Overture Maps Loader¶

OvertureMapsLoader can download the Overture Maps data from the s3 bucket for a given region.

It is a wrapper around the OvertureMaestro library that can download the data in the original format but also have some advanced functions.

In the SRAI context, OvertureMapsLoader utilizes so-called wide format for returning features with columns representing potential categories of the object. If you want to read more in-depth about this format, you can checkout this OvertureMaestro's docs page.

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 shapely.geometry import box

from srai.constants import GEOMETRY_COLUMN
from srai.loaders import OvertureMapsLoader
from srai.regionalizers import geocode_to_region_gdf
from shapely.geometry import box

from srai.constants import GEOMETRY_COLUMN
from srai.loaders import OvertureMapsLoader
from srai.regionalizers import geocode_to_region_gdf

Using OvertureMapsLoader to download data for a specific area¶

Download all available features in Paris, France¶

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loader = OvertureMapsLoader()
paris = geocode_to_region_gdf("Paris")
paris_features_gdf = loader.load(paris)
paris_features_gdf
loader = OvertureMapsLoader()
paris = geocode_to_region_gdf("Paris")
paris_features_gdf = loader.load(paris)
paris_features_gdf

Finished operation in 0:00:27

Out[2]:

	geometry	base\|infrastructure\|aerialway	base\|infrastructure\|airport	base\|infrastructure\|barrier	base\|infrastructure\|bridge	base\|infrastructure\|communication	base\|infrastructure\|emergency	base\|infrastructure\|manhole	base\|infrastructure\|pedestrian	base\|infrastructure\|pier	...	places\|place\|professional_services	places\|place\|public_service_and_government	places\|place\|real_estate	places\|place\|religious_organization	places\|place\|retail	places\|place\|structure_and_geography	places\|place\|travel	transportation\|segment\|rail	transportation\|segment\|road	transportation\|segment\|water
feature_id
08b1fb466e895fff0001bd163118da4a	LINESTRING (2.41281 48.86676, 2.4135 48.8673, ...	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466e882fff0001b56bea9efdfc	LINESTRING (2.41396 48.86759, 2.41378 48.86744...	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466e882fff0001b74ff032275e	LINESTRING (2.41374 48.86723, 2.41377 48.86728...	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466e882fff0001af362bfc6a09	POINT (2.41388 48.86739)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466e886fff0001a478e49ef9a6	POINT (2.41378 48.86744)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
08b1fb4662850fff02002b554c456958	POLYGON ((2.35874 48.86704, 2.35877 48.86704, ...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466284bfff02001eb6226a4658	POLYGON ((2.36154 48.86714, 2.36157 48.86713, ...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb4662ba4fff0200b09be1618aee	POLYGON ((2.36149 48.86705, 2.36154 48.86713, ...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb4662b16fff020030f2b670d99e	POLYGON ((2.36233 48.86724, 2.3623 48.86718, 2...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b1fb466284bfff0200b13fbcd2b6e9	POLYGON ((2.36187 48.86699, 2.36195 48.86715, ...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

572036 rows × 115 columns

Plot features¶

Colours from the this palette: https://colorhunt.co/palette/f8ededff8225b43f3f173b45

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ax = paris.plot(color="#F8EDED", figsize=(16, 16))

# plot water
water_columns = [c for c in paris_features_gdf.columns if "water" in c]
water_data = paris_features_gdf[paris_features_gdf[water_columns].any(axis=1)]
water_data.plot(ax=ax, color="#FF8225", markersize=0)

# plot_roads
roads_data = paris_features_gdf[paris_features_gdf["transportation|segment|road"]]
roads_data.plot(ax=ax, color="#B43F3F", markersize=0, linewidth=0.25)

# plot buildings
building_columns = [c for c in paris_features_gdf.columns if c.startswith("buildings")]
buildings_data = paris_features_gdf[paris_features_gdf[building_columns].any(axis=1)]
buildings_data.plot(ax=ax, color="#173B45", markersize=0)

paris.boundary.plot(ax=ax, color="#173B45", linewidth=2, alpha=0.5)

xmin, ymin, xmax, ymax = paris.total_bounds
ax.set_xlim(xmin - 0.001, xmax + 0.001)
ax.set_ylim(ymin - 0.001, ymax + 0.001)

ax.set_axis_off()
ax = paris.plot(color="#F8EDED", figsize=(16, 16))

# plot water
water_columns = [c for c in paris_features_gdf.columns if "water" in c]
water_data = paris_features_gdf[paris_features_gdf[water_columns].any(axis=1)]
water_data.plot(ax=ax, color="#FF8225", markersize=0)

# plot_roads
roads_data = paris_features_gdf[paris_features_gdf["transportation|segment|road"]]
roads_data.plot(ax=ax, color="#B43F3F", markersize=0, linewidth=0.25)

# plot buildings
building_columns = [c for c in paris_features_gdf.columns if c.startswith("buildings")]
buildings_data = paris_features_gdf[paris_features_gdf[building_columns].any(axis=1)]
buildings_data.plot(ax=ax, color="#173B45", markersize=0)

paris.boundary.plot(ax=ax, color="#173B45", linewidth=2, alpha=0.5)

xmin, ymin, xmax, ymax = paris.total_bounds
ax.set_xlim(xmin - 0.001, xmax + 0.001)
ax.set_ylim(ymin - 0.001, ymax + 0.001)

ax.set_axis_off()

No description has been provided for this image

Download more detailed data with higher hierarchy value¶

By default, the hierarchy_depth value is equal to 1, but it can be set to None to get a full list of all possible columns.

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manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
loader = OvertureMapsLoader(hierarchy_depth=None)
new_york_features_gdf = loader.load(manhattan_bbox)
new_york_features_gdf
manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
loader = OvertureMapsLoader(hierarchy_depth=None)
new_york_features_gdf = loader.load(manhattan_bbox)
new_york_features_gdf

Finished operation in 0:00:31

Out[4]:

	geometry	base\|infrastructure\|aerialway\|aerialway_station	base\|infrastructure\|aerialway\|cable_car	base\|infrastructure\|aerialway\|chair_lift	base\|infrastructure\|aerialway\|drag_lift	base\|infrastructure\|aerialway\|gondola	base\|infrastructure\|aerialway\|goods	base\|infrastructure\|aerialway\|j-bar	base\|infrastructure\|aerialway\|magic_carpet	base\|infrastructure\|aerialway\|mixed_lift	...	transportation\|segment\|road\|service\|parking_aisle	transportation\|segment\|road\|steps	transportation\|segment\|road\|tertiary	transportation\|segment\|road\|tertiary\|link	transportation\|segment\|road\|track	transportation\|segment\|road\|trunk	transportation\|segment\|road\|trunk\|link	transportation\|segment\|road\|unclassified	transportation\|segment\|road\|unknown	transportation\|segment\|water
feature_id
08b2a100f32f0fff0001b39a4f0003f6	LINESTRING (-73.88819 40.77247, -73.88914 40.7...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a100d2ce9fff0001b50e5a03898d	LINESTRING (-73.97214 40.7268, -73.97341 40.72...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a10725843fff0001a5d4ee060f99	POINT (-73.99301 40.7627)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a10725850fff0001a15cab262e5f	POINT (-73.99422 40.76333)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a10725850fff0001aa0db52328c3	POINT (-73.99409 40.76328)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
08b2a100f2650fff02008c2684f54385	POLYGON ((-73.93679 40.77279, -73.93683 40.772...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a100f2656fff0200f667ed909313	POLYGON ((-73.93719 40.77324, -73.93714 40.773...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a100f260bfff0200ec356893da2f	POLYGON ((-73.93741 40.77379, -73.9375 40.7738...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a100f2604fff0200b0ba2bc875ff	POLYGON ((-73.93661 40.77495, -73.9363 40.7755...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
08b2a100f2622fff0200df47d5bd8207	POLYGON ((-73.93694 40.77586, -73.93697 40.775...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

65080 rows × 2633 columns

As you can see, there are over 2600 columns available.

Let's see top 20 most popular columns.

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new_york_features_gdf.drop(columns=GEOMETRY_COLUMN).sum().sort_values(ascending=False).head(20)
new_york_features_gdf.drop(columns=GEOMETRY_COLUMN).sum().sort_values(ascending=False).head(20)

Out[5]:

buildings|building                                                          12487
base|land|tree|tree                                                          5665
base|infrastructure|transportation|crossing                                  3846
transportation|segment|road|footway                                          3373
places|place|health_and_medical                                              3063
base|infrastructure|barrier|kerb                                             3005
places|place|health_and_medical|doctor                                       2350
transportation|segment|road|footway|sidewalk                                 2145
places|place|eat_and_drink|restaurant                                        1795
buildings|building|residential|apartments                                    1706
transportation|segment|road|footway|crosswalk                                1646
places|place|professional_services                                           1418
base|infrastructure|transit|bicycle_parking                                   994
base|infrastructure|transportation|traffic_signals                            990
places|place|beauty_and_spa                                                   875
places|place|health_and_medical|hospital                                      868
places|place|retail                                                           866
transportation|segment|road|residential                                       832
places|place|beauty_and_spa|beauty_salon                                      829
places|place|attractions_and_activities|landmark_and_historical_building      793
dtype: int64

Configure places dataset¶

Places schema is the only one that is treated differently than other data types.

By default, places use both primary and alternate categories to define a feature.

Additionally, there is a filter applied to get only features with confidence score >= 0.75.

There are two dedicated parameters: places_minimal_confidence and places_use_primary_category_only to configure how the data should be transformed.

Let's do example with both of these parameters. We will also use a theme_type_pairs parameter to limit the scope of the downloaded data.

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default_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")], places_use_primary_category_only=True
)
strict_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")],
    places_minimal_confidence=0.99,
    places_use_primary_category_only=True,
)
songpa = geocode_to_region_gdf("Songpa-gu, Seoul")
songpa_default_confidence_features_gdf = default_confidence_loader.load(songpa)
songpa_strict_confidence_features_gdf = strict_confidence_loader.load(songpa)

print(f"Default confidence score: {len(songpa_default_confidence_features_gdf)}")
print(f"Strict confidence score:  {len(songpa_strict_confidence_features_gdf)}")
default_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")], places_use_primary_category_only=True
)
strict_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")],
    places_minimal_confidence=0.99,
    places_use_primary_category_only=True,
)
songpa = geocode_to_region_gdf("Songpa-gu, Seoul")
songpa_default_confidence_features_gdf = default_confidence_loader.load(songpa)
songpa_strict_confidence_features_gdf = strict_confidence_loader.load(songpa)

print(f"Default confidence score: {len(songpa_default_confidence_features_gdf)}")
print(f"Strict confidence score:  {len(songpa_strict_confidence_features_gdf)}")

Finished operation in 0:00:03

Finished operation in 0:00:03

Default confidence score: 5306
Strict confidence score:  8

Let's see the count of categories in the places dataset with confidence score >= 0.99.

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songpa_strict_confidence_features_df = songpa_strict_confidence_features_gdf.drop(
    columns=GEOMETRY_COLUMN
)
songpa_strict_confidence_features_df.sum().loc[lambda x: x > 0].sort_values(ascending=False)
songpa_strict_confidence_features_df = songpa_strict_confidence_features_gdf.drop(
    columns=GEOMETRY_COLUMN
)
songpa_strict_confidence_features_df.sum().loc[lambda x: x > 0].sort_values(ascending=False)

Out[7]:

places|place|retail            7
places|place|beauty_and_spa    1
dtype: int64

Plot features¶

Now we will see the difference between default list of places (gray dots) and strict ones (coloured circles)