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1

Use an attribute field or expression to define your stroke width: As your numbers are likely going to be too large to be used as a width, under the Expression submenu, select Edit to create a new expression. You can use the field that has your count value and multiply it by say 0.1, for example, to bring it down to a more realistic number that can be used ...


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You can try Shortest Path: Processing toolbox > Network Analysis > Shortest Path There are different options I guess yours is Point to point


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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: https://www.esri.com/arcgis-blog/products/...


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To find a "concave hull" around a set of 3D points, I found that using the marching cube algorithm for volumetric data works best. Here is an example using Python. To run it, you first need to transform your cloud of 3D points into a volumetric dataset. Then, you obtain the points at the border of your cloud and the faces that can be used to make a mesh that ...


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First of all, make sure your data is in a projected coordinate system whose linear unit is "Meter" Approximate Solution: 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 ...


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Solved. Just go vector-> research tools -> random selection. Then go to Vector -> analysis tools -> basic stats.. This took me more than it should, but I live to learn


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You could just use left() and right() to create new columns with the Field Calculator. left( "xy", (strpos( "xy", ',' ) - 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: right( "xy", (strpos( "xy"...


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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. ...


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Another more unorthodox way would be to use snapping. First snap the points to the network with "Snap geometries to layer" from the processing toolbox. Then use "Points to path" to connect the snapped points by lines. Then use "Snap geometries to layer" on the created line to snap it to the network. I got some reasonable results by playing around with ...


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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 ...


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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, ...


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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.) import arcpy red_points = "your_red_points_fc_here" ...


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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 ...


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