Tableau: US Domestic Flight Cancellation, Delay and Diversion Analysis (2015)
This is a Tableau Project published on my Tableau Public (Click here to view).
The goal of this project is to analyse the reasons of US flight cancellation, diversion and delay in 2015.
This data comes from a Kaggle dataset, it tracks the on-time performance of US domestic flights operated by large air carriers in 2015.
flights.csv
: 274,964 flight records (rows) * 33 columnsairports.csv
: airports’ informationcancel_reason.csv
: full-name of cancel reasonsairlines.csv
: full-name of airlinesThere are three reasons for flight cancellations:
The top 3 airlines that have the most cancellations are:
However, the top 3 airlines with the highest cancellation rate are a bit different:
Bad Weather has led to most cancellations, but the row data does not show whether they were affected by the weather of the departure or arrival airport.
This map only shows the departure airports of those cancelled flights. We can find that the flights departure from
Southwest Airlines Co. had the most diverted flight (181), which is almost two times than the second most Atlantic Southeast Airlines (99).
However, the diverted rates of most airlines are similar.
The flights towards
The top 3 airport that had the longest delay time due to security reasons were:
The trends were similar for both of them. There were the least cancellations, diversions delayed time in September 2015, followed by April. February and June were bad travel time in 2015.
Saturday would be great for travel, while those who flighted on Monday had a larger chance to have bad experience in 2015.
In terms of maps, I found it might not be easy for readers to quickly get information regarding the numbers, but only using bar chart would lose important geographic information (for example, the map of cancellations for weather is similar to the one of destination of diverted flights). Thus, I put the bar chart at the bottom of each map for users’ convenience. The bar chart and map share a filter.
Thought bar chart is more accurate, I found it would be boring to always use it. Thus, I used bubble chart to show less important information (e.g. diverted rates).
5 reasons of delay are shown in row data. While weather and security delays might tell us which airport we could use less, the airline reasons would help us choose flight when planning a trip.
Some fly-in-and-fly-out persons fly on Monday and come back on Friday. Thus, I decided to explore whether there was a rule of the delayed flights during a week. Line Chart could provide a clear view of this rule.