Check out my Story at my Tableau Public Website here.
As the new lead analyst for the New York Citi Bike Program, I am now responsible for overseeing the largest bike sharing program in the United States. In my new role, I am expected to generate regular reports for city officials looking to publicize and improve the city program.
Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the Citi Bike Data webpage.
However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program.
As my first task is to build a set of data reports to provide the answers to several questions by structuring and analyzing the data with the following criteria:
Aggregate the data found in the Citi Bike Trip History Logs and find unexpected phenomena.
Designed 15 visualizations for each discovered phenomena, I was on fire!.
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Time span chosen: January to November 2018-2020
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Built my Data set on jupyter notebook, where I performed Data cleaning, edition and concatenation.
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I merged multiple datasets from different periods, following the criteria to build one month csv with 3 years each one, summarizing a total of 11 csv's containing one month data from 2018 to 2020.
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I standardized the column headers and data types. Combined and created Data frames, rename columns, convert date times and create new csv's.
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Then, in Tableu, I performed the Union of the 11 csv's in a way that only add up the rows, but not add columns.
The following are some, from other questions I tackle:
- How many trips have been recorded total during the chosen period?
- How has the proportion of short-term customers and annual subscribers changed?
- What are the peak hours in which bikes are used during summer and winter months?
- Today, what are the top/bottom 10 stations in the city for starting a journey?
- Today, what are the top/bottom 10 stations in the city for ending a journey? (Based on data, why?)
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Today, what is the gender breakdown of active participants (Male v. Female)?
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What is the average distance in miles that a bike is ridden?
- How effective has gender outreach been in increasing female ridership over the timespan?
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How does the average trip duration change by age?
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Which bikes (by ID) are most likely due for repair or inspection in the timespan?
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How variable is the utilization by bike ID?
AMONG MANY MORE VISUALIZATIONS!!...
Next, as a chronic over-achiever:
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I Used my visualizations to design a dashboard explaining the Citibike behaviour and trends with the Covid impact on it's performance, comparing 2020 with the two previous years.
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The dashboards are accompanied with an analysis explaining why the Covid has impacted, positively and also negatively to the riders, and how has changed the user type - age - gender mix of the bike riders.
Performed different visualizations:
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Basic: A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top.
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Advanced: A dynamic map that shows how each station's popularity changes over time (by month and year). Again, with zip code data overlaid on the map.
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The map I choose also contains write-up unveiling trends that were noticed during my analysis.
Finally, create my final presentation
- Created a Tableau story that brings together the visualizations, requested maps, and dashboards.
As my audience will be city officials, public administrators, and heads of New York City departments, My data and analysis will be presented in a way that is focused, concise, easy-to-understand, and visually compelling.
My visualizations contain trends, insights and recommendations, enough to be included in press releases.
My final submission includes:
- My Tableau Public link to my workbook that includes:
- 15 Total "Phenomenon" Visualizations
- 7 Dashboards
- 2 City Official Map
- 1 Story
- A text or markdown file with my analysis on the phenomenons I uncovered from the data.
In order to share my work, I saved as a .twbx file so that your TA's can grade them.
To save your workbook as a .twbx file (just selected "Save As..." from the "File" dropdown. Then, select the .twbx option)
Final product will be assessed on the following metrics:
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Analytic Rigor
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Readability
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Visual Attraction
Tableau Destination Map with more than one stop
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Combined each of the CSV files and manipulate it with my jupyter notebook to have the data as good as needed to perform a great analysis. I Dig for an approach that worked and just go with the outcomes I was looking for!
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Adjustede the CSV formats, since I noticed not all of them was standardized. I found subtle changes to the formats in the columns that can blockade my analysis.
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I ensure my data was consistent and clean throughout my analysis.
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First, I built my visualizations with small extracts of the data (i.e. single files cotaining one year and one month) before attempting to import the whole thing.
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Was VERY CHALLENGING to import 11 ,months from the complete 3 years (2018 - 2020) I found myself with performance issues such as reading slow, but most of all, not reading the complete data set!
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I accompanied my data visualizations with clear and directed answers and analysis.
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I look to answer each question, in a way that is concize, organized and presentable.
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I spent quite a good time thinking through decisions on color schemes, fonts, and visual story-telling. I look for ways to have your data visualizations match their aesthetic tones and changed formats in many times :D
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I create calculated fields to get the information I needed. Worked with LOD and found I need more time to become expert on the theme!
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I considered adding other pieces of information on socioeconomic or other geographic data. I used the Tableau map "layer" to feature extra information.
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I look for new tricks and tips from Youtube to perform some aggregate functions in Tableau. Explored new concepts and push my creativity the most!
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Thanks for the challenge!