Contextual 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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from srai.embedders import ContextualCountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer
from srai.embedders import ContextualCountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer

Data preparation¶

In order to use ContextualCountEmbedder 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.

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from srai.regionalizers import geocode_to_region_gdf

area_gdf = geocode_to_region_gdf("Lisboa, PT")
plot_regions(area_gdf)
from srai.regionalizers import geocode_to_region_gdf

area_gdf = geocode_to_region_gdf("Lisboa, PT")
plot_regions(area_gdf)

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Regionalize the area using an H3Regionalizer¶

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regionalizer = H3Regionalizer(resolution=9, buffer=True)
regions_gdf = regionalizer.transform(area_gdf)
regions_gdf
regionalizer = H3Regionalizer(resolution=9, buffer=True)
regions_gdf = regionalizer.transform(area_gdf)
regions_gdf

Out[3]:

	geometry
region_id
89393362a1bffff	POLYGON ((-9.188 38.70896, -9.1897 38.70762, -...
893933628a3ffff	POLYGON ((-9.17016 38.73948, -9.17187 38.73814...
893933628dbffff	POLYGON ((-9.19137 38.7322, -9.19308 38.73086,...
89393375823ffff	POLYGON ((-9.12196 38.78208, -9.12367 38.78074...
89393375a6bffff	POLYGON ((-9.13688 38.74919, -9.13859 38.74785...
...	...
893933759b7ffff	POLYGON ((-9.09747 38.79896, -9.09918 38.79763...
89393362893ffff	POLYGON ((-9.18409 38.74264, -9.18579 38.7413,...
893933664cbffff	POLYGON ((-9.08867 38.79177, -9.09037 38.79043...
89393375a4bffff	POLYGON ((-9.14787 38.74996, -9.14957 38.74862...
89393375877ffff	POLYGON ((-9.12708 38.77806, -9.12878 38.77672...

830 rows × 1 columns

Download some objects from OpenStreetMap¶

You can use both OsmTagsFilter and GroupedOsmTagsFilter filters. In this example, a predefined GroupedOsmTagsFilter filter BASE_OSM_GROUPS_FILTER is used.

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from srai.loaders.osm_loaders.filters import BASE_OSM_GROUPS_FILTER

loader = OSMPbfLoader()
features_gdf = loader.load(area_gdf, tags=BASE_OSM_GROUPS_FILTER)
features_gdf
from srai.loaders.osm_loaders.filters import BASE_OSM_GROUPS_FILTER

loader = OSMPbfLoader()
features_gdf = loader.load(area_gdf, tags=BASE_OSM_GROUPS_FILTER)
features_gdf

Finished operation in 0:00:16

Out[4]:

	geometry	aerialway	airports	buildings	culture_art_entertainment	education	emergency	finances	greenery	healthcare	historic	leisure	other	shops	sport	sustenance	tourism	transportation	water
feature_id
node/1662570350	POINT (-9.13652 38.75509)	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=parking	None
node/1662570365	POINT (-9.13501 38.75859)	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=parking	None
node/1662570400	POINT (-9.13817 38.75503)	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=parking	None
node/1662570428	POINT (-9.13233 38.75529)	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=parking	None
node/1662570468	POINT (-9.13822 38.75534)	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=restaurant	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation/12990755	POLYGON ((-9.1692 38.70779, -9.16909 38.70789,...	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	amenity=parking	None
relation/13884079	POLYGON ((-9.13321 38.74216, -9.13322 38.74213...	None	None	None	None	None	None	None	landuse=grass	None	None	None	None	None	None	None	None	None	None
relation/8306287	POLYGON ((-9.14249 38.71874, -9.14251 38.71877...	None	None	None	None	None	None	None	None	None	historic=castle	None	None	None	None	None	None	None	None
relation/11318814	POLYGON ((-9.21057 38.697, -9.2103 38.69651, -...	None	None	None	None	None	None	None	None	None	None	leisure=garden	None	None	None	None	None	None	None
relation/15475183	POLYGON ((-9.20112 38.69844, -9.20106 38.69825...	None	None	None	None	None	None	None	None	None	historic=castle	None	None	None	None	None	tourism=attraction	None	None

33475 rows × 19 columns

Join the objects with the regions they belong to¶

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

Out[5]:


region_id	feature_id
89393375a0fffff	node/1662570350
89393375a07ffff	node/1662570365
89393375a0fffff	node/1662570400
89393375a3bffff	node/1662570428
89393375a0fffff	node/1662570468
...	...
89393362a27ffff	relation/12990755
893933674dbffff	relation/13884079
893933676dbffff	relation/8306287
89393360573ffff	relation/11318814
8939336052bffff	relation/15475183

37084 rows × 0 columns

Embed using features existing in data¶

ContextualCountEmbedder extends capabilities of basic CountEmbedder by incorporating the neighbourhood of embedded region. In this example we will use the H3Neighbourhood.

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h3n = H3Neighbourhood()
h3n = H3Neighbourhood()

Squashed vector version (default)¶

Embedder will return vector of the same length as CountEmbedder, but will sum averaged values from the neighbourhoods diminished by the neighbour distance squared.

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cce = ContextualCountEmbedder(
    neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=False
)
embeddings = cce.transform(regions_gdf, features_gdf, joint_gdf)
embeddings
cce = ContextualCountEmbedder(
    neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=False
)
embeddings = cce.transform(regions_gdf, features_gdf, joint_gdf)
embeddings

Out[7]:

	aerialway	airports	buildings	culture_art_entertainment	education	emergency	finances	greenery	healthcare	historic	leisure	other	shops	sport	sustenance	tourism	transportation	water
region_id
89393362a1bffff	0.000000	0.000000	0.716885	0.176462	3.819916	0.032079	0.160717	10.420602	0.398342	0.616710	5.976354	0.418704	4.168007	1.566271	5.242159	1.027607	9.238555	0.394107
893933628a3ffff	0.034722	0.000000	7.259548	0.136688	0.613641	0.060180	1.461331	12.443794	2.758658	0.471053	4.153300	0.444289	2.912707	0.701843	4.154008	1.963062	23.090063	0.387077
893933628dbffff	0.002028	0.000000	0.332965	0.027268	0.135732	0.004393	0.048098	1.980143	0.096074	0.157226	5.026685	0.226721	0.642310	2.034423	0.364559	1.730458	6.289365	0.075055
89393375823ffff	0.002057	0.240252	0.387444	0.057641	2.337761	0.027099	0.099275	38.663327	0.221804	0.194872	10.059764	0.096011	3.305428	3.358242	1.741261	0.279932	35.494369	0.042457
89393375a6bffff	0.000950	0.011091	1.202681	1.151128	2.948880	0.018330	0.616595	6.311057	3.017699	1.230854	14.881460	1.382330	10.132226	1.920024	5.294255	2.198081	54.422227	0.094769
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
893933759b7ffff	0.003893	0.005209	0.229245	0.007306	0.286064	0.014414	0.040057	2.198360	0.095918	0.023439	4.056901	0.058804	0.459404	0.921661	0.322675	0.170430	3.691681	1.456921
89393362893ffff	0.013750	0.000000	0.633183	0.043639	0.560733	0.007607	0.153460	1.548746	0.333937	1.524210	4.109246	0.370130	2.848712	0.442937	1.266099	3.467760	4.452792	0.298909
893933664cbffff	0.023845	0.003195	0.499302	0.011176	0.194958	0.027209	0.094964	1.308434	0.135196	0.029546	1.904503	0.082114	0.899838	0.844691	0.694791	0.370233	2.341689	1.668322
89393375a4bffff	0.002999	0.010621	12.728852	2.195494	1.302893	0.074019	3.502840	20.346454	3.113338	1.440307	3.387658	1.407813	16.101068	0.889805	10.962789	3.856779	66.649478	0.207773
89393375877ffff	0.001461	1.363276	5.562409	0.056164	1.224696	0.100400	0.132622	7.089663	0.134208	0.081780	10.303234	0.144550	0.886331	0.367130	0.602830	0.309512	44.635466	0.035165

830 rows × 18 columns

Concatenated vector version¶

Embedder will return vector of length n * distance where n is number of features from the CountEmbedder and distance is number of neighbourhoods analysed.

Each feature will be postfixed with _n string, where n is the current distance. Values are averaged from all neighbours.

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wide_cce = ContextualCountEmbedder(
    neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=True
)
wide_embeddings = wide_cce.transform(regions_gdf, features_gdf, joint_gdf)
wide_embeddings
wide_cce = ContextualCountEmbedder(
    neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=True
)
wide_embeddings = wide_cce.transform(regions_gdf, features_gdf, joint_gdf)
wide_embeddings

Out[8]:

	aerialway_0	airports_0	buildings_0	culture_art_entertainment_0	education_0	emergency_0	finances_0	greenery_0	healthcare_0	historic_0	...	healthcare_10	historic_10	leisure_10	other_10	shops_10	sport_10	sustenance_10	tourism_10	transportation_10	water_10
region_id
89393362a1bffff	0.0	0.0	0.0	0.0	3.0	0.0	0.0	7.0	0.0	0.0	...	1.093750	2.406250	3.531250	1.000000	6.093750	0.593750	6.062500	3.562500	12.562500	0.437500
893933628a3ffff	0.0	0.0	6.0	0.0	0.0	0.0	1.0	7.0	2.0	0.0	...	1.366667	1.966667	4.983333	1.216667	12.883333	1.183333	10.866667	3.800000	17.066667	0.383333
893933628dbffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	1.644444	2.000000	6.266667	1.000000	7.044444	1.600000	7.822222	3.755556	13.844444	1.022222
89393375823ffff	0.0	0.0	0.0	0.0	2.0	0.0	0.0	34.0	0.0	0.0	...	1.090909	0.242424	3.969697	0.393939	7.606061	1.272727	2.939394	1.333333	11.606061	0.484848
89393375a6bffff	0.0	0.0	0.0	1.0	2.0	0.0	0.0	3.0	2.0	1.0	...	1.350000	1.583333	4.266667	0.983333	7.350000	1.200000	6.083333	2.950000	15.666667	0.433333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
893933759b7ffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.571429	0.071429	6.285714	0.357143	10.571429	1.214286	4.071429	1.428571	20.285714	0.857143
89393362893ffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	1.697674	1.418605	4.023256	0.860465	7.534884	1.116279	5.558140	4.046512	17.976744	0.372093
893933664cbffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.000000	0.000000	5.928571	0.285714	3.500000	1.214286	1.714286	0.428571	15.071429	0.357143
89393375a4bffff	0.0	0.0	11.0	2.0	0.0	0.0	3.0	17.0	2.0	1.0	...	0.683333	1.116667	4.383333	0.700000	5.300000	1.500000	4.200000	3.116667	13.316667	0.450000
89393375877ffff	0.0	1.0	5.0	0.0	1.0	0.0	0.0	3.0	0.0	0.0	...	0.976744	0.418605	3.674419	0.348837	4.813953	1.139535	2.813953	1.813953	13.813953	0.767442

830 rows × 198 columns

Plotting example features¶

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plot_numeric_data(regions_gdf, "leisure", embeddings)
plot_numeric_data(regions_gdf, "leisure", embeddings)

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plot_numeric_data(regions_gdf, "transportation", embeddings)
plot_numeric_data(regions_gdf, "transportation", embeddings)

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Other types of aggregations¶

By default, the ContextualCountEmbedder averages the counts from neighbours. This aggregation_function can be changed to one of: median, sum, min, max.

It's best to combine it with the wide format (concatenate_vectors=True).

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sum_cce = ContextualCountEmbedder(
    neighbourhood=h3n,
    neighbourhood_distance=10,
    concatenate_vectors=True,
    aggregation_function="sum",
)
sum_embeddings = sum_cce.transform(regions_gdf, features_gdf, joint_gdf)
sum_embeddings
sum_cce = ContextualCountEmbedder(
    neighbourhood=h3n,
    neighbourhood_distance=10,
    concatenate_vectors=True,
    aggregation_function="sum",
)
sum_embeddings = sum_cce.transform(regions_gdf, features_gdf, joint_gdf)
sum_embeddings

Out[11]:

	aerialway_0	airports_0	buildings_0	culture_art_entertainment_0	education_0	emergency_0	finances_0	greenery_0	healthcare_0	historic_0	...	healthcare_10	historic_10	leisure_10	other_10	shops_10	sport_10	sustenance_10	tourism_10	transportation_10	water_10
region_id
89393362a1bffff	0.0	0.0	0.0	0.0	3.0	0.0	0.0	7.0	0.0	0.0	...	35.0	77.0	113.0	32.0	195.0	19.0	194.0	114.0	402.0	14.0
893933628a3ffff	0.0	0.0	6.0	0.0	0.0	0.0	1.0	7.0	2.0	0.0	...	82.0	118.0	299.0	73.0	773.0	71.0	652.0	228.0	1024.0	23.0
893933628dbffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	74.0	90.0	282.0	45.0	317.0	72.0	352.0	169.0	623.0	46.0
89393375823ffff	0.0	0.0	0.0	0.0	2.0	0.0	0.0	34.0	0.0	0.0	...	36.0	8.0	131.0	13.0	251.0	42.0	97.0	44.0	383.0	16.0
89393375a6bffff	0.0	0.0	0.0	1.0	2.0	0.0	0.0	3.0	2.0	1.0	...	81.0	95.0	256.0	59.0	441.0	72.0	365.0	177.0	940.0	26.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
893933759b7ffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	8.0	1.0	88.0	5.0	148.0	17.0	57.0	20.0	284.0	12.0
89393362893ffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	73.0	61.0	173.0	37.0	324.0	48.0	239.0	174.0	773.0	16.0
893933664cbffff	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	14.0	0.0	83.0	4.0	49.0	17.0	24.0	6.0	211.0	5.0
89393375a4bffff	0.0	0.0	11.0	2.0	0.0	0.0	3.0	17.0	2.0	1.0	...	41.0	67.0	263.0	42.0	318.0	90.0	252.0	187.0	799.0	27.0
89393375877ffff	0.0	1.0	5.0	0.0	1.0	0.0	0.0	3.0	0.0	0.0	...	42.0	18.0	158.0	15.0	207.0	49.0	121.0	78.0	594.0	33.0

830 rows × 198 columns

In [12]:

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plot_numeric_data(regions_gdf, "tourism_8", sum_embeddings)
plot_numeric_data(regions_gdf, "tourism_8", sum_embeddings)

Out[12]:

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