Data Analysis and UFO Reports

Data Analysis and UFO Reports

Data analysis and unidentified flying object (UFO) reports go hand-in-hand. I attended a talk by author Cheryl Costa who analyzes records of UFO sightings and explores their patterns. Cheryl and her wife Linda Miller Costa co-authored a book that compiles UFO reports called UFO Sightings Desk Reference: United States of America 2001-2015.

Records of UFO sightings are considered citizen science because people voluntarily report their experiences. This is similar to wildlife sightings recorded on websites like eBird that help illustrate bird distributions across the world. People report information about UFO sighting events including date, time, and location.

A dark night sky with the moon barely visible and trees below.
Night sky along the roadside outside Wayquecha Biological Field Station in Peru, taken April 2015.

Cheryl spoke about gathering data from two main online databases, MUFON (Mutual UFO Network) and NUFORC (National UFO Reporting Network). NUFORC’s database is public and reports can be sorted by date, UFO shape, and state. MUFON’s database requires a paid membership to access the majority of their data. This talk was not a session to discuss conspiracy theories, but a chance to look at trends in citizen science reports.

The use of data analysis on UFO reports requires careful consideration of potential bias and reasonable explanations for numbers in question. For example, a high volume of reports in the summer could be because more people are spending time outside and would be more likely to notice something strange in the sky.

This talk showed me that conclusions may be temptingly easy to draw when looking at UFO data as a whole, but speculations should be met with careful criticism. The use of the scientific method when approaching ufology, or the study of UFO sightings, seems key for a field often met with overwhelming skepticism.

I have yet to work with any open-source data on UFO reports, but this talk reminded me of the importance of a methodical approach to data analysis. Data visualization for any field of study starts with asking questions, being mindful of outside factors, and being able to communicate messages within large data sets to any audience.