A UFO video which latest world's first UFO observation system automatic observation and video robot SID-1Ⓒ caught in Tokyo Japan
This "Space Phenomena Observatory Center(SPOC)Ⓒ" is under the intended variety of mysterious phenomena and objects have occurred in space and the air in the Earth's atmosphere, that continue to record actively such UFO and unusual things founded June 2014. This is a unique first attempt in the world.
"Space Phenomena Observatory Center(SPOC)Ⓒ" founded and designed by Mr.Hiroshi Kitajima.
From July 2014, activated the "automatic observation and video robot SID-1" (SID1 = Space Intrusion Detection systems-1 Ⓒ) by its own development, and start the observation. this SID-1, is has succeeded in capturing some of the things that could be determined that is UFO.
SID-1, consists of "sensitive high-resolution japanese color camera" "sensitive automatic motion detection system" and "automatic recording system" . This is the UFO observation system which doesn't available in the world and I`ve never seen before, and it enables an observation for 365 days x 24 hours.
See bottom left of this page and click "SID-1 xxxx" then you can take a look of some of video and image which so far SID-1 caught. Updating at any time.
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UFO Sightings Data Visualization ( data comes from the National UFO Reporting Center ) This data comes from the National UFO Reporting Center, which is "dedicated to the collection and dissemination of objective UFO data." That, and some census data, and we are off to the races.
Ratios! Of course, as is the case for any observation data, there is a strong tendency towards echoing a population map. This is certainly the case with this sighting data, as well. In order to visualize the actual sighting phenomenon, I needed to normalize by the underlying population. The first, more prominent map shows a simple ratio of the sightings by population. A per-capita approach. The second, smaller and slightly more complex map, shows a bi-variate mapping of sightings in the color dimension (dark slate for low-sightings and bright green for high-sightings) and population density in the opacity dimension (denser populations are more transparent). The result is a map that is more nuanced regarding the problem of variable populations and area sizes. Double normalized? Sort of. If you would like to traverse the rich and complex world of bi-variate mapping, check out this tour-de-force how-to by Joshua Stevens. UFO Sightings Data Visualization