In the past few years, the frequency of onset natural disaster has been rising, causing significant damage to communities and infrastructure around the world. When it happens, airports in the region affected have to rapidly adjust and evolve from serving regular passengers to becoming a humanitarian hub, that handles a massive surge in both passenger, but especially cargo handling. A number of regions are especially vulnerable and prone to experience such a devastating event, and while there are existing initiatives aiming to raise awareness and improve airports’ preparedness, authorities are often isolated in their efforts to become more resilient. This research is an exploration of how data science and novel machine learning algorithms could help in establishing a base for forming collaborations between airports that might face similar challenges when it comes to disaster preparedness efforts. The goal was to build a comprehensive data set describing airports from the perspective of their disaster preparedness and find similarities between them, based on their intrinsic sociotechnical features, so that perhaps an airport in Indonesia could be matched with its ’sibling’ airport in the Caribbeans. The research involved a number of programming operations starting with collecting data, through data processing up till applying the Self Organising Maps (SOM) algorithm and visualising results.