Count embedder

Run this notebook in Google Colab:

Remember to install the srai library before running the notebook:

%pip install srai[all]

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import geopandas as gpd
from shapely import geometry

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.embedders import CountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMOnlineLoader
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer
import geopandas as gpd
from shapely import geometry

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.embedders import CountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMOnlineLoader
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer

Data preparation¶

In order to use CountEmbedder we need to prepare some data.
Namely we need: regions_gdf, features_gdf, and joint_gdf.
These are the outputs of Regionalizers, Loaders and Joiners respectively.

Define the bounding box polygon¶

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bbox_polygon = geometry.Polygon(
    [
        [17.0198822, 51.1191217],
        [17.017436, 51.105004],
        [17.0485067, 51.1027944],
        [17.0511246, 51.1175054],
        [17.0198822, 51.1191217],
    ]
)
bbox_gdf = gpd.GeoDataFrame(geometry=[bbox_polygon], crs=WGS84_CRS)
bbox_gdf
bbox_polygon = geometry.Polygon(
    [
        [17.0198822, 51.1191217],
        [17.017436, 51.105004],
        [17.0485067, 51.1027944],
        [17.0511246, 51.1175054],
        [17.0198822, 51.1191217],
    ]
)
bbox_gdf = gpd.GeoDataFrame(geometry=[bbox_polygon], crs=WGS84_CRS)
bbox_gdf

Out[2]:

	geometry
0	POLYGON ((17.01988 51.11912, 17.01744 51.105, ...

Regionalize the area using an H3Regionalizer¶

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regionalizer = H3Regionalizer(resolution=8, buffer=True)
regions_gdf = regionalizer.transform(bbox_gdf)
folium_map = bbox_gdf.explore(tiles="CartoDB positron")
plot_regions(regions_gdf, map=folium_map)
regionalizer = H3Regionalizer(resolution=8, buffer=True)
regions_gdf = regionalizer.transform(bbox_gdf)
folium_map = bbox_gdf.explore(tiles="CartoDB positron")
plot_regions(regions_gdf, map=folium_map)

Out[3]:

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Download some objects from OpenStreetMap¶

You can use both OsmTagsFilter and GroupedOsmTagFilter filters. In this example, a simple OsmTagsFilter filter is used.

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loader = OSMOnlineLoader()
tags = {
    "leisure": ["playground", "adult_gaming_centre"],
    "amenity": "pub",
}
features_gdf = loader.load(bbox_gdf, tags=tags)
features_gdf
loader = OSMOnlineLoader()
tags = {
    "leisure": ["playground", "adult_gaming_centre"],
    "amenity": "pub",
}
features_gdf = loader.load(bbox_gdf, tags=tags)
features_gdf

Out[4]:

	geometry	leisure	amenity
feature_id
node/300461010	POINT (17.03086 51.11136)	None	pub
node/300461141	POINT (17.03012 51.10761)	None	pub
node/320922580	POINT (17.02893 51.11182)	None	pub
node/551466140	POINT (17.02916 51.10994)	None	pub
node/1093885062	POINT (17.02947 51.11013)	None	pub
...	...	...	...
way/1074595740	POLYGON ((17.02759 51.10969, 17.02761 51.10968...	playground	None
way/1075488712	POLYGON ((17.03706 51.11154, 17.0371 51.11153,...	playground	None
way/1116839835	POLYGON ((17.04233 51.11126, 17.04247 51.11126...	playground	None
way/1202309655	POLYGON ((17.02259 51.11876, 17.02259 51.11874...	playground	None
way/1286669934	POLYGON ((17.03622 51.11049, 17.03668 51.1104,...	playground	None

107 rows × 3 columns

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folium_map = plot_regions(regions_gdf, tiles_style="CartoDB positron", colormap=["lightgray"])
features_gdf.explore(m=folium_map)
folium_map = plot_regions(regions_gdf, tiles_style="CartoDB positron", colormap=["lightgray"])
features_gdf.explore(m=folium_map)

Out[5]:

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Join the objects with the regions they belong to¶

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joiner = IntersectionJoiner()
joint_gdf = joiner.transform(regions_gdf, features_gdf, return_geom=True)
joint_gdf
joiner = IntersectionJoiner()
joint_gdf = joiner.transform(regions_gdf, features_gdf, return_geom=True)
joint_gdf

Out[6]:

		geometry
region_id	feature_id
881e204089fffff	node/300461010	POINT (17.03086 51.11136)
	node/300461141	POINT (17.03012 51.10761)
	node/320922580	POINT (17.02893 51.11182)
	node/551466140	POINT (17.02916 51.10994)
	node/1093885062	POINT (17.02947 51.11013)
	...	...
	way/1074595740	POLYGON ((17.02761 51.10971, 17.02764 51.10973...
881e2040d5fffff	way/1075488712	POLYGON ((17.0371 51.11153, 17.03712 51.11153,...
881e2040d5fffff	way/1116839835	POLYGON ((17.04233 51.1114, 17.04247 51.1114, ...
881e2040c3fffff	way/1202309655	POLYGON ((17.02265 51.11876, 17.02265 51.11874...
881e2040d5fffff	way/1286669934	POLYGON ((17.03668 51.1104, 17.03664 51.11033,...

107 rows × 1 columns

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from plotly.express import colors

folium_map = plot_regions(regions_gdf, tiles_style="CartoDB positron", colormap=["rgba(0,0,0,0)"])
joint_gdf.reset_index().explore(m=folium_map, column=REGIONS_INDEX, cmap=colors.qualitative.Bold)
from plotly.express import colors

folium_map = plot_regions(regions_gdf, tiles_style="CartoDB positron", colormap=["rgba(0,0,0,0)"])
joint_gdf.reset_index().explore(m=folium_map, column=REGIONS_INDEX, cmap=colors.qualitative.Bold)

Out[7]:

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Embed using features existing in data¶

Count Embedder can count features on a higher level (tag key) or separately for each value (tag key and value). Both examples are shown below.

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wide_embedder = CountEmbedder(count_subcategories=True)
wide_embedding = wide_embedder.transform(regions_gdf, features_gdf, joint_gdf)
wide_embedding
wide_embedder = CountEmbedder(count_subcategories=True)
wide_embedding = wide_embedder.transform(regions_gdf, features_gdf, joint_gdf)
wide_embedding

Out[8]:

	leisure_adult_gaming_centre	leisure_playground	amenity_pub
region_id
881e20408dfffff	0	5	0
881e2040d7fffff	0	1	0
881e2040c3fffff	0	11	1
881e204081fffff	0	1	1
881e2040d1fffff	0	2	1
881e2040c7fffff	0	1	1
881e20409dfffff	0	4	0
881e204089fffff	1	7	34
881e2040d5fffff	0	11	10
881e20408bfffff	0	11	4
881e2040ddfffff	0	0	0

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dense_embedder = CountEmbedder(count_subcategories=False)
dense_embedding = dense_embedder.transform(regions_gdf, features_gdf, joint_gdf)
dense_embedding
dense_embedder = CountEmbedder(count_subcategories=False)
dense_embedding = dense_embedder.transform(regions_gdf, features_gdf, joint_gdf)
dense_embedding

Out[9]:

	leisure	amenity
region_id
881e20408dfffff	5	0
881e2040d7fffff	1	0
881e2040c3fffff	11	1
881e204081fffff	1	1
881e2040d1fffff	2	1
881e2040c7fffff	1	1
881e20409dfffff	4	0
881e204089fffff	8	34
881e2040d5fffff	11	10
881e20408bfffff	11	4
881e2040ddfffff	0	0

Embed with specifying expected output features¶

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embedder = CountEmbedder(
    expected_output_features=[
        "amenity_parking",
        "leisure_park",
        "leisure_playground",
        "amenity_pub",
    ]
)
embedding_expected_features = embedder.transform(regions_gdf, features_gdf, joint_gdf)
embedding_expected_features
embedder = CountEmbedder(
    expected_output_features=[
        "amenity_parking",
        "leisure_park",
        "leisure_playground",
        "amenity_pub",
    ]
)
embedding_expected_features = embedder.transform(regions_gdf, features_gdf, joint_gdf)
embedding_expected_features

Out[10]:

	amenity_parking	leisure_park	leisure_playground	amenity_pub
region_id
881e20408dfffff	0	0	5	0
881e2040d7fffff	0	0	1	0
881e2040c3fffff	0	0	11	1
881e204081fffff	0	0	1	1
881e2040d1fffff	0	0	2	1
881e2040c7fffff	0	0	1	1
881e20409dfffff	0	0	4	0
881e204089fffff	0	0	7	34
881e2040d5fffff	0	0	11	10
881e20408bfffff	0	0	11	4
881e2040ddfffff	0	0	0	0

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plot_numeric_data(regions_gdf, "leisure_playground", embedding_expected_features)
plot_numeric_data(regions_gdf, "leisure_playground", embedding_expected_features)

Out[11]:

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