Hex-Bin Mapping Bigfoot Sighting Clusters in the Lower 48

For day 4 of the #30DayMapChallenge, my design theme is hexagons. For this mapping project I found another wonderful and weird dataset from an organization called the Bigfoot Field Research Organization (BFRO), which is dedicated to the study of bigfoot. BFRO maintains a database of reported bigfoot sightings that includes latitude and longitude coordinates for each reported sighting that can be plotted onto a map. Similar to the NUFORC data featured in my previous UFO/UAP map, the BFRO dataset includes reported bigfoot sightings, with users reporting via a webform hosted on BFRO’s website. Like the previously mapped UFO/UAP subject, the bigfoot legend is an iconic part of American folklore that I find interesting, and I thought it would be neat to map reported sightings by prevalence and perhaps identify clusters where cases are more common.

To make this map, I incorporated hex-binning, a cartographic technique that uses a tessellation of hexagonal polygons to aggregate point data. The aggregated data can then be visualized using the hexagonal polygons to show prevalence of incidence by using either a choropleth color scheme to indicate total number of point features per hexagonal bin, or by incorporating a graduated symbology using different sizing to indicate the number of points with a bin, with larger hex shapes representing a higher rate of incidence and smaller hex shapes representing a lower rate of incidence.

For this project, I decided to limit my area of interest to the Lower 48 states of the United States. To start, I clipped reported sightings to the Continental United States, filtering out sightings from outside this area. Next, I used the generate tessellation tool in ArcGIS Pro to create 1,000 square mile hexagonal bins (using the USA Contiguous Albers Equal Area Conic Projection). This resulted in a hexagonal mesh across my project area with each bin representing 1,000 square miles. I then used the spatial join function in ArcGIS Pro to aggregate reported sightings to the hexagonal bins that they geographically corresponded with. Once this was complete, I selected all bins that had a value of zero and removed them. This reduced the visual clutter, removing the distraction of empty bins. I then applied a graduated symbology to show hexagonal bins with higher numbers of reported sightings as larger, and those with fewer reported sightings as smaller. Below is my resulting map.

In reviewing the results, I was not surprised to see a higher prevalence of cases reported in the Pacific Northwest of the United States. This tracks with my knowledge on bigfoot lore, though that knowledge is limited to a few books I read, and TV shows I watched, when I was teenager. I was somewhat surprised to see more cases reported along the Pennsylvania-Ohio-West Virginia border region. I had not known these states had a history or lore of bigfoot sightings. As a Pennsylvania resident myself and avid hiker and backpacker, I will have to keep my mind and eyes open next time I trek into the wilds of Penn’s Woods. In any case, this was a fun dataset to work with and I liked the resulting map.