As part of a data visualization project, I collaborated on the creation of an interactive platform to analyze New York City’s traffic volume across boroughs over a 24-month period (2021–2022). The centerpiece of the project was a 3D bar chart resembling NYC skyscrapers, where bar height and color intensity represented traffic volume at specific locations and times. This immersive visualization was supported by complementary 2D charts and time-based analytics, offering users a multi-layered view of urban mobility trends.

We used a three-layered architecture:
• Kepler.gl for geospatial 3D mapping of traffic density,
• Python scripts with Plotly to build hourly/borough/monthly comparisons,
• A custom HTML interface to unify these visualizations in an intuitive, web-based user experience.

Key Features:
• Interactive time slider for navigating across 2 years of traffic data
• Hover and orbit controls for detailed 3D bar exploration
• Dynamic timeline navigation for animation-style traffic progression
• Insights into traffic seasonality, borough-level patterns, and data quality challenges

The project reinforced best practices in data storytelling, design iteration, and exploratory visual analytics using real-world transportation data from NYC’s DOT. Despite technical hurdles with Kepler.gl’s limited documentation, we successfully built an elegant, responsive system with informative interactivity.