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2

You can use expressions to get for each point the id of the closest point with the same attribute value, like name. You can use for this overlay_nearest function (available since QGIS 3.16, for older versions have a look at the refFunctions Plugin). It allows you to create an array of features, sorted in ascending order of distance to the current feature: ...


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If you want to measure (real world)- distance, don't draw circles / create buffers on a flat map canvas. Using EPSG:3857 / Web Mercator is especially a bad choice for measuring distances or areas, as the closer you get to the poles, the higher distorted the proejction gets. This is especially true for large distances as in this case here. See what can happen ...


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I am the author of this picture. So I can tell for sure the sign post is located in Thessaloniki, Greece.


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https://github.com/mocnik-science/geogrid - The library geogrid provides methods to generate and handle the ISEA Aperture 3 Hexagon Discrete Global Grid System (ISEA3H DGGS) using the Inverse Snyder Equal-Area Projection (ISEA). https://github.com/mocnik-science/geogrid.js - The library geogrid.js provides a Leaflet layer that depicts data aggregated by the ...


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Especially for public transport isochrones, it worth trying free CommuteTimeMap web app. It is capable of generating public transit isochrones for most cities in the world. "Regular" transportation modes like driving, bicycle, and walking, are supported as well. I also recommend checking the "How to make travel time maps for public transport&...


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This function computes "polygon voronoi" areas - it divides the space up into areas that are nearest spatially dispersed polygons. polyvor <- function(polys){ pts = st_coordinates(polys) pts = pts[duplicated(pts[,"L3"]),] pts = st_as_sf(data.frame(pts), coords=1:2)[,"L3"] vpts = do.call(c, st_geometry(...


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Guess what? ;) >>> p1, p0 = nearest_points(uk_main_landmass.boundary, p0) >>> p0.coords.xy (array('d', [50.907262]), array('d', [-0.420036])) >>> p1.coords.xy (array('d', [1.3844507170000497]), array('d', [51.151678778000075])) Yes, you just inverted your (lat, lon) coordinates when defining p0! This will definitely work: (...) ...


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You can use the native QGIS Line Density algorithm for this task. It computes a density value based on line segments in a search radius as you can see in the picture. You find the algorithm in the QGIS processing toolbox in the interpolation algorithm group.


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Add additional vertices to your lines using Densify by count. Extract vertices from the densified line. Apply heatmap symbol renderer to the extracted vertices. Screenshot: original lines are extended to see their distance and compare it with the rendered heatmap:


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Try to think the opposite way! It's basically a graph problem. Pseudo-code: Get the centroids for each cells. They are nodes. Draw the paths. Count the intersections between each path an cell's borders for each paths (subtract 1). They will eventually sum up if you need to take more than one edge to go from two nodes. Search for 'dual graph' and you will ...


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This is how I would do it import pandas as pd import geopandas as gpd df = pd.DataFrame() df['lat'] = [42, 42.1, 43, 43.2, 43.9] df['lon'] = [-120, -119, -118.2, -118, -117.2] gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(df.lon, df.lat), crs="EPSG:4326").to_crs("EPSG:6340") df['distance_from_previous'] = gdf....


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I suggest to use the "Oriented minimum bounding box" tool from the processing toolbox. "This algorithm calculates the minimum area rotated rectangle which covers each feature in an input layer." The output also contain the width, height, angle, perimeter and area of the bonding box. For mostly straight elongated polygon the width of the ...


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With QGIS you can use the following processing tools: GPS route as input: Densify by interval (add vertices every 1 m) Extract vertices (create a point layer from the vertices) With country border and extracted vertices (from above): Align points to features (to get distance between each point and border) Basic statistics for fields with distance field as ...


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You can do this using QGIS and OpenStreetMap data, so OpenSource/Open data and free to use. The process includes several steps. Using the online QGIS documentation can help you for the steps. When you are stuck, post another question here. Don't forget to critically reflect how accurate the data is (I propose OpenStreetMap) - however, I guess for the kind of ...


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If you just want to calculate the distance from each buidling the the outline of the plots, go to the bottom to see how to calculate a new attribute field for that. Here, I first give some more detailed information and also a visualization of the line you're looking for. Let's supposte you have three layers named: buildings (polygon), plots (polygon) and ...


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That's a multi-step process; using r.grow.distance you can work natively with geodetic CRS (i.e. calculate distances and output using meter as units), rather than depend on the map projection units: <Toolbox Search> | Rasterize (Vector to Raster) select your feature layer as Input layer set A fixed value to burn to 1.0 set Output raster size units to ...


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