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Extracting and Transforming CitiBike Data for Analysis

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bikesharing Project

Overview

The framework for this project was to analyze New York CitiBike data for a presentation to potential investors who would be interested in starting a similar bikesharing business in Des Moines, Iowa.

In a calculated effort to explore the viability of starting a similair bike sharing business, I created an analysis that answers a few key questions:

- Who uses the CitiBike program?
- What area of the city sees the most usage?
- What time of day are the bikes used the most and the least?
- How much are the bikes used and by whom?

Results

A Tableau story has been created and can be seen at this link.

Although Des Moines demographics differ from New York Citie's, analyzing Citibike's August 2019 data providded much needed insight.

Customer_breakdown

  • There were approximately 2,344,224 total rides completed in August 2019.
  • 81% of the users were subscribers while the remaining 19% of users were customers
  • 65% of the users were identified as MALE, 25% were FEMALE, and the remaining 10% of users are labeled UNKNOWN.

Top_starting_locations

The above image displays the count of rideshares started in a given area. The size of the circles and darkness indicate the relative number of trips started in that area. Data suggests a large majortiy of all rideshares started in Manhattan. This makes sense given its population density, tourist/entertainment value, and concentration of commercial activity.

Peak_hours

This chart displays the total riders per hour of the day. We can see peak usuage is during typical "commuting" times of 7am-8am & 5pm-6pm. This chart also identifies optimal times on when to perform routine maintenance on the bikes so that revenue opportunites are not missed.

Checkout_Time_for_Users

This graph shows the number of rides by duration and it shows a majority of trips taken are under an hour in length.

Trips_by_Weekday_per_Hour

Above is a heatmap that shows weekly usage patterns. It supports earlier data that suggests heavy bike usage during weekday commute times.

Trips_by_Gender_wkperhour

This heatmap is similar to the one above it however it provides an additional layer of analysis by showing the weekly usage patterns by gender.

User_Trips_by_Gender_by_Weekday

Lastly, this heatmap further reinforces the trend and higher demand of of MALE users.

Summary

In conclusion, bikeshare services like CitiBike are very popular and meet a strong demand of convienent and cheap transportation in a large metropolitan area. The user base is made primarily of male subscribers. CitiBike has a large growth opportunity infront of them if they can grow their FEMALE user base.

Lastly, additional analysis could be beneficial by:

  • comparing data from different months such as May & July.
  • including weather data to find a correlation of ride demand based on weather performance.

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