Project Overview

Citi Bike users often face bike shortages during certain times and locations. This case study explores key data-driven insights to address availability gaps and guide expansion efforts.

Role: Data Analyst
Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Kepler.gl, Streamlit
Data Sources: Citi Bike API, NOAA Weather API (New York, 2022)

Launch Interactive Dashboard

Objectives

  • Identify high-demand stations and usage trends
  • Understand weather’s impact on ridership
  • Locate underused and underserved areas
  • Recommend redistribution strategies and expansion zones

Visualizations

Bar chart showing the top 20 most-used Citi Bike stations in NYC, highlighting hotspots near major avenues, parks, and transit hubs.

Top 20 Most Popular Stations

Figure 1: Top 20 most popular Citi Bike stations in NYC.

Top 20 Start Stations Annotated

Figure 1b: Annotated version with numeric bar labels and enhanced formatting.

Dual-axis chart comparing trip volume with daily average temperature to understand how weather impacts ridership.

Weather and Bike Usage

Figure 2: Seasonal bike usage trends vs. average temperature.

Dual-Axis Setup Chart

Figure 2b: Dual-axis plot showing bike rides and temperature with labeled axes and date formatting.

Dual-Axis Annotated Chart

Figure 2c: Annotated version highlighting peak summer ridership and coldest days.

3. Trip Density and Station Distribution

Kepler.gl map showing ride density and station placement across NYC, revealing underserved and high-demand areas.

Aggregated Bike Trips in NY

Figure 3: Interactive map showing aggregated bike trips in New York City.

Kepler.gl Map Config Screenshot

Figure 3b: Python code-driven Kepler.gl map setup showing default zoom, coordinates, and configuration for NYC.

Challenges and Solutions

Challenge Solution
Extra records from 2021/2023 Filtered by date in Python to isolate 2022 data
Temperature conversion errors Corrected Celsius-to-Fahrenheit formula
Incorrect geospatial plotting Verified and corrected latitude/longitude mapping
Large dashboard file size Filtered out low-frequency trips (under 65) to reduce size

Conclusions and Recommendations

  • Seasonal Optimization: Reduce bike inventory by 20–30% in winter months. Use weather forecasts for dynamic inventory adjustments.
  • Redistribution: Balance bike supply in high-traffic areas based on historical demand using predictive modeling.
  • Expansion Opportunities: Increase capacity near parks, waterfronts, and underserved areas such as Northern Manhattan and parts of Brooklyn and Queens.