you and your classmates.
A key aspect of writing good code is readability: can you and others quickly and easily understand it? In many cases, code will run (readable to the machine) without being readable for a human. But sloppy, unreadable code won’t be as useful for debugging, recycling, and documenting your process. I will demonstrate readable code in demos and exercise notebooks. You will need to write readable code to get full points on exercises. You will be much more appealing as a prospective collaborator or hire if you write readable code.
UC Berkeley’s introductory computer science course, CS61A, has an excellent composition guide. PEP 8 is a standard Python style guide. Google publishes their internal Python style guide. When in doubt, be consistent and use good judgement.
Coders often feel like professional Googlers. Neary endless code snippets are available on sites like StackOverflow. Generative AIs, like ChatGTP, are increasingly important tools. Human collaboration is also extremely useful for ideation and troubleshooting. This course will give you practice using diverse sources of help while not abusing them. There are both practical and ethical reasons to avoid leaning heavily on code you don’t write yourself. Code ripped from a forum, AI bot, or your too-generous friend, is unlikely to be beautiful, reliable, or efficient. Part of your job is to use resources intelligently, gut-check sources (is ChatGTP doing what I wanted?), customize examples to your purposes, and be a creative and ethical backstop to the availability of sloppy and poorly credited shortcuts.
Here are my suggestions:
- Be a compulsive Googler. Ask ChatGTP what’s wrong with your code. Ask your friends, too. Use these resources to learn and make your coding better, not to avoid learning. If I suspect you have merely copied and pasted code for an exercise, I will call you out on it. If I can’t tell, good on the AI, but bad for your learning as a coder. Why bother taking this class?
- If you’re in a Googling death spiral and just can’t find an answer, first ask a classmate—there’s a good chance you’ve confronted the same issue—then ask me.
- You are encouraged to work on exercises with a classmate. You’re even welcome to submit the same code. Just add a comment about who you worked with to the top of your submission.
If you copy and paste a non-trivial amount of code from any source, including a generative AI, you are expected to cite your source. I often paste StackOverflow URLs into docstrings for basic functions that I’ve borrowed or adapted from posts, both to show others where I got them, and remind myself in case I want to go back to the source later. If ChatGPT helps you write a code block, make a comment about the tool name query that generated the code. This approach is both practical and ethical.
Writing that is intended to reflect your voice as an author, including any aspect of an assignment requiring narrative prose, may not be substantively authored by a generative AI. Doing so would require that you declare the AI tool as the author, which would preclude you taking credit for the product. AI tools may be used for planning and editing. This includes refining arguments or research questions, outlining, spelling and grammar assistance, and translation.
Final letter grades will be assigned based on these ranges: 90-100%: A, 80-89%: B, 70-79%: C, 60-69%: D, 0-59%: F. Letters may be augmented by + or – at the high or low end of each range.
The University of Maryland is committed to creating and maintaining a welcoming and inclusive educational, working, and living environment for people of all abilities. The University of Maryland is also committed to the principle that no qualified individual with a disability shall, on the basis of disability, be excluded from participation in or be denied the benefits of the services, programs, or activities of the University, or be subjected to discrimination. The Accessibility & Disability Service (ADS) provides reasonable accommodations to qualified individuals to provide equal access to services, programs and activities. ADS cannot assist retroactively, so it is generally best to request accommodations several weeks before the semester begins or as soon as a disability becomes known. Any student who needs accommodations should contact me as soon as possible so that I have sufficient time to make arrangements.
For assistance in obtaining an accommodation, contact Accessibility and Disability Service at 301-314-7682, or email them at [email protected]. Information about sharing your accommodations with instructors, note taking assistance and more is available from the Counseling Center.
Notice of mandatory reporting of sexual assault, sexual harassment, interpersonal violence, and stalking: As a faculty member, I am designated as a “Responsible University Employee,” and I must report all disclosures of sexual assault, sexual harassment, interpersonal violence, and stalking to UMD’s Title IX Coordinator per University Policy on Sexual Harassment and Other Sexual Misconduct.
If you wish to speak with someone confidentially, please contact one of UMD’s confidential resources, such as CARE to Stop Violence (located on the Ground Floor of the Health Center) at 301-741-3442 or the Counseling Center (located at the Shoemaker Building) at 301-314-7651.
You may also seek assistance or supportive measures from UMD’s Title IX Coordinator, Angela Nastase, by calling 301-405-1142 or emailing [email protected].
To view further information on the above, please visit the Office of Civil Rights and Sexual Misconduct's website at ocrsm.umd.edu.
Please see UMD’s website for graduate course-related policies.
Structure and Interpretation of Computer Programs (CS61A), UC Berkeley. https://cs61a.org/
Introduction to Computer Science (CS50), Harvard. https://www.edx.org/cs50
Introduction to Data Science (CMSC320), UMD. https://cmsc320.github.io/
Courses listed below under “Acknowledgements”
Adhikari, A., DeNero, J., Wagner, D. (2022) Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition. https://inferentialthinking.com (Originally developed as the textbook for Data 8: Foundations of Data Science, UC Berkeley)
Downey, A. B. (2012). Think Python: How to Think Like a Computer Scientist - 2e. Green Tea Press. https://greenteapress.com/wp/think-python-2e/
Lloyd, C. D. (2010). Spatial Data Analysis: An Introduction for GIS Users. Oxford University Press. (UMD Link)
Rey, S., Arribas-Bel, D., & Wolf, L. J. (2023). Geographic Data Science with Python. CRC Press. https://geographicdata.science/book/intro.html
Singleton, A. D., Spielman, S., & Folch, D. (2018). Urban Analytics. SAGE Publications Ltd.
Ousterhout, J. (2021). A Philosophy of Software Design, 2nd Edition. Yaknyam Press. (PDF Link)
Urban Informatics and Visualization Course Wiki (UC Berkeley)
Software Carpentry (Scientific computing tutorials)
Real Python (Python tutorials)
This course is inspired by numerous other courses and colleagues, especially:
- Urban Informatics and Visualization at UC Berkeley: Max Gardner, Paul Waddell, Meiqing Li, Irene Farah, Geoff Boeing, Sam Maurer, Arezoo Besharati, and others.
- Introduction to Urban Data Analytics at UC Berkeley: Karen Chapple, Irene Farah, Abby Cochran, Manual Santana Palacios, and others.
- Urban Data Science at UCLA: Adam Millard-Ball
- Spatial Data and Analytics at UC Berkeley: Solomon Hsiang, Jonathan Proctor, Ian Bolliger, Luna Huang, and others.
- Software Design Studio at Stanford University: John Ousterhout
- Course introduction
- Why data science?
- Why urban data science?
- Opportunities and challenges
- Plan for the semester
- Level setting
- Experience in planning/urban systems
- Experience with programming/data/technology
- Programming fundamentals
- Problem decomposition
- Inputs and outputs
- Flow and logic
- Modular design/encapsulation
- Incremental development
- Pseudocode
- Problem decomposition
- Intro to Python
- Why Python?
- Variables
- Syntax vs. style
- Basic data types
- Conditions and loops
- Errors and debugging
- Version control and code sharing
- Submitting exercises with GitHub
- Reading Seminar
- Somers, J. (2023). Begin End: A coder on the waning days of the craft. New Yorker, 99(38), 14-18. (Direct Link) (UMD Link)
- Development environment
- Conda
- Jupyter Lab
- More programming fundamentals
- Logical flow
- Functions
- Building and troubleshooting
- Documentation
- Googling
- Generative AI
- Reading seminar
- Chapter 1: “Uneven Innovation: The Evolution of the Urban Technology Project” (pp. 1–30) Clark, J. (2020). Uneven Innovation… (UMD Link)
- Tabular Analysis
- Table structure
- Tidy data
- Wide vs. Long
- Packages
- Numpy
- Pandas
- Tabular joins
- Loading and exporting files
- Table structure
- Code Review Workshop (How-To)
- Reading seminar
- Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., Haddock, S. H. D., Huff, K. D., Mitchell, I. M., Plumbley, M. D., Waugh, B., White, E. P., & Wilson, P. (2014). Best Practices for Scientific Computing. PLOS Biology, 12(1), e1001745. https://doi.org/10.1371/journal.pbio.1001745
- Modular programming and generalizability
- Writing and importing modules
- Repository structure
- Code Review Workshop
- Reading seminar
- Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. https://doi.org/10.1007/s10708-013-9516-8
- Accessing and wrangling data
- APIs
- Parsing JSON
- Messy data
- Big data
- Reading seminar
- Chapter 3: “Smart Cities as Emerging Markets” (pp. 57–94)
Clark, J. (2020). Uneven Innovation… (UMD Link)
- Chapter 3: “Smart Cities as Emerging Markets” (pp. 57–94)
- Data visualization
- Principles of graphic communication
- Matplotlib
- Seaborn
- Export to Illustrator
- Reading seminar
- [Chapter 6: “Smart Cities as Participatory Planning” (pp. 156–180)]
Clark, J. (2020). Uneven Innovation… (UMD Link)
- [Chapter 6: “Smart Cities as Participatory Planning” (pp. 156–180)]
- Geospatial data
- Points, linestrings, and polygons
- Coordinate systems
- Shapely
- Geopandas
- Overlay and proximity analyses
- Spatial joins
- Code Review Workshop
- Reading seminar
- Peng, Q., Knaap, G., & Finio, N. (2023). Do Multifamily unit Rents Increase in Response to Light Rail in the Pre-service Period? International Regional Science Review, 01600176231162563. https://doi.org/10.1177/01600176231162563
- Spring Break
- Final Project Proposal Workshop
- Network analysis
- Reading seminar
- Pereira, R. H. M. (2019). Future accessibility impacts of transport policy scenarios: Equity and sensitivity to travel time thresholds for Bus Rapid Transit expansion in Rio de Janeiro. Journal of Transport Geography, 74, 321–332. https://doi.org/10.1016/j.jtrangeo.2018.12.005
- Spatial visualization
- Map design fundamentals
- Basemaps
- Mapping in Python
- Code Review Workshop
- Reading seminar
- Hatch, M. E., Raymond, E. L., Teresa, B. F., & Howell, K. (2023). A data feminist approach to urban data practice: Tenant power through eviction data. Journal of Urban Affairs, 0(0), 1–20. https://doi.org/10.1080/07352166.2023.2262629
- Dashboards
- Reading seminar
- [Chapter 7: “Smart Cities as the New Uneven Development” (pp. 181–200)]
Clark, J. (2020). Uneven Innovation… (UMD Link)
- [Chapter 7: “Smart Cities as the New Uneven Development” (pp. 181–200)]
- Machine learning
- Supervised and unsupervised learning
- Classification
- Reading seminar
- Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992. https://doi.org/10.1016/j.cities.2020.102992
- [lab space held for a student-requested topic]
- Code Review Workshop
- Reading seminar
- Goodspeed, R. (2022). Leveraging the promise of smart cities to advance smart growth. In Handbook on Smart Growth (pp. 307–322). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781789904697/book-part-9781789904697-31.xml
- Final project presentations
- Final project due