Data Science For Smart Cities

Unlocking Equitable Transit Through Data-Driven Insights

New York City's complex subway system forms the backbone of urban mobility. But does it provide equitable access for all neighborhoods? This project investigates subway accessibility across NYC's diverse communities.

Using MTA data and demographic census statistics, I employed principal component analysis and clustering algorithms to group neighborhoods by transit availability. The results revealed striking disparities in access correlated to socioeconomic factors.

But the subway is more than just data points. It's infrastructure that impacts people's livelihoods. So I translated the technical analysis into an interactive dashboard that brings the numbers to life. Now policymakers and community groups can visualize subway accessibility at a glance, identifying gaps and driving targeted solutions.

At its core, this project showcases my passion for data science that serves the public good. By deriving actionable insights from complex datasets, I aim to empower stakeholders with information that cuts through the noise. My goal is to drive social change through transparent, accessible analysis and visuals that speak to our shared humanity. Because behind every data point is a community waiting to be heard.

Here's the link to the project: https://github.com/ayusuf9/Data-Science-For-Smart-Cities