Divvy Bicycle Data Analysis

Chicago's Cycling Patterns

By analyzing trip patterns, popular stations, and temporal trends, we aim to gain valuable insights into urban mobility and transportation. This analysis is not just about numbers; it's about understanding how people move within their city and how we can make that movement even better.

What have been done so far

Data Exploration & Preprocessing: Cleaned and transformed raw Divvy data to ensure accuracy and consistency.

Feature Engineering:

• Created new features, such as time of day, to uncover hidden patterns and relationships within the data.

Geospatial Analysis:

• Utilized GeoPandas to work with the geographic data of the Divvy stations.

Interactive Mapping:

• Created a map of Divvy stations using Folium, laying the groundwork for future interactive visualizations.

Temporal Analysis:

• Examined hourly ride counts for popular stations, providing insights into peak usage times.

Room for Development: Let's Collaborate

This project provides a solid starting point, and there are several avenues for further exploration :

Interactive Dashboard:

• Develop a dashboard (e.g., using Plotly or Dash) to enable users to explore the data, filter by station or time, and visualize trends.

Advanced Geospatial Analysis:

• Visualize popular routes by creating a heatmap of ride origins and destinations.

• Analyze station locations to identify areas with potential gaps in service.

• Develop a model to predict bike demand at different locations and times.

Predictive Modeling:

• Build models to predict ride duration or bike availability, considering factors like time of day, day of week, and weather.

• Use machine learning to group stations based on usage patterns.

Impact of Events:

• Analyze the influence of special events or weather conditions on Divvy usage.

Tools and Libraries

The project uses the following tools and libraries:

Python: The primary programming language used for data analysis and visualization.

Pandas: A library for data manipulation and analysis.

GeoPandas: A library for working with geospatial data.

Folium: A library for creating interactive maps.

Matplotlib: A library for creating static, interactive, and animated visualizations.

Seaborn: A library for making statistical graphics.

Scikit-learn: A library for machine learning.

Plotly: A library for creating interactive plots and dashboards.

Dash: A Python framework for building web applications.

Let's collaborate to enhance this project and create a useful resource for understanding urban mobility in Chicago.