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The Berka Dataset Visualisation

An anonymised dataset from a Czech bank, known as the Berka Dataset was downloaded from here. This dataset was prepared by Petr Berka and Marta Sochorova. The data about clients and their accounts include:

  • Account details
  • Client details
  • Disposition details; which client has the right to operate an account
  • Standing Order details
  • Transactions details
  • Loan details; each loan is linked to an account
  • Credit Card details; each credit card is linked to an account
  • Demographic details; characteristics and statistics for a district

Each of the above is stored in a separate CSV file, which was loaded to PowerBI. Data cleaning was performed on various relations, mainly translating Czech to English, and date conversion. The relationships between each table were then identified. More information about this dataset can be found on data.world and here.

A report with a number of pages highlighting some aspects from each relation was then created.

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MIA Passenger & Aircraft Movements Insights

After publishing our BI dashboard tool based on the MIA public data set we would like to provide an overview of the data cleaning process involved and some interesting insights.

Data Cleaning

The available data from the respective websites (MIA and NSO) is in PDF and XLS formats and was first migrated to an SQL database. Some exploratory analysis was made and it was found that across different years some variations in the same airlines, cities, countries, and airports were found and needed to be addressed. In some cases, such as in Air Serbia, there were companies that re-branded and changed the name, this required some research and consolidation. In two particular cases, there were errors in summations and thus we double checked and made our own summations. Therefore, some yearly and monthly summations were calculated rather than quoting them as is from the reports. In some cases, the values for some months were missing but the yearly total was provided, we, therefore, distributed the difference across the missing months since there were only 2 cases.

Insights

After working on this tool and publishing it we would like to draw attention to a few interesting patterns.

Passenger Movement

  • Comparing the passenger movements of Gatwick vs Heathrow, one can see a steady flow of passengers with a radical shift from Gatwick to Heathrow from 2008 to 2013.
  • There is a drop in passenger flow to Tunisia and Egypt during the Arab Spring, but a steady flow to Libya during the same period.
  • A surprising drop in passenger movement in recent years towards Norway and Russia.• A steep rise in movements to Turkey since 2012.

Aircraft Movement

  • Surprisingly, there is a drop in movements between 2007 and 2010 (financial recession) which is not reflected in size for passenger movement.
  • Another drop in movements can be observed between 2010 and 2012, which once again is not reflected in passenger movement.

Market Share

  • A drop of 1% in market share for Air Malta in aircraft movement with a constant market share in passenger movement.
  • Ryanair invested heavily in increasing the market share of aircraft movement which led to surpassing Air Malta in passenger movement market share.

Passenger to Aircraft Ratio by Airline

  • Even though Air Malta has been increasing the ratio of passengers per aircraft, it is clear that easyJet, Lufthansa and Ryanair have a more efficient model with a 20% higher ratio. This means that on average, the latter airlines have a 20% higher occupancy rate than AirMalta.
  • Alitalia had a similar model to that of Air Malta with a notable improvement from 2011 to 2014, then joining the other airlines in 2016 with a 20% higher ratio than Air Malta.

Passenger to Aircraft Ratio by Country

  • The UK has a high passenger to aircraft ratio as expected.
  • Norway had a similarly high ratio yet movements ended in 2016.
  • Greece and Cyprus have a very poor ratio and thus the aircraft movements can be reduced.

Disclaimer

We would like to note that the data is as found on the MIA and NSO sites and ensquad Ltd. does not take responsibility for the correctness of said data. We would also like to note that this tool should not be considered in isolation, meaning that no concrete conclusion can be made without further investigation. Should you have any queries feel free to contact us on info@ensquad.frankieinguanez.com

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MIA Passenger & Aircraft Movements Dashboard

We are proud to launch a free public Business Intelligence (BI) dashboard based on the Malta International Airport (MIA) public data of aircraft and passenger movements. This showcases the importance of data visualization, which leads to insights and reflection to ask very valid questions. In this article, we shall go through the different sections of the tool and in a subsequent article, we shall give an overview of the cleaning process and some insights.

The report contains passenger and aircraft movements from MIA between 2005 and 2016 with the possibility of filtering by Year, Country, and Airport. The Airline market share is available from 2004 until 2016 with the possibility of filtering by Airline and Year. The passenger to aircraft ratio is a metric that measures the aircraft occupancy rate, and we are providing it for the top 5 airlines with the possibility of filtering by year. The same metric is also provided for 5 airlines with the possibility of filtering by year and country.

Continue reading part 02 for insights from the dashboard.

Disclaimer

We would like to note that the data is as found on the MIA and NSO sites and ensquad Ltd. does not take responsibility for the correctness of said data. We would also like to note that this tool should not be considered in isolation, meaning that no concrete conclusion can be made without further investigation. Should you have any queries feel free to contact us on info@ensquad.frankieinguanez.com