cluster together adjacent grid tiles

class TileClustering[source]

TileClustering(cluster_type:str='four_way')

Cluster together adjacent square grid cells. Grid cells belonging to the same cluster will get assigned the same ID. Optionally, you cluster adjacent cells by category by passing in category_col

By default, with cluster_type = " four-way", it clusters together grid cells with adjacent edges only. If you wish to consider grid cells with adjacent corners as well, use cluster_type = " eight-way"

TileClustering.cluster_tiles[source]

TileClustering.cluster_tiles(df:DataFrame, grid_x_col='x', grid_y_col='y', category_col:Optional[str]=None)

Appends the cluster ID for each square grid cell

Test data

Create sample scores for square grid cells and cluster the cells

import geopandas as gpd
import numpy as np

from geowrangler import grids

np.random.seed(1562)

region3_gdf = gpd.read_file("../data/region3_admin.geojson")

grid_generator5k = grids.SquareGridGenerator(5_000)
grid_gdf5k = grid_generator5k.generate_grid(region3_gdf)
grid_gdf5k.head()

	x	y	geometry
0	7	8	POLYGON ((120.10024 14.75528, 120.14516 14.755...
1	6	8	POLYGON ((120.05533 14.75528, 120.10024 14.755...
2	9	8	POLYGON ((120.19008 14.75528, 120.23499 14.755...
3	9	9	POLYGON ((120.19008 14.79871, 120.23499 14.798...
4	10	9	POLYGON ((120.23499 14.79871, 120.27991 14.798...

grid_gdf5k.plot()

<AxesSubplot:>

%%time

grid_gdf5k["score"] = np.random.random(len(grid_gdf5k))
grid_gdf5k["class"] = grid_gdf5k["score"] > 0.7
grid_gdf5k.head()

CPU times: user 3.15 ms, sys: 1.32 ms, total: 4.47 ms
Wall time: 10 ms

	x	y	geometry	score	class
0	7	8	POLYGON ((120.10024 14.75528, 120.14516 14.755...	0.761806	True
1	6	8	POLYGON ((120.05533 14.75528, 120.10024 14.755...	0.012455	False
2	9	8	POLYGON ((120.19008 14.75528, 120.23499 14.755...	0.446552	False
3	9	9	POLYGON ((120.19008 14.79871, 120.23499 14.798...	0.669020	False
4	10	9	POLYGON ((120.23499 14.79871, 120.27991 14.798...	0.815914	True

%%time
tileclustering = TileClustering()
grid_gdf5k = tileclustering.cluster_tiles(grid_gdf5k, category_col="class")
grid_gdf5k.head()

CPU times: user 25.8 ms, sys: 0 ns, total: 25.8 ms
Wall time: 47.6 ms

	x	y	geometry	score	class	tile_cluster
0	7	8	POLYGON ((120.10024 14.75528, 120.14516 14.755...	0.761806	True	6-1
1	6	8	POLYGON ((120.05533 14.75528, 120.10024 14.755...	0.012455	False	7-2
2	9	8	POLYGON ((120.19008 14.75528, 120.23499 14.755...	0.446552	False	1-2
3	9	9	POLYGON ((120.19008 14.79871, 120.23499 14.798...	0.669020	False	1-2
4	10	9	POLYGON ((120.23499 14.79871, 120.27991 14.798...	0.815914	True	23-1

grid_gdf5k["tile_cluster"].nunique()

160

grid_gdf5k.plot(column="class", categorical=True, cmap="Spectral")

<AxesSubplot:>