Given you already have your RED 6km span points which I am referring to red_points, you can do something like this to obtain your 3X3 200m grey_points.
(please, adjust the sign (+/-) as you wish to achieve your result. Also, here 200 is in meters as long as the units of your reference system is meter.)
red_points = "your_red_points_fc_here"
You could just use left() and right() to create new columns with the Field Calculator.
',' ) - 1)
The above extracts the number before the comma (by extracting all characters up until the index of character ',' minus 1).
The below extracts the number after the comma:
First of all, simply try the approach that was suggested by @Erik, deploy Excel with the combination of FIND(), LEFT()/RIGHT() and LENGTH(). Afterwards, import into QGIS.
Alternatively, try the following workflow
Tested on QGIS 2.18 and QGIS 3.4
Let's assume there is a .csv-file "points" with wrong coordinates knitted together as x,y, see image below.
First of all, make sure your data is in a projected coordinate system whose linear unit is "Meter"
In the tree stand polygon's attribute table, use Field Calculator to make a new field with the expression $area - (264 * (4.5/2)^2 * 3.14). Make sure the field type is "Real" or some other numeric type.
This however will not take into ...
There are two basic methods for displaying points that are too close together.
The first method is clustering. This method combines nearby points into a single symbol. The symbol often includes a number, which shows how many points have been combined. Here's a blog post about using point clustering symbology:
I suggest organising your points into start and end layers like so: Layer 1: Point 1-2, Layer 2: Point 2-3, Layer 3: Point 3-4 and so on, based on their order.
[Edit: I meant v.net.distance, not path]
Then you can run GRASS7 v.net.distance in batch mode on all pairs and dissolve the resulting lines to form a continous path in the network.
Alternately, if ...
Run NEAR tool on itself and select points with near distance greater or equal to 250 km.
If you are lucky to get 200 points or more, copy them to a separate feature class and use any random generator to pick 200 of them. Selected points in a picture below show points sitting at least 500 m away from any other point.
If there are less than 200 points, ...
If you have a set of point, you can random select from it based on a random number in your attribute table. This is the first step. Once you have the random number, sort your attribute table and take the 200 first points (select manually is the fastest).
The probability that you have two points at less than 250 km from each others is quite small, but it ...