Bikesharing_Visulization

Bikesharing visualisation with NYC CitiBike data

Overview

Purpose of this analysis is to present a solid business proposal to key stakeholders with a review of a bike trip analysis for New York City using Tableau and present it to key stakeholders for a bike trip analysis. Data will have type modification using Python in Jupyter Notebook

Results

The analysis results can be view from Citi Bike Story

• Citi Bike Locations The visualisation tells us the active starting and ending locations are almost the same in Manhatten. It gives us an indication whether there might be an excess or shortage of bikes at particularly stations over time.

• Gender Breakdown The pie chart shows that male represents a majority of 65,27%. The usage might be the same for both male and female.

• Trips by Gender This heatmap shows that the useage pattern is true regardless of gender, although the total numbers for males is much higher.

• User Trips by Gender during weekdays This heatmap demonstrates the subscribers are mostly male and account for most of the weekday activities. The useage on the weekend is more evenly split between subscribers and customers.

• Checkout times by Gender We can visulise that the pattern is the same regardless of gender.

• Ave trip duration We see most rides are under 20 minutes because of the real estate density in NYC, and that the vast majority of rides are less than one hour.

• Weekday trip per hour It shows a clear pattern of bicycle useage during the weekday morning and evening commute hours (7-9 AM and 4-7 PM).

Summary

In conclusion, bikeshare services are very popular in busy metropolitan areas like NYC with dense population and scarce parking spaces. The program is funded by subscribers, mainly male. More outreach should be done to attract female riders. The main bike usage is focused around morning and evening commute hours.

If I were to pursue additional lines of inquiry for analysis and visualization with the data provided, I would consider a deeper dive into:

• trip starting and ending locations during weekday rush hours to display the flow of traffic between neighborhoods;
• average trip duration, by birth year, by gender, to explore if there was any difference in male or female or un-gendered riders according to their age.