import geopandas as gpd
import numpy as np
from geowrangler import gridsTile Clustering
cluster together adjacent grid tiles
TileClustering
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
TileClustering.cluster_tiles (df:pandas.core.frame.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
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()
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 |
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()160grid_gdf5k.plot(column="class", categorical=True, cmap="Spectral")