Contextual count embedder
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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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Make this Notebook Trusted to load map: File -> Trust Notebook
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 | |
| 89393375807ffff | POLYGON ((-9.12892 38.78367, -9.13063 38.78233... |
| 89393362903ffff | POLYGON ((-9.14820 38.73793, -9.14990 38.73659... |
| 89393375e37ffff | POLYGON ((-9.14905 38.77961, -9.15076 38.77827... |
| 89393362b77ffff | POLYGON ((-9.14994 38.71068, -9.15165 38.70933... |
| 89393367097ffff | POLYGON ((-9.10318 38.73804, -9.10489 38.73671... |
| ... | ... |
| 89393375a27ffff | POLYGON ((-9.12557 38.76043, -9.12727 38.75909... |
| 89393362cb7ffff | POLYGON ((-9.18964 38.75946, -9.19134 38.75812... |
| 89393367683ffff | POLYGON ((-9.13243 38.72916, -9.13414 38.72782... |
| 8939337535bffff | POLYGON ((-9.19954 38.76344, -9.20125 38.76210... |
| 8939337595bffff | POLYGON ((-9.11500 38.78050, -9.11671 38.77916... |
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
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/srai/loaders/osm_loaders/osm_pbf_loader.py:128: FutureWarning: Use `convert_geometry_to_geodataframe` instead. Deprecated since 0.8.1 version. features_gdf = pbf_reader.get_features_gdf_from_geometry(
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Finished operation in 0:01:23
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/259112393 | POINT (-9.16792 38.76113) | None | None | None | None | None | None | None | None | None | None | None | amenity=post_office | None | None | None | None | None | None |
| node/259113774 | POINT (-9.16954 38.76066) | None | None | None | None | None | None | amenity=bank | None | None | None | None | None | None | None | None | None | None | None |
| node/277831550 | POINT (-9.16476 38.73881) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| node/277831931 | POINT (-9.15994 38.73804) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| node/277834117 | POINT (-9.15942 38.75257) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| relation/18073831 | POLYGON ((-9.16824 38.70755, -9.16811 38.70766... | None | None | None | None | None | None | None | None | None | None | leisure=garden | None | None | None | None | None | None | None |
| relation/18114682 | POLYGON ((-9.19031 38.74668, -9.18983 38.74664... | None | None | None | None | None | None | None | None | amenity=clinic | None | None | None | None | None | None | None | None | None |
| relation/18129628 | POLYGON ((-9.14697 38.71892, -9.14695 38.71867... | None | None | None | amenity=theatre | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
| relation/18168748 | POLYGON ((-9.17061 38.75452, -9.17057 38.75449... | None | None | None | None | None | None | None | landuse=recreation_ground | None | None | None | None | None | None | None | None | None | None |
| relation/18216967 | POLYGON ((-9.13568 38.71231, -9.13578 38.71233... | None | None | None | None | None | None | None | None | amenity=clinic | historic=castle | None | None | None | None | None | None | None | None |
31730 rows × 19 columns
Join the objects with the regions they belong to¶
In [5]:
Copied!
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 |
|---|---|
| 89393375807ffff | way/336112741 |
| way/857632816 | |
| way/336112743 | |
| way/246197499 | |
| way/336146807 | |
| ... | ... |
| 8939337595bffff | way/784894014 |
| node/5746421700 | |
| way/891675661 | |
| way/216524628 | |
| way/531347329 |
35256 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.
In [6]:
Copied!
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.
In [7]:
Copied!
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
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Generating embeddings for neighbours: 100%|██████████| 10/10 [00:03<00:00, 2.97it/s]
Out[7]:
| aerialway | airports | buildings | culture_art_entertainment | education | emergency | finances | greenery | healthcare | historic | leisure | other | shops | sport | sustenance | tourism | transportation | water | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region_id | ||||||||||||||||||
| 89393375807ffff | 0.000424 | 2.522626 | 0.202267 | 0.023442 | 0.196962 | 0.036525 | 0.062314 | 5.735401 | 0.075130 | 0.038168 | 2.346146 | 1.093156 | 0.523263 | 0.223166 | 0.352128 | 0.135426 | 8.539636 | 0.031633 |
| 89393362903ffff | 0.001940 | 0.001698 | 12.409412 | 1.353760 | 7.261210 | 0.042150 | 10.452963 | 9.422425 | 7.618586 | 0.945735 | 8.270947 | 1.623016 | 36.309447 | 0.473300 | 24.248795 | 8.817891 | 44.415862 | 0.321079 |
| 89393375e37ffff | 0.000000 | 0.143304 | 0.224910 | 0.064807 | 0.653069 | 0.011579 | 0.066747 | 13.999185 | 0.291650 | 0.073693 | 3.581006 | 0.118047 | 0.935143 | 1.611358 | 0.649094 | 4.206955 | 10.095597 | 1.195257 |
| 89393362b77ffff | 0.000000 | 0.000000 | 6.817581 | 0.729065 | 2.049014 | 0.076358 | 2.907855 | 4.456693 | 2.094359 | 9.132607 | 5.523225 | 3.482696 | 27.225454 | 0.297701 | 58.750673 | 25.605754 | 29.185735 | 0.860737 |
| 89393367097ffff | 0.001709 | 0.000000 | 6.942717 | 0.262311 | 0.266154 | 0.002019 | 0.065782 | 2.840018 | 0.253256 | 0.289361 | 1.303416 | 1.365994 | 1.724712 | 0.417280 | 3.397065 | 0.585443 | 16.755198 | 2.490156 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 89393375a27ffff | 0.001658 | 0.044809 | 0.338675 | 0.098740 | 3.498379 | 0.012504 | 0.116507 | 18.024361 | 1.260719 | 0.053485 | 8.944176 | 0.156641 | 12.324194 | 4.875606 | 3.781297 | 0.390916 | 17.238997 | 0.246963 |
| 89393362cb7ffff | 0.004139 | 0.000000 | 1.988572 | 0.341248 | 0.747185 | 0.044313 | 0.483579 | 6.850177 | 0.550667 | 1.584998 | 6.457678 | 0.428569 | 10.724208 | 2.003579 | 3.130296 | 0.777097 | 10.226391 | 0.127299 |
| 89393367683ffff | 0.000000 | 0.000000 | 2.191808 | 0.294570 | 6.114881 | 0.017328 | 1.331265 | 3.302630 | 2.345614 | 0.660878 | 4.392056 | 1.334071 | 37.455788 | 0.620049 | 19.487535 | 6.469591 | 38.132233 | 0.131340 |
| 8939337535bffff | 0.002129 | 0.000000 | 0.421833 | 0.108654 | 0.477528 | 0.006600 | 0.161815 | 9.306525 | 0.349763 | 0.310472 | 3.469307 | 0.252805 | 2.428283 | 0.380434 | 1.033579 | 0.364513 | 27.715797 | 0.052521 |
| 8939337595bffff | 0.004116 | 0.061044 | 0.354094 | 0.059926 | 2.432663 | 0.017368 | 0.181453 | 6.757675 | 0.287201 | 1.186616 | 7.522743 | 0.114218 | 1.823443 | 1.557880 | 0.881500 | 0.276990 | 20.818908 | 0.079381 |
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.
In [8]:
Copied!
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
Generating embeddings for neighbours: 100%|██████████| 10/10 [00:03<00:00, 2.92it/s]
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 | |||||||||||||||||||||
| 89393375807ffff | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | ... | 1.102564 | 0.256410 | 3.948718 | 0.307692 | 5.820513 | 1.564103 | 3.923077 | 1.333333 | 11.128205 | 1.179487 |
| 89393362903ffff | 0.0 | 0.0 | 9.0 | 1.0 | 6.0 | 0.0 | 9.0 | 6.0 | 6.0 | 0.0 | ... | 0.644068 | 1.033898 | 3.152542 | 0.559322 | 4.508475 | 0.898305 | 6.610169 | 2.694915 | 10.661017 | 0.779661 |
| 89393375e37ffff | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 11.0 | 0.0 | 0.0 | ... | 1.322581 | 0.193548 | 4.870968 | 0.451613 | 4.451613 | 2.161290 | 2.806452 | 1.032258 | 14.870968 | 0.000000 |
| 89393362b77ffff | 0.0 | 0.0 | 4.0 | 0.0 | 1.0 | 0.0 | 1.0 | 2.0 | 1.0 | 6.0 | ... | 1.129032 | 1.032258 | 3.064516 | 0.870968 | 10.064516 | 0.709677 | 6.096774 | 3.419355 | 17.064516 | 0.612903 |
| 89393367097ffff | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 1.321429 | 1.214286 | 4.928571 | 1.750000 | 14.357143 | 1.392857 | 9.321429 | 3.785714 | 22.892857 | 0.821429 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 89393375a27ffff | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 14.0 | 1.0 | 0.0 | ... | 1.289474 | 0.526316 | 5.605263 | 0.710526 | 8.473684 | 2.394737 | 6.131579 | 1.947368 | 16.368421 | 0.500000 |
| 89393362cb7ffff | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 0.0 | 1.0 | ... | 0.516129 | 0.419355 | 3.580645 | 0.419355 | 1.354839 | 1.387097 | 1.709677 | 1.806452 | 11.483871 | 0.322581 |
| 89393367683ffff | 0.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | ... | 1.228571 | 0.857143 | 5.685714 | 0.914286 | 7.257143 | 2.257143 | 5.142857 | 2.342857 | 16.800000 | 0.400000 |
| 8939337535bffff | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 0.0 | 0.0 | ... | 0.692308 | 0.846154 | 5.115385 | 0.346154 | 2.653846 | 2.961538 | 1.730769 | 1.538462 | 10.769231 | 0.500000 |
| 8939337595bffff | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 2.0 | 0.0 | 1.0 | ... | 0.592593 | 0.111111 | 2.481481 | 0.296296 | 1.481481 | 0.666667 | 1.333333 | 1.481481 | 7.666667 | 0.518519 |
830 rows × 198 columns
Plotting example features¶
In [9]:
Copied!
plot_numeric_data(regions_gdf, "leisure", embeddings)
plot_numeric_data(regions_gdf, "leisure", embeddings)
Out[9]:
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In [10]:
Copied!
plot_numeric_data(regions_gdf, "transportation", embeddings)
plot_numeric_data(regions_gdf, "transportation", embeddings)
Out[10]:
Make this Notebook Trusted to load map: File -> Trust Notebook