Preface
About a century after the invention of powered flight, aviation has slowly become a vital element of everyday life. While pioneers and flying aces build the collective imaginary around the early days of aviation, technical advances around surveillance systems, the use of radar in civil aviation in the 1950s, the generalisation of GPS for civil applications in the 1980s and the ADS-B mandate emerging in the 2000s make the use of data in aviation an interesting field of research for many disciplines. In particular, such effort has been justified by the historic growth of traffic from the early 2000s, and new challenges such as world-wide crises, pandemics or new unmanned technologies.
Aviation and air transportation are data-rich environments. At the very start of each aircraft, it comes with its own design information and performance data. During flight operations, it can collect several gigabytes of raw data per flight including trajectory data and sensor information. Beyond the aircraft itself, information regarding procedures, flight tables, surveillance states, and weather reports are also constantly being generated and aggregated.
Traditionally, open data has not been a well adopted concept in the aviation industry. The availability and sharing of data on a global scale and with a varied community of researchers and practitioners is limited. Such a lack of transparency hampers the industry as a whole, limiting its efficiency and sustainability.
In recent years, the open data philosophy is gaining ground within the aviation research community, primarily thanks to the wide adoption of Automatic Dependent Surveillance–Broadcast (ADS-B) technology. Data sharing within the aviation industry has also been identified as an enabler for a more rational use of resources. With lower cost of storage devices and more convenient internet access, large open data has become one of the strong foundations for researchers, and a gold mine of information for the passionate.
In such a Eureka moment in open aviation science, four aviation enthusiasts with different backgrounds come together and present this open book. This book presents the ecosystem of common data formats used in aviation. It takes the readers onto a data journey, with a strong focus on open access. With a little bit of programming knowledge and aviation background, this book also presents insights of data mining and visualisation techniques that convey a colorful story of aviation.
Who is this book for?
This book was written for graduate students, academics, scientists and analysts addressing data based aviation research. This includes questions related to aviation data science, aircraft performance, environment impact, economic analysis, and more. A basic set of skills in one programming language commonly used in data science is required: in its current form, the book covers Javascript, Python and R. The book will give the reader a comprehensive overview on common aviation data formats, data sources, and a decent command in the language of her choice to address data parsing, data analysis and data visualisation techniques.
Do not expect to find in this book a crash course in Python, R or Javascript.
If you are passionate about aviation, some chapters may be of interest, but you should get proficient in basic programming to enjoy the full content.
How to get a copy of this book?
The book is designed as an online book, edited with TU Delft OPEN Publishing, and is made available online https://aviationbook.netlify.app/ all along the writing process. Stable outstanding versions will be tagged, marked with a DOI and made freely and openly available as web versions, printable PDF and ebook documents.
The content of this book is licensed as Creative Commons CC-BY-NC-ND. This license lets you download the book and share it with others as long as you credit the authors. You are not allowed to change the content in any way or to use the text for commercial purposes.
How is this book organised?
Part 1. Background knowledge about aviation
This part brings in the minimal background necessary to comprehend the aviation world, including vocabulary and historical aspects which led to the current situation.
Part 2. The ecosystem of aviation data
This part goes through all the most commonly used data formats in the aviation and ATM data analysis community.
Part 3. Process Data
This part introduces mathematical and programming skills. The tidy paradigm to manipulate data frames is introduced. Challenges associated with geometrical shapes, geographical coordinates, trajectories, projections are presented, before introducing common AI tools for information extraction, prediction and optimisation.
Part 4. Visualise Data
This part turns to the data visualisation aspects. It explains how to choose the most appropriate tool to convey a message with particular focus on geographical information.
Part 5. Share Data
The most overlooked aspect of data analysis probably turns around data sharing. Data curation is often a very time consuming process and enriching data by labelling specific tags or merging several sources of information brings additional value to a dataset. This part deals with the data sharing and publication process. (paper reproducibility?)