Exploring the Use of Geographic Information Systems to Identify Spatial Patterns of Remote UAS Pilots and Possible National Airspace Risk

The proliferation of Unmanned Aircraft Systems (UAS) in the United States National Airspace System (NAS) has resulted in an increasing number of close encounters between manned aircraft and UAS, which correlates with the increasing number of remote pilots in the Federal Aviation Administration (FAA) airmen database. This research explores spatial patterns of registered airmen using Geographic Information Systems (GIS) analyses that provide notable spatial distribution patterns of pilots and how they relate to UAS sightings and airspace categories. The application of GIS to these aviation data may assist safety practitioners with identifying geographic patterns, areas of higher risk, and ultimately improve safety management. The authors analyzed publicly available airmen data to examine spatial distribution patterns, data correlations, and inferences. Airmen addresses were first geocoded into ArcPro 10.4 GIS software as a vector data layer containing attribute values of the database. The spatial analysis tool set was then utilized to establish clustering, density patterns, and spatial relationships between various categories of registered airmen. These density analyses revealed implicitly that commercial registered pilots tend to have the highest clustering near major commercial use controlled airspace, yet registered remote (UAS) pilots are also clustered in these and other densely populated areas. UAS sighting data were also geocoded using zip code values of the reported city to potentially correlate UAS sighting with registered remote pilots, yet the lack of spatial precision in the database made establishing any type of spatial relationship ineffective. The implicit spatial relationships between commercial and remote registered pilots revealed further research is needed to integrate UAS safely and effectively into the national airspace. The poor quality of UAS sighting data also demonstrates the need to better utilize GIS to monitor and track UAS flights within the context of an Unmanned Traffic Management System.