
Developed an end-to-end educational equity study in the Chameleon-company/MOP-Code repository, analyzing the spatial relationship between Melbourne schools and public transport stops. Leveraged Python, GeoPandas, and Jupyter Notebook to build a reproducible data pipeline encompassing acquisition, cleaning, geospatial processing, and nearest-neighbor distance calculations. Enhanced the analysis with clustering and data visualization to identify accessibility gaps and inform policy decisions. Iteratively updated notebooks for clarity, maintainability, and reproducibility, while implementing API-based data loading and robust dataset management. Added export capabilities in HTML and JSON formats to support stakeholder reporting, enabling data-driven discussions around mobility and educational accessibility in Melbourne.
September 2025 performance summary for Chameleon-company/MOP-Code. Delivered an end-to-end Educational Equity Study focused on Melbourne school accessibility to public transport, including data acquisition, cleaning, geospatial processing, distance calculations to transport, accessibility clustering, and visualizations to identify equity gaps. Enhanced notebooks, API-based data loading, and robust dataset management (adding new datasets, removing large local files) with export capabilities (HTML/JSON) for Melbourne-focused reports.
September 2025 performance summary for Chameleon-company/MOP-Code. Delivered an end-to-end Educational Equity Study focused on Melbourne school accessibility to public transport, including data acquisition, cleaning, geospatial processing, distance calculations to transport, accessibility clustering, and visualizations to identify equity gaps. Enhanced notebooks, API-based data loading, and robust dataset management (adding new datasets, removing large local files) with export capabilities (HTML/JSON) for Melbourne-focused reports.
August 2025 performance summary for Chameleon-company/MOP-Code: Delivered an end-to-end Educational equity study focused on the spatial relationship between Melbourne schools and public transport stops. Implemented and iteratively updated a reproducible notebook-based analysis, including data cleaning, nearest-neighbor calculations, and visualizations to identify accessibility gaps and inform policy decisions. The work strengthens the ability to translate geospatial insights into actionable recommendations and enhances reproducibility of the data pipeline.
August 2025 performance summary for Chameleon-company/MOP-Code: Delivered an end-to-end Educational equity study focused on the spatial relationship between Melbourne schools and public transport stops. Implemented and iteratively updated a reproducible notebook-based analysis, including data cleaning, nearest-neighbor calculations, and visualizations to identify accessibility gaps and inform policy decisions. The work strengthens the ability to translate geospatial insights into actionable recommendations and enhances reproducibility of the data pipeline.

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