https://github.com/canayter/tableau-citi-bike-data-visualization

Aggregating the data found in the Citi Bike Trip History Logs to identify and visualize new phenomena regarding city bike use.
https://github.com/canayter/tableau-citi-bike-data-visualization

datavisualization tableau

Last synced: 4 days ago
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Aggregating the data found in the Citi Bike Trip History Logs to identify and visualize new phenomena regarding city bike use.

• Host: GitHub
• URL: https://github.com/canayter/tableau-citi-bike-data-visualization
• Owner: canayter
• License: mit
• Created: 2024-04-03T03:14:17.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-08-23T22:54:53.000Z (3 months ago)
• Last Synced: 2024-08-23T23:51:15.504Z (3 months ago)
• Topics: datavisualization, tableau
• Homepage: https://public.tableau.com/app/profile/mustafa.ayter/viz/module18-tableau/CityBikeAnalysis
• Size: 26.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Citi Bike Data Analysis Project

By: Can "Jon" Ayter

## Introduction

This project analyzes Citi Bike ridership data using Tableau to uncover insights about user behavior, popular stations, and usage patterns. The goal is to provide actionable recommendations for improving the bike-sharing service.

## Interactive Dashboard

[Citi Bike Data Analysis Project - Tableau Portfolio](https://public.tableau.com/app/profile/canayter/viz/module18-tableau/CityBikeAnalysis)

## Highlights

* Popular stations have larger circles on maps, representing frequent starting and ending points for riders.

* Many popular stations are consistent for both ride starts and ride ends.

* Stations in outer city areas tend to have larger circles, possibly due to population distribution and limited alternative transportation options.

## Key Findings

* Popular stations are consistent for both ride starts and ends, with larger circles on maps indicating higher usage.

* Outer city areas show higher station usage, possibly due to limited transportation alternatives.

* Rider behavior varies throughout the month, with longer rides in the first half and increased ride frequency in the second half.

* Gender influences ride patterns, with women taking longer rides on average.

## Spotlights

Rider behavior varies based on the time of the month:

* First half: Both customers and subscribers take longer trips on average.

* Second half: Number of rides increases, especially for subscribers.

* Subscribers may be motivated to maximize their subscription value.

* Gender influences bike usage: women, on average, take longer rides than men.

* Although there are more male subscribers, women's ride lengths stand out, impacting the overall dataset.

## Recommendations

To boost membership and ride usage, the city can:

* Promote deals for becoming a subscriber, focusing on popular stations.

* Tailor marketing efforts based on target audience and gender.

* Highlight leisure benefits for female riders and accessibility advantages for men.

## Future Work

* Investigate how gender affects ride frequency at the start and end of the month.

* Explore age-related impacts on dataset trends.

* Examine bike utilization further, especially the bike with ID 15259, which stands out in terms of usage.

## Tools Used

* Tableau Public

* Data cleaning and preprocessing tools (e.g., Python, Excel)

### How to View the Project

[Visit Tableau Public Link](https://public.tableau.com/app/profile/canayter/viz/module18-tableau/CityBikeAnalysis)

* Navigate through the interactive dashboards

* Explore individual visualizations for detailed insights

## About the Author

Can "Jon" Ayter

* [GitHub Profile](https://github.com/canayter/)

* [LinkedIn Profile](https://www.linkedin.com/in/canayter/)