Linh Nguyen developed a suite of data-driven urban planning and housing analytics features for the Chameleon-company/MOP-Code repository over five months. She engineered end-to-end pipelines for infrastructure planning, housing accessibility scoring, and travel-time analytics, integrating Python, Pandas, and GeoPandas for data processing and visualization. Her work included building a GTFS-based travel-time API, interactive map visualizations, and standardized project organization to support reproducible workflows. Linh emphasized maintainability through improved documentation, localization, and code review processes. The depth of her contributions is reflected in robust data ingestion, cleaning, and geospatial analysis, enabling scalable, decision-support tools for education and housing use cases.
September 2025 highlights in Chameleon-company/MOP-Code: delivered end-to-end travel-time aware housing analytics and multi-modal travel-time capabilities, and prepared the project for release. Key features delivered include: 1) Housing recommendations use-case refinement and travel-time visualization: refined the use-case to account for parental travel times, established data processing scaffolding to integrate public transport and school-location data, and implemented travel-time calculations and map visualizations to support decision-making. 2) GTFS travel-time API development (API_GTFS): built a robust API_GTFS class to compute travel times across car, walking, and train modes using GTFS data and external services; added GTFS data download/processing, geocoding, and validation. 3) Housing rental analytics focused on proximity to transit and schools: Melbourne rental listings analytics with data ingestion, cleaning, geocoding, and interactive map visualizations to evaluate travel times to transit and schools for tenants. 4) Release preparation and asset organization: organized assets into a Ready to Publish directory to streamline final release or distribution.
September 2025 highlights in Chameleon-company/MOP-Code: delivered end-to-end travel-time aware housing analytics and multi-modal travel-time capabilities, and prepared the project for release. Key features delivered include: 1) Housing recommendations use-case refinement and travel-time visualization: refined the use-case to account for parental travel times, established data processing scaffolding to integrate public transport and school-location data, and implemented travel-time calculations and map visualizations to support decision-making. 2) GTFS travel-time API development (API_GTFS): built a robust API_GTFS class to compute travel times across car, walking, and train modes using GTFS data and external services; added GTFS data download/processing, geocoding, and validation. 3) Housing rental analytics focused on proximity to transit and schools: Melbourne rental listings analytics with data ingestion, cleaning, geocoding, and interactive map visualizations to evaluate travel times to transit and schools for tenants. 4) Release preparation and asset organization: organized assets into a Ready to Publish directory to streamline final release or distribution.
August 2025 performance: Delivered the Housing Accessibility Scoring and Visualization feature for rental listings in MOP-Code. The feature calculates accessibility scores by integrating utilities, stations, and landmarks, normalizes distance metrics, and visualizes the top 10 most accessible listings on a map. The work included improved documentation and inline comments to boost maintainability and onboarding. This lays the groundwork for data-driven accessibility insights that can improve match quality and user engagement across rental listings.
August 2025 performance: Delivered the Housing Accessibility Scoring and Visualization feature for rental listings in MOP-Code. The feature calculates accessibility scores by integrating utilities, stations, and landmarks, normalizes distance metrics, and visualizes the top 10 most accessible listings on a map. The work included improved documentation and inline comments to boost maintainability and onboarding. This lays the groundwork for data-driven accessibility insights that can improve match quality and user engagement across rental listings.
May 2025 monthly summary for Chameleon-company/MOP-Code: Focused on delivering data-driven infrastructure planning features for Melbourne, improving governance processes, and raising publishability through repository organization. Delivered end-to-end data loading and visualization pipelines, enhanced urban planning scenarios, and a standardized code review process. No major defects reported; the work emphasizes business value and technical excellence.
May 2025 monthly summary for Chameleon-company/MOP-Code: Focused on delivering data-driven infrastructure planning features for Melbourne, improving governance processes, and raising publishability through repository organization. Delivered end-to-end data loading and visualization pipelines, enhanced urban planning scenarios, and a standardized code review process. No major defects reported; the work emphasizes business value and technical excellence.
April 2025: Delivered the foundation and enhancements for the School Infrastructure Planning Notebook in Chameleon-company/MOP-Code. Key work included localization fixes (language setting), improved plot insights, cleanup of HTML artefacts, and removal of redundant notebooks, culminating in a significant interactive map visualization with polygons and popups for demographic data or school zones. This establishes a scalable planning tool for educational infrastructure with end-to-end data visualization and multi-language support, supporting data-driven decision making and broader adoption across districts.
April 2025: Delivered the foundation and enhancements for the School Infrastructure Planning Notebook in Chameleon-company/MOP-Code. Key work included localization fixes (language setting), improved plot insights, cleanup of HTML artefacts, and removal of redundant notebooks, culminating in a significant interactive map visualization with polygons and popups for demographic data or school zones. This establishes a scalable planning tool for educational infrastructure with end-to-end data visualization and multi-language support, supporting data-driven decision making and broader adoption across districts.
March 2025 monthly summary for Chameleon-company/MOP-Code: Delivered the Jenny Nguyen Experiment Notebook Setup feature and improved experiment organization to enable faster, reproducible experiment workflows. Implemented by adding a placeholder Jupyter Notebook and standardizing file naming, setting a foundation for future experiments. No critical bugs reported; the focus was on scaffolding, maintainability, and business value through structured data science artifacts. This work enhances onboarding, reproducibility, and project hygiene, aligning with our goals of scalable experimentation and faster time-to-value.
March 2025 monthly summary for Chameleon-company/MOP-Code: Delivered the Jenny Nguyen Experiment Notebook Setup feature and improved experiment organization to enable faster, reproducible experiment workflows. Implemented by adding a placeholder Jupyter Notebook and standardizing file naming, setting a foundation for future experiments. No critical bugs reported; the focus was on scaffolding, maintainability, and business value through structured data science artifacts. This work enhances onboarding, reproducibility, and project hygiene, aligning with our goals of scalable experimentation and faster time-to-value.
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