TechnicalGIS

#Shapely

4 posts

Tutorial Python Part 5 of 5

Tiling a large 3D-printed terrain model: jigsaw joints and dowel pins in Python

Split a 45 × 30 km model of the central Lake District into six interlocking 150 mm tiles from Copernicus GLO-30, with jigsaw knobs and a 0.2 mm printing clearance or straight edges and filament dowels, and prove every tile is watertight and fits its neighbours.

Advanced 9 min read

Four panels showing how Voronoi polygons are built for 47 rain gauges around the River Eden catchment: the gauges and the catchment outline, their Delaunay triangulation, the triangle circumcentres joined into red Voronoi edges, and the cells clipped to the catchment and shaded by each gauge's rainfall.

Tutorial Python

Voronoi (Thiessen) polygons in Python: catchment rainfall from EA rain gauges and fire station areas in Cumbria

Build Voronoi (Thiessen) polygons in Python with shapely and GeoPandas, clipped to a real boundary. Compute Thiessen-weighted catchment rainfall for the River Eden from Environment Agency rain gauge readings, check the gauges, compare with the gauge average and IDW, then compare Voronoi 'nearest fire station' areas in Cumbria with drive-time service areas from OpenStreetMap.

Intermediate 15 min read

Three panels of the same 1 km by 0.7 km area at Salford Quays. Left: square WorldCover pixels, mostly red built-up land with blue dock basins and small green and yellow patches. Middle: the same area as 142 polygons with black outlines that follow the pixel staircase. Right: the polygons after coverage simplification at 10 m, with smoother outlines and no gaps between them.

Tutorial Python Part 3 of 3

Raster to vector in Python: polygonize with rasterio, rasterize boundaries and convert a GeoTIFF to a Cloud Optimized GeoTIFF

Convert raster to vector in Python with rasterio.features.shapes on 10 m ESA WorldCover for Greater Manchester: polygonize water and built-up land, fix invalid polygons, simplify without gaps, rasterize ONS district boundaries for land-cover shares, then write and validate a Cloud Optimized GeoTIFF with rio-cogeo and measure HTTP range reads.

Intermediate 14 min read