AIRLINE DELAY ANALYSIS WITH TABLEAU

Airline is the fastest, safest, and most convenient transportation that we have today. However, I believe, besides all the positive aspects, we all had some experience about airline delays. So, I analyzed the data of airline delays in 2008 and found out some interesting relations and trends.

Here is the link to the data I used:

http://stat-computing.org/dataexpo/2009/the-data.html

Here is the link of my final analysis of this project on Tableau:

https://public.tableau.com/profile/xinyin.wang#!/vizhome/AirlineDelay_0/Presentation

Before I started my analysis, I had a couple of questions in mind that I wanted to include in my analysis:

  • What is the percentage of delay airlines and on time airlines in 2008?
  • What are the performance of all the carriers and airports in 2008?
  • Which day of month or day of week had the most delays in 2008?
  • What are the reasons of those delays?

First of all, since I only wanted to know the airlines that had delay in 2008, so I created several calculations that only keep track on the delayed airlines.

To begin with the analysis, I made a summary dashboard that shows the overall performance of all airlines in 2008. I also made three bar charts that including total delays, average delays, and percentage of delays by each carriers. The reason why I used three ways to measure carriers’ performance was because I believe that will give me an objective conclusion.

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The second dashboard is the percentage delay of each origin airport and destination airport. The bigger and redder the spot is, the higher the percentage of delays were in that airport. Here I also created a parameter that applies for both maps. Viewers can change the percentage range that they want at the right corner.

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The third dashboard is based on delay by month, delay by day of month, and delay by day of week. Here I used dual axis function that combined bar chart and line chart into one single chart. Based on this dashboard, viewers can see the number of airlines in a certain period of time and the percentage of delays in a certain period of time.

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The fourth dashboard shows delay by different times during a day. Viewers can see the number of airlines and the percentage of delays in a certain period of time during a day. As you can see 3 o’clock to 5 o’clock in the morning had the lowest numbers of delay and 8 to 9 at night had the highest numbers of delay. However, as you can see line chart on the bottom, even there were only limited numbers of airlines in the early mornings, it had a very high percentage of delays.

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The final dashboard shows viewers the reasons of delay. The first bar chart on the left shows the 5 reasons that caused all those airline delays. Apparently, late aircraft delays were the majority delays were happened in 2008. The pie charts in the middle shows that if an airline got delayed, almost 69% of them had depart delays. However, if an airline arrived on time, almost 84% of the airlines had non-depart delay. Also, the scatter plots chart on the bottom left shows that the more depart delay an airline had, the more arrive delay were happened. Last but not the least, I found that a diverted airline always arrived on time. There were around 17 thousand airlines changed their routes, but all of them arrived on time. However, there was no delayed airline changed its route.

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Overall, based on the analysis that I made for the airline delays in 2008. Here is my conclusion: airline delay is a big issues in 2008. The majority reasons are late aircraft delay, National Airspace System delay, and carrier delay. More importantly, depart late and aircraft condition were the main reasons caused those delays.

About Henry W

Data Analyst at Novedea Systems, Inc.

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