After reviewing the flaws of the previous visualization of the DOT Airline performance data in part 1, I created an improved version with the same recordsets. It is a separate viz because the first version have some mistakes due to the number conversion during the csv import. I cleaned up, checked the data and used calculated fields to derive the sum of delays.
Airline Performance in the US 2015
The basic concept is still the same, the matrix on the top left controls the dashboard, initially you see all data for 2015 combined, clicking into cells drills down.
I changed the barchart to stacked bars comparing total to delayed flight in one bar for each month.
I moved the split delay reasons into a separate bar chart and added a pie chart which reveals the main reason for delays (surprisingly weather and security have the smalles share!) The 2 lists are a Top 10 style lists highlighting the airports and airlines with the most delays.
How does the visualization transport information ? Let’s look at the strong and weak points of the second iteration.
+ The key information presentation is improved. We can see the viz is about delays.
– The dashboard starts to look a bit disorganized and the viewer eyes are moving around without a centre of attention.
+ The barchart now makes sense, you can compare total flights and delays.
– The detail delay reason over time does not create too much value as the distribution of reason is quite similar.
Conclusion: Spending more time on both data and visualizations improved the overall impact, though a bit cluttered.
Going beyond sample datasets and basic visualizations I was looking for open data in my professional domain, the aviation and airport industry. Potential candidates for visualizations are connections, routes, flight plans, airport and airline performance. Performance is usually the comparison of scheduled operations vs. actual milestones. The delay of arriving or departure flights is not only affecting passengers and many parties inside and outside the airport community, but it is driving sentiments, perception and reputation and eventually costs money. This kind of data is not something operators like to release but thanks to the Freedom of Information Act (FOIA), a US Federal law, public gets access to all kind of statistics. From the US DOT (Department of Transportation) you can access and download a variety of datasets, one of them is the On-Time Arrival Performance of US airlines in the US and their delay causes since the year 2003 (link). You can filter by airline, airport and timeframe, review the summary on the DOT website or download the set as CSV for your own analysis. I downloaded the complete dataset for 2015, a 2,25 MB file with roughly 13.500 records.
Arrival Delays in Tableau
Airline Delays in the US in 2015 by DOT
It provides total arriving flights, cancelled and diverted flights, the delay count and total time by reason (weather, carrier, NAS, security, late aircraft) for each month-airport-airline combination for 14 carriers at 322 airports.
WhatsApp known for its massive security issues, still used by millions of people as a free replacement of SMS and MMS, was acquired by FB, one (maybe the) biggest data harvester in the internet. I dont use FB, the acquisition is a reason to finally move on to another more secure communication tool: Threema (Made in Switzerland app with end-to-end encryption). Hope they wont sell privacy for money. Please help to spread the word.
It is NOT free, but is time to understand FREE comes at a price !