Urban Vein is a dynamic mapping platform that leverages public data, machine learning, and advanced data visualization to map the building materials of New York City. By using these materials as a lens, it explores what exists, what is being lost, and what should be built in the city's future.
This project relies solely on open-source data for machine learning training and data visualization. No monetized data or subscription services were used in its development. For details on the machine learning data, refer to the full story in the "ABOUT" tab.
This archive collects the visual materials during urban vein's development process, including data analysis, initial concept explorations, the evolution of the web ui design, and some branching ideas along the way.
This project was created for GSAPP's Footprint: Carbon & Design course in spring 2025. It explores the complex relationship between embodied and operational carbon, highlighting the challenges of selecting buliding materials amid information barriers. It also contributed to the development of the embedded carbon layer within Urban Vein.
This is the poster for the Colloquium II end-of-semester review, designed as a 24" x 36" printed piece. Note how the website then still uses rose charts for hover interaction with less emphasis on reflecting the actual dots per tile on the material map.
Balloonify explores inflatable interventions as a radical and sarcastic tool for reconfiguring urban landscapes. Set in a speculative future where land scarcity renders new construction impossible, this project envisions architects repurposing existing structures through subscribing to inflatable devices—offering a surreal approach to adaptive reuse.
MAP X-RAY
basemap
esri topographic
satellite imagery
planning
zoning & landuse
transportation
subway
bus
Each of the five materials is organized into six categories using a method called quantile binning. in this method, the data is divided into equal-sized groups, so each bin contains roughly the same number of data points. For example, brick is divided into six bins, ranging from 1 to 6, with each bin containing an equal portion of the data. the other four materials are also divided into their own six bins. The values for each bin differ because the maximum weight of each material varies.