Final Project FAQ

Answers to questions that come up repeatedly about the final project: forming a group, the proposal, scoping, data, and what past submissions look like.

For questions about tools and techniques that apply across the whole course — finding niche data, querying OpenStreetMap, diagramming a workflow, strengthening an analysis — see the Course FAQ.

Table of Contents

Q: How can I find a group for the final project?

A: We’ve started a thread on Ed Discussion here to help facilitate the formation of final project groups.

Q: I have prior coding experience. Which track should I choose?

A: Track B. Both tracks write and run Python, but Track B is where that experience earns points: code structure and reproducibility are the heaviest criterion, and the analysis is expected to be more rigorous. See Project Tracks for what each track asks for.

Q: What are the most common issues in proposal submissions and how can I address them in my final submission?

A: Based on feedback from proposal reviews, here are the top areas to strengthen in your final submission:

  1. Distinguish Between Problem and Research Question Start by clearly stating the actual problem, not just the question you’re trying to answer. Try to specify: problem, scope, geography, population, time frame, and intended outcome (this may be more than just a single sentence—or even a single paragraph!). Explain where your question originates from and why it matters. Make it quantifiable and bounded.

  2. Support All Claims with Sources Cite sources when defining problems or making claims about importance. Don’t assume things matter without providing evidence. Include citations in both the written proposal document and presentation slides.

  3. Clarify Methods: Analysis vs. Visualization Explain how your methods will help you answer your research question, not just communicate findings. Name core transforms such as buffer, join, aggregate, and calculate rates. Describe analytical methods beyond creating maps, such as statistical tests or comparative metrics. Ensure methods match your question and avoid unnecessary complexity. Consider diagramming your process for greater clarity, but provide narrative explanation in subsequent paragraph text.

  4. Specify Your Data Precisely Define temporal bounds clearly, including what time period you are studying and why. Explain relevant context for that period, such as pre/during/post COVID-19 or infrastructure changes. Justify your unit of analysis and spatial boundaries. For each dataset, explain relevance and provide Coverage, Granularity, Type, Format, and Source.

  5. Define Variables, Criteria, and Handle Abstractions Clearly define key terms in your research question, such as what makes something “underserved” or “accessible.” Explain the significance of your variables. Identify essential abstractions in your computational approach. Be aware of implications your definitions may have on affected populations and do better than just a disclaimer when describing how those impacts might be mitigated.

  6. Review and Address Feedback Methodically address written feedback comments in your revised submissions. If you are unclear on ANY feedback provided, reach out for clarification in-class or during office hours immediately. Make sure your approach directly addresses the problem you have defined.

Q: I’m worried about scoping my project. Will I have the opportunity to revise my proposal after my initial submission?

A: Yes, you MUST revise your proposal in response to feedback. In general, if you are worried about your project scope, the best thing you can do is be as specific as possible in your research proposal. Explore and identify specific data sources, review literature and research methodologies relevant to your question up front, attend office hours and pitch your draft question, proposed data sources, and methodologies. By doing this, you will gain better insight into how feasible your project will be to complete in the span of this course.

Q: How technical can we get with our projects?

A: As technical as you want. At minimum, use the methods we cover in class; beyond that, go as far as your question genuinely needs. Bring in your own domain knowledge — the strongest projects usually come from a group that knows something about the topic that we do not.

Q: I don’t know where to find the data needed to answer my research question

A: Start with the Datasets page, which lists sources by topic: transit and mobility, California and Bay Area portals, 311 service requests, housing, health and environment, urban sound, demographics and labor, and cross-cutting catalogs.

Beyond that list, try the APA Technology Division’s Resource Search Tool. The teaching team is also available to help you identify sources, and asking us is generally one of the fastest routes.

Q: Where can I find examples of advanced and basic submissions?

A: See the Past Examples page for projects from previous semesters, in both the StoryMap and GitHub Pages formats.

Acknowledgement: Thanks to Lian Plass for their help compiling these questions.