20.6 Final Capstone Project

Key takeaways

• A capstone project consolidates everything you've learned into a portfolio piece.
• The workflow spans problem framing, data acquisition, analysis, visualisation, and publication.
• Focus on one question, execute rigorously, document everything.

Introduction

Congratulations — you've reached the capstone. This lab is open-ended: pick a problem, execute it end-to-end, publish the result. The outcome is a portfolio piece you can share with potential employers, clients, or collaborators.

Budget: 5–10 hours of real work, spread across a week. The instructions are deliberately high-level; that's the point.

Goals

By completing the capstone you will have:

• Practised every major skill from the 20 modules.
• A finished, publishable analysis.
• A reusable template for future projects.

Pick a question

Choose something specific, answerable, and interesting to you:

• "Which neighbourhoods in my city have the longest walks to public transit?"
• "Where has forest cover changed in [my state/province] over the last 10 years?"
• "Which residential buildings in [my city] have the best solar-panel potential?"
• "What's the flood risk zone for 1-in-100-year rainfall in [a watershed]?"
• "Where could the city add bike infrastructure to close accessibility gaps?"

Choose one. Write one sentence describing your research question.

Gather data

Identify the datasets you need. Use:

• OSM (roads, POIs, buildings).
• National / municipal open-data portals.
• Sentinel-2 imagery (Copernicus Browser, Planetary Computer).
• LiDAR (if available for your area).
• Census data.

For each dataset, record: source URL, vintage, licence, CRS, accuracy.

Pre-process

• Reproject everything to a common metric CRS.
• Clip to your study area.
• Clean invalid geometries.
• Drop unneeded attributes.
• Save intermediate results in GeoPackage or GeoParquet.

Analyse

Apply the appropriate operations:

• Accessibility question? — network routing, isochrones, population-weighted access metrics.
• Change question? — pre/post comparison, NDVI differencing, classification.
• Suitability question? — multi-criterion overlay, weighted sum.
• Risk question? — terrain + hydrology + inundation modelling.

Document every step in a Jupyter notebook or SQL script.

Visualise

Produce at least:

1. A static publication-quality map (PDF from QGIS or matplotlib) — for reports and presentations.
2. An interactive web map (MapLibre + PMTiles or Leaflet) — for sharing online.
3. A chart or table — summary statistics per region or category.

Follow Module 5 / 16 cartographic principles — legible, honest, with all required elements.

Atlas is a good option for the interactive piece if you want to focus on the GIS analysis rather than maintaining a custom web app. Upload your final layer, style it, add popups for the key fields, and include the shared map URL in your portfolio write-up.

Use the Street Light Reporter as a reference for a finished operational map: it has a clear purpose, useful status fields, practical popups, and a map that someone else can act on.

Interpret

Write 500–1000 words explaining:

• The question and why it matters.
• The data and methods.
• Key findings.
• Limitations and caveats.
• Next steps or related questions.

This is the part most analyses skip; it's the part that matters most to readers.

Publish

Minimum:

• GitHub repo with code, data preparation steps, and README.
• Blog post or report hosted somewhere permanent.
• Interactive map URL.

Tools that make this easy:

• GitHub Pages — free static hosting.
• Netlify / Vercel — JAMstack deployments.
• Observable — notebooks with live JavaScript.
• Jupyter Book / Quarto — publish notebooks as websites.
• Atlas — publish a shareable interactive map or lightweight dashboard from your final spatial layer.

Share

Post to:

• Your LinkedIn with a short summary.
• Twitter / Bluesky / Mastodon tagged with relevant GIS / your city.
• Relevant subreddits (r/gis, r/MapPorn) if the result is visual.
• The open-data providers you used — often eager to promote downstream uses.

Credit the data sources. Acknowledge any human collaborators.

Iterate

After initial publication:

• Address any feedback.
• Fix issues noted by readers.
• Extend with new data as it becomes available.

A living portfolio project is more valuable than a finished one.

Rubric for self-assessment

• Question specificity — one sentence? Answerable? ✔ / ✘
• Data provenance — every source documented? ✔ / ✘
• Methods — reproducible script / notebook? ✔ / ✘
• Analysis — uses appropriate operations from the course? ✔ / ✘
• Cartography — print map has all nine elements? ✔ / ✘
• Web — interactive map live? ✔ / ✘
• Interpretation — 500+ words of prose? ✔ / ✘
• Publication — public repo + public post? ✔ / ✘
• Ethics / limitations — acknowledged? ✔ / ✘

If you tick all nine, you've produced a strong capstone. Congratulations.

Capstone ideas (if you're stuck)

Urban

• "15-minute city" audit for your city.
• Solar-panel potential per roof in a neighbourhood.
• Accessibility of supermarkets by walking / transit.
• Which bus stops serve the most people?

Environmental

• Deforestation in a country or province over 5 years.
• Urban heat island analysis.
• Water-stress mapping in a basin.
• Habitat connectivity corridors.

Historical

• Urban expansion 1990–2024.
• Road network growth over decades.
• Comparison of historical vs modern street layouts.

Technical / methodological

• Compare multiple interpolation methods on an open dataset.
• Benchmark three routing engines on a city OSM extract.
• Reproduce a published GIS study with open data.

Troubleshooting

• Scope creep — pick one question and stop. Extend later.
• Data frustration — if one dataset doesn't exist, reframe the question.
• Visualisation paralysis — start with gdf.plot(); style later.
• Publication anxiety — imperfect and published beats perfect and unpublished.

Self-check exercises

1. What makes a capstone project good portfolio material?

Clear research question, documented methods, reproducible code, polished visuals, honest caveats, public access. A short, focused project that shows the full pipeline (data → method → result → interpretation) is more valuable than a sprawling one that shows only pieces. Employers scan in 60 seconds; make those seconds count.

2. You're half-way through and realise the data isn't sufficient. What do you do?

Reframe the question. Instead of abandoning the work, adjust scope: narrower area, different time period, coarser resolution. Document what you ran into. Real research bends the question to the available data, not vice versa. Reporting the reframing honestly adds to your credibility.

3. Why publish publicly rather than keeping a private project for your next job interview?

A public project is discoverable, criticisable (honest feedback improves you), attributable (proves it's yours), and re-usable (others build on it). Interviewers can find it before talking to you. Private projects only help once — and they're hard to prove are yours. The downside of "what if someone steals it" is nearly zero in a field where execution matters more than ideas.

Summary

• Capstone = end-to-end analysis + publication.
• Pick one question; execute rigorously; document everything.
• Static + interactive + prose = a portfolio piece that sells itself.
• Publication > perfection.

Further reading

• Wilson, Aaron — The Open Source Software Development Series.
• Good Enough Practices in Scientific Computing (Wilson et al., 2017).
• Observable examples gallery for visual inspiration.
• GIS community blogs — Kenneth Field, Anita Graser, Daniel Huffman.

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Thank you for completing the course!

You've worked through 20 modules, 78 lessons, labs, and a capstone. Whatever you build next — a career, a research project, a side interest — you now have the foundation. Go and make maps that matter.