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11

Open "Create Wedge Buffer" tool. Select the layer containing the start points as input layer. Click "Data defined override" option () near the "Azimuth" and select "Edit.." Use this expression: ('end': the layer name of the end point) degrees( azimuth( $geometry, geometry( get_feature_by_id( 'end', 1 ))))


3

The following is only valid for differential elements of a direct Mercator projection on a unit sphere: In the direction of parallels, deformation modulus is constant. And a parallel circumference measures 2 π cos(φ) on the sphere surface and 2 π on the projected image. We know that it is conform, so we will save ourselves from calculating the deformation ...


2

An unsupervised learning approach using density based clustering could be applied to determine the clusters the OP is looking for. To this end, this proposed solution is implemented using PostGIS, however the idea can be conveyed to QGIS, too. To obtain the clusters, a perpendicular will be required intersecting all parallel input lines which will yield a ...


2

I downloaded shapefile available in the question and it looks as follows in QGIS3. It can be observed that lines are not equally spaced between groups and it could be difficult to exclude a manual decision for a quick useful approach. However, it can be summarized in a python script because there are several algorithms in processing tool that it can be used. ...


2

What you want to achieve is basically a kind of "clustering" of lines: grouping lines that are close together. You need one manual decision, a maximum distance up until which lines should be considered part of the same group: see end of step 2 for details. This solution has five steps, one step for preparing the data, step 2 as the main step to ...


1

Reproject to a projected coordinate system in meters, for example EPSG:32634. Then the buffer distance will be in meters: import geopandas as gpd import pandas as pd events = pd.DataFrame([ {'id': 1, 'longitude': 54.606402, 'latitude': 18.347709}, {'id': 2, 'longitude': 54.604681, 'latitude': 18.346534}, {'id': 3, '...


1

The problem here that needs solving is due to the fact that the rows of buildings are different distances away from the park boundary. For example the buildings to the south have a road in-between them and the park. So the distances are not constant. Also the number of buildings per side varies. Finally the configuration of buildings varies. In the north you ...


1

I think the issue is the you buffer the locations while using the EPSG 4326 projection. This doesn't use meters as the unit so the buffer zones might not all be 2km. Here I re-project the data to the WGS 84 / Pseudo-Mercator projection. Depending on where you're points are located there might be a more suitable projection also. locations = [[54.156014, 53....


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