Urban Longevity

Inspiration

Our journey started by exploring the "Green Paradox"—the phenomenon where urban greening meant to improve public health can unintentionally trigger respiratory issues like asthma through high pollen counts. As a student at Baruch College specializing in Data Analytics, I wanted to apply my Python skills to solve a real-world civic challenge. We realized that while New York City has over 64,000 street trees, their health implications are rarely considered in urban planning.

What it does

Urban Longevity is a predictive dashboard that maps 64,488 trees against air quality and health baselines. It identifies "Concrete Deserts" and provides zip-code-specific "tree prescriptions"—recommending low-pollen species like the Japanese Zelkova for high-risk areas to filter PM2.5 without spiking asthma rates.

How we built it

We built the tool using Streamlit, Pandas, and Plotly. We synthesized NYC Open Data for 64,488 trees, grounded in a 7.31 µg/m³ PM2.5 baseline and an 18.91% borough-wide asthma rate. To drive our resource allocation logic, we engineered a custom metric: $Need_Index = \left(1 - \frac{tree_count}{tree_count_{max}}\right) \times 100$

Challenges we ran into

The biggest hurdle was the lack of hyper-local health data; currently, official asthma rates are only available at the borough level. We adapted by using the 18.91% rate as a constant baseline while focusing on the Need Index for visual contrast. Technically, we also had to optimize Mapbox to render nearly 65,000 data points smoothly without layout errors.

Accomplishments that we're proud of

Developed a high-fidelity "Forest Theme" UI with a dynamic light/dark mode toggle.
Designed Premium Hover HUD Cards with custom padding and Information Architecture for instant planner feedback.
Created a scalable "Decision Engine" that transforms raw environmental data into actionable urban policy. ## What we learned We learned that urban sustainability isn't just about tree quantity—it’s about strategic quality. We discovered how to pivot from data constraints (like flat health rates) to create meaningful insights by focusing on the relationship between tree density and public health risk. ## What's next for Urban Longevity Our next step is integrating real-time IoT air quality sensors to replace static baselines with live neighborhood data. We also plan to expand the species prescription logic to include other boroughs and different urban environments beyond Manhattan.