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Another option is to use the Statistical Summary tool. Type $perimiter in the input:

"] = df.apply(lambda x: shape(ast.literal_eval(x.col1)), axis=1) df = gpd.GeoDataFrame(data=df, geometry=df["geometry"], crs=4326) df.geometry Out[100]: 0 LINESTRING (5.80703 43.19285, 5.80731 43.19276 … ) Name: geometry, dtype: geometry …

Having large complex geometries will slow down processing. Try: Multipart To Singleparts Subdivide 3. And Create Spatial Index On both input layers

Execute the query and "Load as new Layer" select row_number() over() as id, st_envelope(st_buffer(geometry, 200.0, 16)) as geometry from "My point layer" Place a point in the center of where you want …

Try this workflow: My starting layer, some holes along the borders and inside: Dissolve Delete holes Difference the Delete holes output with the start layer to create polygons where there are h …

You can: Create a column where you save the larger polygon geometries, then spatial join the larger polygons to the smaller Calculate the overlap area (using the saved geometry and the spatial join geometry … True print(sj.loc[sj["id_left"]==1][["id_left", "id_right"]]) # id_left id_right # 0 1 9.0 # 0 1 10.0 #Calculate overlap areas sj["overlap_area"] = sj.apply(lambda x: x["geometry …

Intersect, calculate line length, sum and merge it back to the polygon df: import geopandas as gpd hexgrid = gpd.read_file(r"C:\GIS\GIStest\hexgrid.shp") hexgrid["gridid"] = range(hexgrid.shape[0]) # …

Delete holes: Takes a polygon layer and removes holes in polygons Use buffer output as input: layer = iface.activeLayer() buffered = processing.run("native:buffer", {'INPUT':layer,'DISTANCE':1 …

One measure is Sinuosity which is the line length divided by the shortest distance from the line start and end points: select id, st_length(geom)/st_distance(st_startpoint(geom), st_endpoint(g …

This is now very easy with the .reverse method: import shapely line = shapely.wkt.loads("LINESTRING (0 0, 1 1, 3 1, -3 3)") multiline = shapely.wkt.loads("MULTILINESTRING ((120 105, 140 98),(160 103, …

You shouldnt need dtype={'geom': Geometry(geometry_type='GEOMETRY', srid= 4326)}. … use .to_postgis: import geopandas as gpd from sqlalchemy import create_engine gdf = gpd.read_file(r"/home/bera/Desktop/GIStest/distances_houses/house.geojson") # gdf.dtypes # houseid object # geometry …

Modify layer name (row 3 and 4) and the id column name (row 6) in the query below and execute: select row_number() over() as newid, st_intersection(a.geometry, b.geometry) as geometry from "qwerty …

= gpd.points_from_xy(x=df["lon"], y=df["lat"], crs=4326) df = gpd.GeoDataFrame(data=data, geometry=geometry, crs=4326) df = df.to_crs(3829) #You need to have a projected coordinate system with meters … endy"])]), axis=1) df = df.set_geometry("line").drop(columns="geometry").rename_geometry("geometry").set_crs(3829) df.to_file(r"C:\GIS\data\testdata\lines.shp") …

range(point.shape[0]) #For each point find the closest polygon(s) # If there are more than one polygon within snapdistance, the points will be duplicated poly["polygeom"] = poly.geometry #Save the geometry … NaN None sj["geometry"] = sj.apply(lambda x: snap(x.geometry, x.nearestpoint, snapdist) if x.nearestpoint is not None else x.geometry, axis=1) #Snap …

I can recommend this way, it is very fast. Each group of polygons is assigned the same id if they are 0 distance apart, and number at least 2, using ST_ClusterDBSCAN. Single polygons will get NULL as …