I took the weekend off from typing and spent some time composing a model to attempt to determine the probabilities that a random condition caused the meteor strike distribution in Europe.
I know, I know, so I am addicted to science, I admit it.
My intent is to measure the radial density of the meteor strike area and have a random generator attempt to mimic the condition.
The number of attempts required before a successful event is recorded.
This process will be repeated multiple times to determine an estimation of the probability that the meteor strike record is random.
From the map of Europe I selected the portion 40 to 70 degrees latitude and 10-40 longitude.
From this site.
http://www.unb.ca/passc/ImpactDatabase/europe.html
I selected this area for several reasons as follows.
1. The greater geological changes happen where the greatest concentration of meteor strikes are recorded. It is therefore concluded that geological movement is not responsible for the removal of meteor strikes from areas which contain few recorded strikes.
2. The area is in an accessible and technologically advanced location, making it unlikely the area has not been well searched and that appropriate satellite information is available.
3. My intent is to measure meteor strike density. Distortion of the map due to the forming of a flat map of a curved surface of the Earth causes the upper portion of the map to be stretched. The highest concentration of meteor strikes occur in the upper portion of the map which will cause the meteor strikes to appear to disperse slightly.
This will give the random generator a slightly better chance to match the condition as the skewed meteor data will give a slightly less dense condition.
This location therefore is immune the argument that map distortion is hurting the chance for a successful random condition.
4. This location does not contain deep water conditions and there is also a body of water close to the center of the meteor concentration, so it can not be argued that the placement of bodies of water have disproportionately prevented, removed or hidden meteor strike data.
The following modifications were made to the raw data.
1. On close inspection it was discovered that there was not a location number 1 and that there were two locations numbered 36. One of the locations assigned as 36 was reassigned to location number 1.
2. A portion of the area used included areas in Asia and information from the Asian map was used to position four meteor strikes.
The Asian map is at this site.
http://www.unb.ca/passc/ImpactDatabase/asia.html
3. Several meteor strikes which were included on the map of Europe but were not within the designated latitude and longtitude limits and were excluded.
4. Meteor strikes which left a crater less then one Kilo Meter in diameter were removed from the data base to assure the data base contained only craters that were large enough to be detected easily and consistently.
This was done to eliminate that argument that small craters had been found only in specific densely populated areas causing the data base to be flawed.
When these modifications to the data were completed the information was renumbered to provide a sequential assignment for data entry in a computer program.
The meteor strike map was printed on an 11" x 17" sheet.
The map was then printer over with a 100 x 100 grid.
Locations of all 35 qualifying meteor strikes were recorded to 0.5 increments.
Quick basic was used to create the computer simulation.
The data of an X and Y component for each meteor were assigned to an array.
The average X and Y values were determined to locate the "center of the meteor" strikes.
The distance from the "center of the meteors" to each meteor was determined using the pythagorean theorem.
The meteor offset distances were averaged and used as an indication of meteor strike radial concentration density.
A random generator was then used to assign (35) X and Y coordinates to (35) hypothetical meteor strikes.
A meteor strike concentration density was derived for the hypothetical meteor strike condition about the hypothetical meteor strike center and checked to see if it was equal to or closer packed then the actual meteor strike data.
If the hypothetical meteor strike data was less dense then the actual meteor strike data then the attempt was considered a failure.
The number of failures were recorded and sequential attempts were made until a successful attempt occurred.
The program repeated this process until 100 successful attempts were completed.
The average number of attempts required to produce a successful attempt was then given.
The program had run for several hours and indicated it had made about 4.5 millions failed attempts without a successful attempt.
I terminated the program prematurely to determine if it had a flaw.
The number of hypothetical meteor strikes was reduced to 10 and a successful attempt was accomplished in a short time.
I then modified the program to start with (3) hypothetical meteors and progress to (35) hypothetical meteors.
As each hypothetical meteor count was finished an average number of attempts were output.
I left the computer run over night and in the morning it had progressed from (3) hypothetical meteors ( repeated 100 times and averaged) requiring about 3.5 attempts to produce a successful attempt, through (25) hypothetical meteors ( repeated 100 times and averaged) requiring about 641,000 attempts.
Due to my knowledge of mathematical probability functions, it was suspected that the function of the number of attempts would be as follows.
F(x) = A ^ (n) = average number of failed attempts to produce a successful attempt.
Where A was an unknown variable and (n) was the number of hypothetical meteors.
The function matched the random generated data well if A = 1.575.
By extrapolation if A = 1.5 the number of attempts would be about 1 million.
If A = 1.6 then number of attempts would be about 14 million.
I will need hundreds or thousands of successful attempts to produce a reliable distribution pattern of 35 hypothetical meteors and therefore it is necessary to make the program as fast as possible and then compile it (or let it run for forty days and forty nights).
As soon as I finish I will post as much material as possible however considering the limitations of this site I am not sure what form it will take.
This process could be repeated with a more complicated model using several Earth locations or even the entire planet but odds of millions to one should be enough to indicate the meteor strike pattern is not a random condition.
I am curious the ability of a mathematical prof to convince individuals who would reject the obvious condition of the maps shown which they can see with their own eyes?
My guess this is a faith based decision on their part and not a logical decision.
I suspect therefore it will have no more success then the visual attempt.
However I have been wrong before.
We shall see.
Duane