This project was completed as a requirement for the course, Data Visualizations with Python, by CareerFoundry as part of their Data Analytics Certificate Program.
You are the lead analyst for a bike-sharing service based in New York City, USA, which also provides service in Jersey City. Your team has been tasked with analyzing user behavior to help the business strategy department assess the current logistics model of bike distribution in Jersey City and identify expansion opportunities.
The project’s objective is to conduct a descriptive analysis of existing data and discover actionable insights for the business strategy team to help make informed decisions that will circumvent availability issues and ensure the company’s position as a leader in eco-friendly transportation solutions in the city.
For this project, you’ll be using public data from New York bike-sharing facilities operated by Citi Bike. For context, Citi Bike’s popularity has increased since its launch in 2013. The company’s marketing strategy promotes bike sharing as a sustainable and convenient means of transportation, which has been very successful. Since the Covid–19 pandemic, Jersey City residents have found even more merit in bike sharing, creating higher demand. This has led to distribution problems—such as fewer bikes at popular bike stations or stations full of docked bikes, making it difficult to return a hired bike—and customer complaints.
The task is to diagnose where distribution issues stem from and advise higher management on a solution based on your diagnosis of the root of the problem—whether it’s sheer numbers, seasonal demand, or something else. Being in a management position also makes you the bridge between divisions, which requires you to ensure the information is tangible for the business development team. To effectively communicate your analysis to non-analysts, you’ll present your insights in an interactive dashboard depicting the metrics you identify as vital for tackling the distribution issues.
Skills: Sourcing Data with API Key, Data Visualization with Python, Geospatial Plotting, Creating a Dashboard
Python: pandas, numpy, os, beautifulsoup, streamlit, pillow, kepler.gl, matplotlib, seaborn, pyplot, numerize
Open Source Data from Citi Bike's Database: https://s3.amazonaws.com/tripdata/index.html
Weather data using NOAA's API Service: https://www.noaa.gov/
The following are a list of initial questions to guide the analysis. Any unanswered questions are a potential direction for further research.
1a. Import all files for NY Citi Bike trips (Jersey City only - data for NYC is on a separate file that was not used for this project)
Dashboard: http://localhost:8501/
There is an obvious correlation between the rise and drop of temperatures and their relationship with the frequency of bike trips taken daily. As temperatures plunge, so does bike usage. This insight indicates that the shortage problem may be prevalent merely in the warmer months, approximately from May to October. Below are more specific initial insights:
1. There are noticeable points of drops in bike usage. The first in early October; followed by a more signifcant drop in mid-November, and again in mid-December (into the holiday season).
2. There are individual days of particularly low bike-usage that don't seem to correlate with temperature. The two lowest days for bike usage in 2022 are January 29th (a Saturday) and December 25th. Other noticeably low-use days (in relation to their particular time period) are March 12th (Saturday), May 7th (Saturday), September 6th (Tuesday), and October 3rd (Monday). Further research recommended to find reason for these 'isolated dips.'
There are noticeable points of rises in bike usage in mid-February, mid-March and mid-April. Peak usage is reached in May and is sustained until October.
There are three main hubs for bike usage (which are visible by observing the 'route arcs'). 'Hub 1' around in the area of Hoboken Terminal and Yards/Stevens Institute of Technology (parallel to the Meatpacking District in Manhattan); 'Hub 2' south (slightly west of what is labeled Downtown Jersey City); and smaller 'Hub 3' furhter west (near Indian Square). I will focus my insights mostly on Hub 1 and 2. There is also a much smaller Hub 4 in downtown Jersey City, by the water (parallel to Battery City Park in Manhattan).
