🚲 Citi Bike Data Analysis & Geospatial Insights

An end-to-end data analytics project exploring Citi Bike trips in Jersey City (2025) using Python, geospatial analysis, interactive visualizations, and weather data integration.

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📖 Project Overview

The objective of this project is to analyze Citi Bike usage patterns in Jersey City and identify temporal, spatial, and environmental factors that influence bike-sharing activity.

The project combines data cleaning, feature engineering, exploratory data analysis, weather data integration, and geospatial analytics to generate actionable insights.

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🎯 Project Objectives

• Analyze Citi Bike trip patterns throughout the year.
• Identify the busiest months and seasons.
• Explore the most popular stations and travel routes.
• Investigate how weather conditions influence bike usage.
• Perform geospatial analysis of station activity across Jersey City neighborhoods.
• Create interactive maps for better spatial understanding.

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🛠 Technologies Used

• Python
• Pandas
• GeoPandas
• Plotly
• Folium
• Matplotlib
• Requests
• Jupyter Notebook

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📁 Project Structure

citibike/
├── data/
│   └── citibike/
├── notebooks/
│   ├── 1_Download_Citibike_Data.ipynb
│   ├── 2_Data_Enrichment.ipynb
│   ├── 3_Weather_Data.ipynb
│   ├── 4_Data_Visualization.ipynb
│   └── 5_Neighborhood_Analysis.ipynb
├── README.md
├── .gitignore
└── requirements.txt

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📊 Data Processing

The project includes:

• Downloading Citi Bike trip data
• Data cleaning
• Missing value handling
• Ride duration calculation
• Date and time feature engineering
• Season classification
• Processed dataset generation

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🌦 Weather Data Integration

Weather information was retrieved from the Open-Meteo Archive API and merged with daily Citi Bike activity.

Weather variables include:

• Average temperature
• Maximum temperature
• Minimum temperature
• Precipitation
• Rainfall
• Snowfall
• Maximum wind speed

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📈 Exploratory Data Analysis

The project explores:

• Monthly ride volume
• Seasonal ride distribution
• Top departure stations
• Top arrival stations
• Daily ride trends
• Weather impact on bike usage

Interactive Plotly visualizations were created to better understand the data.

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🗺 Geospatial Analysis

GeoPandas and Folium were used to perform spatial analysis, including:

• Station point generation
• Coordinate Reference System (CRS) handling
• Spatial joins between stations and neighborhoods
• Route analysis
• Interactive route maps
• Neighborhood-level activity analysis
• Choropleth maps
• Interactive station maps

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📌 Key Analyses

✔ Monthly Citi Bike demand

✔ Seasonal trends

✔ Most popular stations

✔ Most popular routes

✔ Weather vs bike usage

✔ Neighborhood activity

✔ Station density

✔ Average activity per station

✔ Departure and arrival distribution

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🚀 Skills Demonstrated

• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Data Visualization
• API Integration
• Geospatial Analytics
• Interactive Mapping
• Python Programming
• Business Data Analysis

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📚 Dataset

Citi Bike Trip Data (Jersey City, 2025)

Neighborhood boundaries were provided as GeoJSON files.

Weather data was obtained using the Open-Meteo Archive API.

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▶️ How to Run

1. Clone the repository

git clone https://github.com/arpidata/citibike.git
cd citibike

2. Create a Conda environment

conda create -n citibike python=3.12

3. Activate the environment

conda activate citibike

4. Install the required packages

pip install -r requirements.txt

5. Launch Jupyter Notebook

jupyter notebook

6. Run the notebooks in order

1. 1_Download_Citibike_Data.ipynb
2. 2_Data_Enrichment.ipynb
3. 3_Weather_Data.ipynb
4. 4_Data_Visualization.ipynb
5. 5_Neighborhood_Analysis.ipynb

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👩‍💻 Author

Arpenik Mesropyan

Radiologist transitioning into Data Analytics with a strong interest in data visualization, geospatial analytics, and business intelligence.