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This project is to use Tableau to visualize the usage patterns of Divvy bikes in Chicago. By analyzing the trip data provided, we can gain insights into when, where, and how bikes are being used. This information can be useful for Divvy and the City of Chicago in planning future bike infrastructure and promoting sustainable transportation options.

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Divvy Analytics for Bikes Trips duration using Tableau

Introduction

The goal of this project is to use Tableau to visualize the usage patterns of Divvy bikes in Chicago in 2016. By analyzing the trip data provided, we can gain insights into when, where, and how bikes are being used. This information can be useful for Divvy and the City of Chicago in planning future bike infrastructure and promoting sustainable transportation options.

Tableau Link -

https://public.tableau.com/app/profile/vishesh.gupta1641/viz/DivvyStoryPoints/Whoaretheriders

Data

The data provided contains information on each trip taken on a Divvy bike in 2016, including the trip start and end time, the starting and ending station, and the rider demographics. The data has been pre-processed to exclude trips without a start or end date.

Data Analysis

• Trip duration: Analyze the distribution of trip durations to determine the average and median trip lengths.

• Station usage: Determine the most popular starting and ending stations by analyzing the number of trips originating or terminating at each station.

• Time of day: Determine the busiest times of day by analyzing the number of trips starting or ending at different times.

• User type: Compare the usage patterns of Customers and Subscribers to determine the difference in their usage patterns.

• Gender and age: Analyze the gender and age of Subscribers to determine any demographic trends in bike usage.

Visualization

Tableau will be used to create a series of interactive dashboards to visualize the data. Dashboards will include:

• Map of Chicago with starting and ending stations highlighted

• Histogram of trip duration

• Bar charts showing the number of trips starting or ending at each station

• Line charts showing the number of trips starting or ending at different times of day

• Bar charts comparing the usage patterns of Customers and Subscribers

• Bar charts showing the gender and age distribution of Subscribers.

Conclusion

By using Tableau to analyze the Divvy bike trip data, we can gain valuable insights into how bikes are being used in Chicago. This information can be useful in planning future bike infrastructure and promoting sustainable transportation options. The interactive dashboards created will allow users to easily explore the data and uncover patterns and trends in bike usage.

About

This project is to use Tableau to visualize the usage patterns of Divvy bikes in Chicago. By analyzing the trip data provided, we can gain insights into when, where, and how bikes are being used. This information can be useful for Divvy and the City of Chicago in planning future bike infrastructure and promoting sustainable transportation options.

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