Bigfoot field guide
This Tableau project was begun as a way to utilize a fun data set for a unique, creative use case - my home county is the birthplace of the modern day "Bigfoot" phenomenon.
The project begins with the simple question - what information would provide the best chance of finding Bigfoot? Using data sources from the Bigfoot Field Researchers Organization (BFRO), we leverage 100+ years of eyewitness sightings for Bigfoot. Below, I will review the broad steps required to create this visual, starting with strategy for visualization, and finishing with end result.
Next step: Strategizing our visualization
Planning the layout
An exercise was conducted to determine what was most important to a viewer who wanted to find Bigfoot - this exercise highlighted ~10 potential questions that a viewer would want to know. To minimize information overload, these questions were stack ranked, and the top three questions were made thematic for our field guide:
Where is Bigfoot
When is it spotted
How do people find it?
Next step: Create the data to drive the visual.
Data sets and manipulation
There are 5 distinct datasets being merged into a single visual; 2 of these datasets were sourced from the BFRO, providing well-structured data (date of eyewitness account, latitude and longitude) and un-structured data, like free-form anecdotal descriptions of the sighting. Another 2 data sets came from the US Census Bureau, and contain "shapefiles" for the U.S.; their primary function is to allow us to visualize and summarize sightings across types of geographic boundaries - in our case, we have shapefiles for state and county boundaries in the US. Without these files, we could not "see" a county boundary on a Tableau map. The final dataset was a simple lookup file for associating states with regions, used for rolling up eyewitness reports.
Our BFRO dataset was the main data set requiring manipulation - specifically for the un-structured string fields that contained valuable keyword information. For the unstructured data, we leverage Excel to create "flags" for keywords and identifiers that we wish to visualize (e.g. a mention of a "howl" or "shriek" in the eyewitness testimony would be turned into an "auditory flag" for us to summarize encounters by.)
Next step: Visualizing the data.
Visualization creation
Through unions and joins, we merged our 5 data sets, creating several key visuals for viewers. These included maps of the US, state and county that a viewer searches for, highlighting eyewitness reports and the locations where they occurred. The field guide also includes views to show the seasonality and time of day / week for sightings. Finally, the last visual indicates how an eyewitness identified Bigfoot (visual, audible, etc.)
Next step: Publish to Tableau.
