Abstract

Aviation safety data are limited in availability due to their confidential nature. Some aggregated overviews already exist but in order to effectively use the data, it is important to fill the gaps of their existing limitations. For some data, there are not enough data points in order to process them through advanced analysis. For other, only expert assumptions can be obtained. In both cases, these shortcomings can be addressed via proper data resampling or simulation where little effort can make the data suitable for various research and development initiatives. Examples of real aviation safety data made public are demonstrated together with key principles of how to perform their resampling. Then, for cases where only expert assumptions are available, general solution to the transformation of the assumptions into simulated data is introduced. The goal is to demonstrate how to transform accessible data or knowledge about aviation safety into data samples with sufficient granularity. The results provide general solution suitable not only for aviation safety data and knowledge, but also for similar transportation or high-risk industries related data issues, indicating that both the data resampling and simulation provide an option for generating datasets, which can be used for statistical inferential methods, linear regression modelling, recurrent analysis etc. Example of data resampling application is included in Aerospace Performance Factor calculation for years 2008 up to 2015. Aviation safety data comprise accidents, incidents and safety occurrences. The data are available in form of aggregated figures denoting number of observations of respective accident, incident or occurrence during given time interval. Airplane crashes are among the most destructive accidents causing 400-600 loss of life. The data of aviation safety thus fuels some prevention in the accidents and fatal incidents caused due to various reasons by analyzing the data and creating a story out of it. This study focuses on the overall Airport Operational Performance of various airports Worldwide. At the time one of the Dataset used for this analysis was created in Kaggle (2016-09-09), the original version was hosted by Open Data by Socrata at the at: opendata.socrata.com/Government/Airplane-Cr.., but unfortunately that is not available anymore. The dataset contains data of airplane accidents involving civil, commercial and military transport worldwide from 1908-09-17 to 2009-06-08. The study focused on measuring the operational performance of all selected airports.

Note: The dataset required could not be sourced as it was required and the guided insights could not be fully answered due to the short timing to get the data and work on the project, aviation data being very scarce and not so open sourced, however we found two that we could work fairly with and derive some data driven as well as data informed insights.

TABLE OF CONTENTS

1. Introduction

2. Data Wrangling, Data Cleaning and Data Modeling

3. Exploratory Data Analysis/Data Storytelling and Visualization

4. Conclusion

5. References

Introduction

At the very beginning of 20th century, Air industry started to aggressively compete with one another. Not only Airline Company, but also Airport itself tried to play against others to survive in the rival. Airport is the place that the entire journal begins. It plays an important role to deliver one of the most essential transportation modes around the world. However, in order to be competitive, Airport aims to have an efficiency and effectiveness in all kind of activities. Passenger terminal, runways, employees, check-in desks etc. are responsible for producing a better higher service quality than other potential airports. To have incomparably satisfied customers, airport would have to supply sufficient runways and terminal capacity to stay away from the delays at even the demanding period.

To ensure that the airport will have the superlative performance, as well as, can generate profitability, Airport industry may needs to constantly monitor their performance, in order to increase the market force and more than that a competitive.

The competitive environment of the aviation industry basically is about greater market mobility and the freedom to link and align. A great deal of effort and resources have been broadened in developing a performance measurement for carriers in the different modes of transportation. The purpose of this study is to produce an inclusive evaluation from sources of efficiencies related to operation scale and input deployments that contribute to the output of an airport. The supporting tool used in this study is Data Envelopment Analysis (DEA). It is workable to reveal the best link between airport infrastructure and passenger movement.

Data Wrangling and Data Cleaning

We sourced data from kaggle as well as Aviation Safety database where we got the two datasets that were used for the analysis. We imported the data via Excel('xlsx'), after which we transformed and cleaned in power query editor in PowerBI. Columns that did not have any impact in our analysis were removed, values were replaced(null and blanks), columns were renamed, datatypes were changed, relationships were created and duplicates were removed. We made use of two datasets namely "Airplane Crashes Since 1908" which contains Aviation Safety Incidents data set since 1950 and "ASN Aviation Safety Data" which contains Aviation Safety Incidents data set since 1950.

Exploratory Data Analysis/Data Storytelling and Visualization

Analysis 1(Total Incidents)

This shows the total incidents in the various airports over the years.

Analysis 2(Total Deaths On Ground)

This shows the total number of deaths that occurred on ground.

Analysis 3(Total Aboard)

This shows the total deaths that occurred aboard the aircraft.

Analysis 4(Total Fatalities by Accident Category)

This shows the total fatalities that occurred by the various type of accident category across the years.

Analysis 5(Total Incidents by Operator Type)

This analysis showing the two operator type(Military and Commercial) shows that commercial operators has more incidents(80.46%) as compared to the military(19.54%).

Analysis 6(Total Aboard and Total Fatalities by Operator)

This shows the various percentages of the Total Incidents aboard and Total Fatalities across the various operators.

Analysis 7(Average Fatalities by Location)

Across the Various locations, this shows the Average Fatalities that occurred filtered down to the top ten locations.

Analysis 8(Total Incidents by Year)

This tracks the Total Incidents that occured across the various years.

Analysis 9(Year Slicer)

The year slicer filters the visuals according to the various specific years.

Airport Operational Performance Analysis Dashboard

Conclusion

This analysis shows the various airport performances as regards to safety and occurrence of incidents across these airports. We were able to descriptively analyse our dataset such that we could see the various patterns on incidents whether on ground or aboard; and as well determine the level of fatalities as it relates to the accidents categories, different operators and then locations. Hence future preparation can be made through the insight gotten to prevent occurrence of such incidents and ensure safety in the various aiports.

References

kaggle.com/datasets/ahmedabdelmageed/asn-av.. kaggle.com/datasets/saurograndi/airplane-cr.. opendata.socrata.com/Government/Airplane-Cr.. aviation-safety.net/database researchgate.net