Final Project

The Final Project provides an opportunity to apply the skills and concepts developed throughout the course to a real-world urban planning challenge. In groups of 3–4, you will formulate a research question, identify relevant datasets, conduct in-depth analyses, and communicate your findings through impactful visualizations and interpretation.

You may choose any topic relevant to urban analytics (see the feasible and infeasible project examples on this page), or you may choose one of the topics suggested by MTC.

You must use Python for all analysis. No other software or languages (e.g., ArcGIS Pro, QGIS, R) are allowed.

To support different project orientations, the final project offers two tracks, which share the same analytical foundation but differ in emphasis.

Project Tracks

Track A: Python for Data Interpretation & Visual Reasoning

For students interested in interpreting urban data through maps and visual analysis. Lighter coding focus.

Deliverables

  • ArcGIS StoryMap (or equivalent)
  • 4+ standalone interactive HTML maps generated with Python

Emphasis

  • Visual reasoning and interpretability
  • Thoughtful use of spatial methods, robust cleaning process
  • Accessibility, transparency, and design judgment

Track B: Python for Data Analytics & Reproducibility

For students more comfortable coding in Python and aiming to showcase technical skills. Shorter written reports in interim phases.

Deliverables

  • GitHub repository + GitHub Pages webpage
  • 2+ standalone interactive and animated HTML maps generated with Python (integrated in the webpage)

Emphasis

  • Reproducible, modular workflows
  • Handling large /more complex datasets
  • Robust analytical methods

Opportunity Strong projects may be developed into workshop submissions, conference posters, or journal publications.

Important

Both tracks:

  • Use Python for data acquisition, cleaning, analysis, and visualization
  • Work with real urban datasets
  • Are evaluated using track-specific criteria aligned with their emphasis

Three Phases:

To ensure steady progress and tailored guidance, the project is structured into 3 phases. Detailed submission requirements will be provided in the assignment description on bCourses.

0. Group Formation

Deadline: Feb. 3 - 12:00 pm

Choose a team of 3-4 and fill this form out by the deadline.

1. Proposal and Lightning Talk Videos

Submission Deadline: Feb. 17 - 11:59 pm

This step ensures you are on the right track. The plan you outline here is a starting point and will likely evolve as you progress. You will document and justify any changes during the midterm submission phase.

2. Midterm

Submission Deadline: Mar. 31 - 11:59 pm

3. Final Project Submission

Submission Deadline: May 10 - 11:59 pm

Examples of Feasible and Unfeasible Projects

Feasible Projects:

These are some examples of feasible projects, but you are encouraged to explore beyond them.

  1. Mapping Environmental Justice Disparities: Identifying Spatial Inequities in Exposure and Access
    • Example: Do school districts in Alameda County with lower median household incomes have lower levels of tree canopy coverage? (CP101 - Fall 2025)
  2. Investigating Food Deserts in City X: Spatial Correlates and Socioeconomic Impacts
  3. Unveiling Mobility Patterns: Analyzing Bike-Sharing Data to Enhance Accessibility
  4. Mobility patterns and access
    • Example: How does transit-based job accessibility change between the AM Peak (7:30–8:30) and Late Night (22:00–23:00) in 2018-2022 period, and do Equity Priority Community tracts experience larger losses than non-EPC tracts? (CP101 - Fall 2025)
    • Example: How did BART ridership collapse and incomplete recovery (2019-2024) differentially impact Berkeley’s three stations (Downtown Berkeley, North Berkeley, Ashby) across varying income levels and multimodal connectivity, and where did the missing regional riders go? (CP101 - Fall 2025)
  5. Urban Noise and Built Form: Examining Spatial Noise Distributions in Relation to Building Configurations
  6. Optimizing Public Transit: Data Cleaning and Visualization to Detect System Inefficiencies
  7. Crime and Urban Green Spaces: Evaluating Hotspot Patterns in Relation to Local Parks in City X
  8. Decoding 311 Complaints: Spatial Patterns and Predictors of Citizen-Reported Urban Issues in City X

Unfeasible Projects

These projects are either too broad, require advanced techniques beyond the course scope, or rely on inaccessible data:

  1. Predicting Urban Sprawl for the Next 10 Years
  2. Real-Time Traffic Monitoring Using CCTV Feeds
  3. Training a deep learning model from scratch to detect pedestrians in videos.
  4. Large-scale satellite image analysis requiring significant computational resources.
  5. Building a complex VR environment representing urban planning scenarios.
  6. Developing novel algorithms requiring extensive mathematical derivation.
  7. Simulating Climate Change Impact on Coastal Cities
  8. Proposing a new accessibility/walkability/… index

Remember, the goal is to showcase your skills in using Python for urban data analytics, geospatial analysis, and visualization.

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