Visualization was generated as a part of the r/dataisbeautiful August 2018 competition. Data was obtained from Department of Homeland Security, and thanks to reddit user gi_funk for giving a head start parsing the PDF files. The TSA appears to have switched their list of categories they use to describe the items between 2009 and 2010. To handle this I manually created a mapping from each of the old categories to the current categories such that all claims could be categorized in the new system. It should be noted that some claims can have multiple categories, likely because multiple items get damaged/lost. That's fine! The next stage was to aggregate the claims by airport, and join in some airport metadata like coordinates as well as yearly passenger count With everything neatly organized by airport, we're ready to create a map. I wanted to make sure I captured the idea that the passengers traveling via the airport are not just "at the airport" they're going to/from the areas surrounding it. For this (as well as aesthetic) reason I decided to use a heatmap rather than a bunch of dots. In order to implement the heatmap effect, I needed a way to interpolate the value at all the non-airport points. I wanted to capture the fact that travelers in a given area will likely use one of the handful of "nearest" airports, but were also more likely to go to the closest and biggest one. As such, I implemented a weighted average according to YearlyAirportPassengers/Distance2 The heatmap was then rescaled from absolute quantities to be % Difference from the national level. Code can be found in full on github.