This repository contains a complete, end-to-end GIS-based neighbourhood analysis designed to support evidence-informed site selection for a YMCA-like community facility in Toronto. The project integrates demographic data, spatial analysis, and custom scoring logic to identify neighbourhoods with the highest potential need and viability.
The workflow combines Python-based data cleaning and scoring with QGIS spatial visualization, producing both reproducible analysis files and presentation-ready outputs.
- Clean and geocode Toronto neighbourhood-level demographic data
- Construct per-capita supply, demand, and viability proxies relevant to community recreation facilities
- Score and rank neighbourhoods using transparent, interpretable logic
- Visualize spatial patterns and high-priority areas in QGIS
- Produce a polished sample analysis suitable for portfolio or stakeholder review
A concise, stakeholder-facing report summarizing:
- Analytical approach
- Key assumptions and proxies
- Final neighbourhood rankings
- Visual maps and takeaways
Designed as a portfolio-ready sample rather than a technical appendix.
- Python (pandas, numpy)
- Jupyter Notebooks
- QGIS (vector layers, heatmaps, basemaps)
- OpenStreetMap basemap tiles
- This analysis does not use proprietary YMCA location data.
- Supply metrics rely on publicly available proxies rather than exact facility counts.
- Scoring weights are illustrative and designed to be adjustable based on stakeholder priorities.
- Results are intended for decision support and exploration, not definitive site selection.
- Absence of true facility-level supply data
- Neighbourhood-level aggregation may mask within-area variation
- Proxy measures may not fully capture lived community needs
These limitations are discussed explicitly in the PDF report.
Ariana Youm
This repository is intended as a demonstration of analytical reasoning, transparency, and applied GIS workflows rather than a production deployment.