I was reading about the book Space-Time Transients and Unusual Events and it occurred to me that a far more scientific study of window areas or hot points for high strangeness is now possible. We have a fairly large sample set in the form of back-issues of The Fortean Times. Were one to take machine-readable copies of all the back-issues, isolate the place names, and plot a point on a map for every mention, we could have an idea of distribution of claims. This self-adjusts for intensity because the stranger the phenomenon the more often it is likely to be mentioned.
Such a map could then be adjusted by population.
If high strangeness is contingent solely on population (in other words, if every person in every place has an equal likelihood of having an experience of a fortean nature) then our adjusted map should be a single fairly solid colour with very little variation, with the exception of places like Point Pleasant, Roswell, Dulce, the Groom Lake Facility, and so on, which are more notorious because of their particular relationship to the mass media.
If high strangeness has an inverse relationship with population (perhaps because loners see things, or because people steer themselves away from places with these phenomena, or for some other reason), we should see the original distribution of points made far more clear.
If high strangeness has a relationship with fault lines, as the authors of Space-Time Transients suggest, then we should see that distribution quite clearly once population is factored out. Likewise with leys, or aboriginal burial sites, or magnetic faults, or changes in gravity, or closeness to radio transmitters, or any of the other suggested explanations whose documented points can be traced on a map.
If I had machine-readable back-issues of the Fortean Times, I could do this in my copious free time. Perhaps I can rely upon the kindness of strangers and/or Fortean Times employees.
An enormous list of monster trucks. - [image: An enormous list of monster trucks.] submitted by /u/Star-spangled-Banner [link] [comments]
18 minutes ago