Citi Bike Strategy Case Study

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

1. Most Popular Stations

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

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

2. Monthly Trends + Weather Correlation

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

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

3. Trip Density & Station Distribution

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

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

Challenges & 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 & Recommendations

Seasonal Optimization: Reduce bike inventory 20–30% in winter; use weather data for demand forecasting.
Redistribution: Balance bike supply in high-traffic areas using predictive models.
Expansion Opportunities: Add capacity near parks, waterfronts, and underserved areas in Northern Manhattan and outer boroughs.