I'm writing a small QGIS 3 python plugin to convert polygon shapefiles to input files for a scientific model. I therefore need to split up the polygon geometries to more simple geometries with up to 4 vertices. The most simple way that crossed my mind was making use of the generic function delaunayTriangulation, that I suppose, would split my geometries into triangles. I did the following (simplified excerpt, replaced polygon by example-WKT so you can try): # get current geometry from buildings layer # polygonGeometry = buildingGeometries[b] # get a building geometry, all valid polygons, tested before polygonGeometry = QgsGeometry.fromWkt('PolygonZ ((3413873.66000000014901161 5318284.88999999966472387 286.15166666666664241, 3413878.89999999990686774 5318283.04999999981373549 286.15166666666664241, 3413860.56000000005587935 5318227.38999999966472387 286.15166666666664241, 3413806.66000000014901161 5318245.30999999959021807 286.15166666666664241, 3413805.93000000016763806 5318243.12000000011175871 286.15166666666664241, 3413805.22000000020489097 5318243.30999999959021807 286.15166666666664241, 3413790.99000000022351742 5318200.08999999985098839 286.15166666666664241, 3413774.7099999999627471 5318205.36000000033527613 286.15166666666664241, 3413793.41999999992549419 5318261.62000000011175871 286.15166666666664241, 3413794 5318261.44000000040978193 286.15166666666664241, 3413808.87999999988824129 5318306.34999999962747097 286.15166666666664241, 3413814.14000000013038516 5318304.58999999985098839 286.15166666666664241, 3413811.62999999988824129 5318296.96999999973922968 286.15166666666664241, 3413822.52999999979510903 5318293.36000000033527613 286.15166666666664241, 3413812 5318261.4599999999627471 286.15166666666664241, 3413849.68999999994412065 5318249.07000000029802322 286.15166666666664241, 3413860.33999999985098839 5318280.83999999985098839 286.15166666666664241, 3413871.14000000013038516 5318277.26999999955296516 286.15166666666664241, 3413873.66000000014901161 5318284.88999999966472387 286.15166666666664241))') # do Delaunay triangulation to split the polygons in a fast and simple way triangles = polygonGeometry.delaunayTriangulation(0.01, False) # convert the result to a GeometryCollection to facilitate extraction of individual items triangles = triangles.asGeometryCollection() # iterate over all individual triangles 't' in the geometryCollection 'triangles' for t in triangles: # all t are Polygons with 4 points while 1st and last point are the same # test if polygon is outside original polygon (might happen for multi polygons) if (polygonGeometry.contains(t)): # if the original polygon contains t # write trinagles to output layer However, the ouput I get (violet triangles) sems to be lacking some triangles (orange polygones are the source layer). Am I missing something? Or could you think of some hack to fix the issue?

I have in my possession four sets of shapefiles: 1. Locations (points), around 1000 2. Roads (lines), each road has a speed attribute 3. Terrain (polygons), each zone also has a speed attribute 4. Rivers (lines), each river has a crossing delay time attribute How can I construct a Voronoi diagram for my location using travel time as a distance metric? I could have used pgRouting if I had roads only, but I am not sure how to approach different off-road terrain and rivers. I'm willing to code this, but I've spent enough time trying to come up with an algorithm, but to no avail. My current plan is to dump it all into a sufficiently large raster and create 1000 distance maps out of it, then use them to calculate the Voronoi cells, but I have a suspicion that a sufficiently large raster will take too long to process. Is there a better vector-based algorithm?

First steps: municipal maps of a census, census figures and changes over time.

This dataset comprises multi-scale road network data extracted from OpenStreetMap (OSM) for five Chinese cities—Chengdu (radial-ring pattern), Wuhan, Kunming, Shenzhen, and Hefei (grid-irregular mixed...

This dataset includes shapefiles of classification of benthic cover and geomorphic zones of the shallow coral reefs in the area of western Micronesia.  The boundary of the mapped area is included in t...

What I have is two shapefiles, describing roads in a city. The shapefiles are made of linestrings, each covering a section of road. * The road segments in shapefile 1 are generally several times larger than the segments in shapefile 2. * The road segments break at road junctions for both shapefiles. * The shapefiles do not describe every road in the city. * Both shapefiles describe the same city, although there is some difference in the roads they cover. Some roads decribed by segments in shapefile 1 are not present in shapefile 2, and vice versa. What I want is to create a many to one map between the shapefiles. I would like a table that lists each segment in shapefile 2, and it's closest match in shapefile 1. By closest match I mean closest in similarity, not necessarily in distance. In situations where a segment in shapefile 2 overlaps two segments in shapefile 1, the segment it overlaps the most would be chosen. Where there is no matching segment in shapefile 1, the table entry would be null. I'm not sure how to best approach this task. My initial plan is to create a new polygon layer using the buffer tool in QGIS, from the features in shapefile 1, and then somehow find which buffer the linestrings from shapefile 2 predominantly fall in. I don't know if there are any tools which would help with this second step, or tackles the whole problem. I know how to use QGIS, and Python.

Learn how to pull World Bank indicators with wbopendata, get official WB boundaries with geoboundary, and build maps using geoplot.

Explorer les cartes : Découvrez des cartes thématiques et de référence dont les fonds IGN sur l’ensemble du territoire français.

This question is related to Converting shapefiles to text (ASCII) files?. I have a CSV file, with one column, where all the rows correspond to WKT POLYGON()'s: WKT POLYGON(...) POLYGON(...) ... I'm familiar with how to convert from shapefile to wkt, but I need to go the other way around. How to accomplish this task? I tried playing around with ogr2ogr's settings/flags but didn't really get anything useful. I also know I can use QGIS to do this, but it freezes/crashes since the dataset is fairly large.

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