Wide format¶

OvertureMaestro implements a logic for transforming downloaded data into a wide format. This format is dedicated for geospatial machine learning usage, where selected datasets are pivoted based on their categories to a columnar format.

This notebook will explore what is this format and how to work with it.

New functions¶

New module contains the same set of functions as the basic api, just with the wide_form part inside:

convert_geometry_to_parquet → convert_geometry_to_wide_form_parquet
convert_geometry_to_geodataframe → convert_geometry_to_wide_form_geodataframe
other functions ...

Additionally, special functions for downloading all available datasets are available:

convert_geometry_to_wide_form_parquet_for_all_types
convert_geometry_to_wide_form_geodataframe_for_all_types
convert_bounding_box_to_wide_form_parquet_for_all_types
convert_bounding_box_to_wide_form_geodataframe_for_all_types

You can import them from the overturemaestro.advanced_functions module.

Run this notebook in Google Colab:

Remember to install the overturemaestro library before running the notebook:

%pip install overturemaestro[cli]

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from overturemaestro import convert_geometry_to_geodataframe, geocode_to_geometry
from overturemaestro.advanced_functions import convert_geometry_to_wide_form_geodataframe
from overturemaestro import convert_geometry_to_geodataframe, geocode_to_geometry
from overturemaestro.advanced_functions import convert_geometry_to_wide_form_geodataframe

What is the wide format?¶

In this section we will compare how the original data format differs from the wide format based on water data.

Let's start by looking at the official Overture Maps schema for the base water data type:

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import requests
import yaml

response = requests.get(
    "https://raw.githubusercontent.com/OvertureMaps/schema/refs/tags/v1.4.0/schema/base/water.yaml",
    allow_redirects=True,
)
water_schema = yaml.safe_load(response.content.decode("utf-8"))
water_schema
import requests
import yaml

response = requests.get(
    "https://raw.githubusercontent.com/OvertureMaps/schema/refs/tags/v1.4.0/schema/base/water.yaml",
    allow_redirects=True,
)
water_schema = yaml.safe_load(response.content.decode("utf-8"))
water_schema

Out[2]:

{'$schema': 'https://json-schema.org/draft/2020-12/schema',
 'title': 'water',
 'description': 'Physical representations of inland and ocean marine surfaces. Translates `natural` and `waterway` tags from OpenStreetMap.',
 'type': 'object',
 'properties': {'id': {'$ref': '../defs.yaml#/$defs/propertyDefinitions/id'},
  'geometry': {'unevaluatedProperties': False,
   'oneOf': [{'$ref': 'https://geojson.org/schema/Point.json'},
    {'$ref': 'https://geojson.org/schema/LineString.json'},
    {'$ref': 'https://geojson.org/schema/Polygon.json'},
    {'$ref': 'https://geojson.org/schema/MultiPolygon.json'}]},
  'properties': {'unevaluatedProperties': False,
   'allOf': [{'$ref': '../defs.yaml#/$defs/propertyContainers/overtureFeaturePropertiesContainer'},
    {'$ref': '../defs.yaml#/$defs/propertyContainers/levelContainer'},
    {'$ref': '../defs.yaml#/$defs/propertyContainers/namesContainer'},
    {'$ref': './defs.yaml#/$defs/propertyContainers/osmPropertiesContainer'}],
   'required': ['subtype', 'class'],
   'properties': {'subtype': {'description': 'The type of water body such as an river, ocean or lake.',
     'default': ['water'],
     'type': 'string',
     'enum': ['canal',
      'human_made',
      'lake',
      'ocean',
      'physical',
      'pond',
      'reservoir',
      'river',
      'spring',
      'stream',
      'wastewater',
      'water']},
    'class': {'description': 'Further description of the type of water',
     'default': ['water'],
     'enum': ['basin',
      'bay',
      'blowhole',
      'canal',
      'cape',
      'ditch',
      'dock',
      'drain',
      'fairway',
      'fish_pass',
      'fishpond',
      'geyser',
      'hot_spring',
      'lagoon',
      'lake',
      'moat',
      'ocean',
      'oxbow',
      'pond',
      'reflecting_pool',
      'reservoir',
      'river',
      'salt_pond',
      'sea',
      'sewage',
      'shoal',
      'spring',
      'strait',
      'stream',
      'swimming_pool',
      'tidal_channel',
      'wastewater',
      'water',
      'water_storage',
      'waterfall']},
    'is_salt': {'description': 'Is it salt water or not', 'type': 'boolean'},
    'is_intermittent': {'description': 'Is it intermittent water or not',
     'type': 'boolean'}}}}}

Two required fields are defined in the specification: subtype and class. There are even lists of possible values defined.

Both of these values detail the meaning of the feature. Together, everything maps to the path:

theme (base) → type (water) → subtype (eg. reservoir) → class (eg. basin).

Based on this hierarchy, all available values can be determined and mapped to columns.

In this way, you will obtain data in a wide format, where each feature defines what it is with boolean flags.

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amsterdam = geocode_to_geometry("Amsterdam")

original_data = convert_geometry_to_geodataframe("base", "water", amsterdam)
wide_data = convert_geometry_to_wide_form_geodataframe("base", "water", amsterdam)
amsterdam = geocode_to_geometry("Amsterdam")

original_data = convert_geometry_to_geodataframe("base", "water", amsterdam)
wide_data = convert_geometry_to_wide_form_geodataframe("base", "water", amsterdam)

Finished operation in 0:00:08

Finished operation in 0:00:10

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original_data
original_data

Out[4]:

	geometry	bbox	version	sources	level	subtype	class	names	source_tags	wikidata	is_salt	is_intermittent
id
3e3314a0-c979-366b-94ed-16c0bafd3bd5	POLYGON ((-72.99934 40.68825, -72.99967 40.688...	{'xmin': -74.00066375732422, 'xmax': -72.99933...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	ocean	ocean	{'primary': None, 'common': None, 'rules': None}	None	None	True	None
7198db90-6b71-3792-8947-b581d6c5b5c4	POLYGON ((-73.72785 40.66063, -73.72784 40.660...	{'xmin': -73.7278823852539, 'xmax': -73.727821...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	human_made	swimming_pool	{'primary': None, 'common': None, 'rules': None}	[(access, private), (leisure, swimming_pool)]	None	None	None
d3a3c77d-8078-3d98-b7b7-323323bacf44	LINESTRING (-73.72846 40.66632, -73.72851 40.6...	{'xmin': -73.728515625, 'xmax': -73.7254257202...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	-1.0	canal	drain	{'primary': 'Hook Creek', 'common': None, 'rul...	[(tunnel, culvert), (waterway, drain)]	None	None	None
722d3468-4f50-37a7-b809-1f0c1e7814ab	POLYGON ((-73.72678 40.65868, -73.72682 40.658...	{'xmin': -73.7268295288086, 'xmax': -73.726760...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	human_made	swimming_pool	{'primary': None, 'common': None, 'rules': None}	[(access, private), (leisure, swimming_pool)]	None	None	None
61f8df08-22b7-309b-a39d-1e27ffdea4c1	POLYGON ((-73.72903 40.66007, -73.72902 40.660...	{'xmin': -73.72909545898438, 'xmax': -73.72901...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	human_made	swimming_pool	{'primary': None, 'common': None, 'rules': None}	[(access, private), (leisure, swimming_pool)]	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...
1b55cc6a-4875-35bb-973c-7f78107e9349	POLYGON ((-73.81194 40.88809, -73.81196 40.888...	{'xmin': -73.81196594238281, 'xmax': -73.81179...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	human_made	swimming_pool	{'primary': None, 'common': None, 'rules': None}	[(access, private), (leisure, swimming_pool), ...	None	None	None
710cc072-2f90-3f24-847d-d994d2ff23e2	POLYGON ((-73.91045 40.91526, -73.91018 40.915...	{'xmin': -73.94308471679688, 'xmax': -73.88537...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	river	river	{'primary': None, 'common': None, 'rules': None}	[(intermittent, no), (natural, water), (source...	None	None	False
79dced11-730d-34b5-bb9c-803ddfeb40f0	LINESTRING (-73.88916 41.04345, -73.89008 41.0...	{'xmin': -73.93392944335938, 'xmax': -73.88914...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	river	river	{'primary': 'Hudson River', 'common': [('es', ...	[(boat, yes), (canoe, yes), (canoe:description...	None	None	False
e0ef7fc9-ba8a-3da1-b5c9-775faf05dca3	POLYGON ((-73.64509 41.0005, -73.64511 41.0004...	{'xmin': -74.00066375732422, 'xmax': -72.99933...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	ocean	ocean	{'primary': None, 'common': None, 'rules': None}	None	None	True	None
6b67cfae-059c-387b-9391-14e8d4946a7b	POLYGON ((-72.23316 41.16052, -72.2331 41.1604...	{'xmin': -73.80953979492188, 'xmax': -71.85736...	0	[{'property': '', 'dataset': 'OpenStreetMap', ...	NaN	physical	bay	{'primary': 'Long Island Sound', 'common': Non...	[(ele, 0), (gnis:feature_id, 977427), (natural...	Q867460	None	None

49065 rows × 12 columns

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wide_data
wide_data

Out[5]:

	geometry	base\|water\|canal\|canal	base\|water\|canal\|ditch	base\|water\|canal\|drain	base\|water\|canal\|moat	base\|water\|human_made\|fish_pass	base\|water\|human_made\|reflecting_pool	base\|water\|human_made\|salt_pond	base\|water\|human_made\|swimming_pool	base\|water\|lake\|lagoon	...	base\|water\|spring\|geyser	base\|water\|spring\|hot_spring	base\|water\|spring\|spring	base\|water\|stream\|stream	base\|water\|wastewater\|sewage	base\|water\|water\|dock	base\|water\|water\|fairway	base\|water\|water\|tidal_channel	base\|water\|water\|wastewater	base\|water\|water\|water
id
3e3314a0-c979-366b-94ed-16c0bafd3bd5	POLYGON ((-72.99934 40.68825, -72.99967 40.688...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7198db90-6b71-3792-8947-b581d6c5b5c4	POLYGON ((-73.72785 40.66063, -73.72784 40.660...	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
d3a3c77d-8078-3d98-b7b7-323323bacf44	LINESTRING (-73.72846 40.66632, -73.72851 40.6...	False	False	True	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
722d3468-4f50-37a7-b809-1f0c1e7814ab	POLYGON ((-73.72678 40.65868, -73.72682 40.658...	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
61f8df08-22b7-309b-a39d-1e27ffdea4c1	POLYGON ((-73.72903 40.66007, -73.72902 40.660...	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1b55cc6a-4875-35bb-973c-7f78107e9349	POLYGON ((-73.81194 40.88809, -73.81196 40.888...	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
710cc072-2f90-3f24-847d-d994d2ff23e2	POLYGON ((-73.91045 40.91526, -73.91018 40.915...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
79dced11-730d-34b5-bb9c-803ddfeb40f0	LINESTRING (-73.88916 41.04345, -73.89008 41.0...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
e0ef7fc9-ba8a-3da1-b5c9-775faf05dca3	POLYGON ((-73.64509 41.0005, -73.64511 41.0004...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
6b67cfae-059c-387b-9391-14e8d4946a7b	POLYGON ((-72.23316 41.16052, -72.2331 41.1604...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

49065 rows × 37 columns

Using this format, we can quickly filter out data or calculate number of features per category.

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wide_data.drop(columns="geometry").sum().sort_values(ascending=False)
wide_data.drop(columns="geometry").sum().sort_values(ascending=False)

Out[6]:

base|water|human_made|swimming_pool      46824
base|water|stream|stream                   665
base|water|water|water                     498
base|water|pond|pond                       352
base|water|river|river                     157
base|water|water|wastewater                123
base|water|canal|ditch                     110
base|water|reservoir|basin                  74
base|water|water|tidal_channel              65
base|water|physical|bay                     53
base|water|canal|drain                      37
base|water|physical|cape                    30
base|water|water|fairway                    14
base|water|canal|canal                      14
base|water|physical|waterfall               11
base|water|physical|shoal                    9
base|water|ocean|ocean                       9
base|water|lake|lake                         6
base|water|reservoir|reservoir               6
base|water|human_made|reflecting_pool        4
base|water|spring|spring                     2
base|water|physical|strait                   1
base|water|human_made|fish_pass              1
base|water|canal|moat                        0
base|water|water|dock                        0
base|water|wastewater|sewage                 0
base|water|human_made|salt_pond              0
base|water|physical|ocean                    0
base|water|spring|hot_spring                 0
base|water|spring|geyser                     0
base|water|physical|sea                      0
base|water|lake|lagoon                       0
base|water|reservoir|water_storage           0
base|water|lake|oxbow                        0
base|water|pond|fishpond                     0
base|water|spring|blowhole                   0
dtype: int64

Each theme type has defined list of columns used for generating final list of columns.

Most of the datasets have two columns (subtype and class) with three exceptions:

base|land_cover → subtype only
transportation|segment → subtype, class and subclass
places|place → 1, 2, 3, ... (this one is described in detail below)

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from overturemaestro.advanced_functions.wide_form import THEME_TYPE_CLASSIFICATION

for (theme_value, type_value), definition in sorted(THEME_TYPE_CLASSIFICATION.items()):
    print(theme_value, type_value, definition.hierachy_columns)
from overturemaestro.advanced_functions.wide_form import THEME_TYPE_CLASSIFICATION

for (theme_value, type_value), definition in sorted(THEME_TYPE_CLASSIFICATION.items()):
    print(theme_value, type_value, definition.hierachy_columns)

base infrastructure ['subtype', 'class']
base land ['subtype', 'class']
base land_cover ['subtype']
base land_use ['subtype', 'class']
base water ['subtype', 'class']
buildings building ['subtype', 'class']
places place ['1', '2', '3', '4', '5', '6']
transportation segment ['subtype', 'class', 'subclass']

Multiple data types¶

You can also download data for multiple data theme/types at once, or even download all at once.

If some datasets have been downloaded during previous executions, then only missing data is downloaded.

Here we will look at the top 10 most common features for both examples.

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from overturemaestro.advanced_functions import (
    convert_geometry_to_wide_form_geodataframe_for_all_types,
    convert_geometry_to_wide_form_geodataframe_for_multiple_types,
)

two_datasets_gdf = convert_geometry_to_wide_form_geodataframe_for_multiple_types(
    [("base", "water"), ("base", "land_cover")], amsterdam
)
two_datasets_gdf.drop(columns="geometry").sum().sort_values(ascending=False).head(10)
from overturemaestro.advanced_functions import (
    convert_geometry_to_wide_form_geodataframe_for_all_types,
    convert_geometry_to_wide_form_geodataframe_for_multiple_types,
)

two_datasets_gdf = convert_geometry_to_wide_form_geodataframe_for_multiple_types(
    [("base", "water"), ("base", "land_cover")], amsterdam
)
two_datasets_gdf.drop(columns="geometry").sum().sort_values(ascending=False).head(10)

Finished operation in 0:00:09

Out[8]:

base|water|human_made|swimming_pool    46824
base|water|stream|stream                 665
base|land_cover|shrub                    505
base|water|water|water                   498
base|land_cover|barren                   475
base|land_cover|forest                   442
base|water|pond|pond                     352
base|water|river|river                   157
base|land_cover|wetland                  129
base|water|water|wastewater              123
dtype: int64

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len(two_datasets_gdf.columns)
len(two_datasets_gdf.columns)

Out[9]:

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all_datasets_gdf = convert_geometry_to_wide_form_geodataframe_for_all_types(
    amsterdam, sort_result=False # we skip sorting the result here for faster execution
)
all_datasets_gdf.drop(columns="geometry").sum().sort_values(ascending=False).head(10)
all_datasets_gdf = convert_geometry_to_wide_form_geodataframe_for_all_types(
    amsterdam, sort_result=False # we skip sorting the result here for faster execution
)
all_datasets_gdf.drop(columns="geometry").sum().sort_values(ascending=False).head(10)

Finished operation in 0:01:48

Out[10]:

buildings|building                              818371
base|infrastructure|barrier|kerb                123967
base|infrastructure|transportation|crossing     109622
buildings|building|residential|garage           103650
base|infrastructure|transit|parking_space        87404
transportation|segment|road|footway              68119
buildings|building|residential|detached          68058
transportation|segment|road|residential          59596
base|land|tree|tree                              53675
transportation|segment|road|footway|sidewalk     51870
dtype: int64

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len(all_datasets_gdf.columns)
len(all_datasets_gdf.columns)

Out[11]:

Limiting hierarchy depth¶

If for some reason you want to only have higher level aggregation of the data, you can limit the hierarchy depth of the data.

By default full hierarchy is used to generate the columns.

Note

If you pass too high value, it will be automatically capped to the highest possible for a given theme/type pair.

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limited_depth_water_gdf = convert_geometry_to_wide_form_geodataframe(
    "base", "water", amsterdam, hierarchy_depth=1
)
limited_depth_water_gdf.drop(columns="geometry").sum()
limited_depth_water_gdf = convert_geometry_to_wide_form_geodataframe(
    "base", "water", amsterdam, hierarchy_depth=1
)
limited_depth_water_gdf.drop(columns="geometry").sum()

Finished operation in 0:00:13

Out[12]:

base|water|canal           161
base|water|human_made    46829
base|water|lake              6
base|water|ocean             9
base|water|physical        104
base|water|pond            352
base|water|reservoir        80
base|water|river           157
base|water|spring            2
base|water|stream          665
base|water|wastewater        0
base|water|water           700
dtype: int64

Using value of 0 will result in just a list of theme/type pairs.

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limited_depth_all_gdf = convert_geometry_to_wide_form_geodataframe_for_all_types(
    amsterdam, hierarchy_depth=0
)
limited_depth_all_gdf.drop(columns="geometry").sum()
limited_depth_all_gdf = convert_geometry_to_wide_form_geodataframe_for_all_types(
    amsterdam, hierarchy_depth=0
)
limited_depth_all_gdf.drop(columns="geometry").sum()

Finished operation in 0:00:45

Out[13]:

base|infrastructure        504296
base|land                   60141
base|land_cover              1714
base|land_use               43099
base|water                  49065
buildings|building        1091533
places|place               186017
transportation|segment     299210
dtype: int64

You can also pass a list if you are downloading data for multiple datasets at once. The list of values must be the same length as a list of theme_type_pairs.

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limited_depth_multiple_gdf = convert_geometry_to_wide_form_geodataframe_for_multiple_types(
    [("places", "place"), ("base", "land_cover"), ("base", "water")],
    amsterdam,
    hierarchy_depth=[1, None, 0],
)
limited_depth_multiple_gdf.drop(columns="geometry").sum()
limited_depth_multiple_gdf = convert_geometry_to_wide_form_geodataframe_for_multiple_types(
    [("places", "place"), ("base", "land_cover"), ("base", "water")],
    amsterdam,
    hierarchy_depth=[1, None, 0],
)
limited_depth_multiple_gdf.drop(columns="geometry").sum()

Finished operation in 0:00:11

Out[14]:

base|land_cover|barren                                  475
base|land_cover|crop                                     58
base|land_cover|forest                                  442
base|land_cover|grass                                     0
base|land_cover|mangrove                                  0
base|land_cover|moss                                      0
base|land_cover|shrub                                   505
base|land_cover|snow                                      0
base|land_cover|urban                                   105
base|land_cover|wetland                                 129
base|water                                            49065
places|place|accommodation                             2972
places|place|active_life                               7329
places|place|arts_and_entertainment                    7356
places|place|attractions_and_activities                9088
places|place|automotive                                5605
places|place|beauty_and_spa                           14496
places|place|business_to_business                     11382
places|place|eat_and_drink                            32622
places|place|education                                 9636
places|place|financial_service                         9988
places|place|health_and_medical                       25644
places|place|home_service                             11005
places|place|mass_media                                3238
places|place|pets                                      1342
places|place|private_establishments_and_corporates      522
places|place|professional_services                    43519
places|place|public_service_and_government            14427
places|place|real_estate                               8372
places|place|religious_organization                    6091
places|place|retail                                   45736
places|place|structure_and_geography                    666
places|place|travel                                    5484
dtype: int64

Places¶

Places data have different schema than other datasets and it's the only one with possible multiple categories at once: primary and optional multiple alternative.

This structure is preserved in the wide format and it's the only dataset where a single feature can have multiple True values in the columns.

OvertureMaestro utilizes the categories column with primary and alternate sub-fields to get feature categorization. The hierarchy depth of 6 is based on official taxonomy of the possible categories.

There are two pyarrow filters applied automatically when downloading the data for the wide format: confidence value >= 0.75 and categories cannot be empty.

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import pyarrow.compute as pc

category_not_null_filter = pc.invert(pc.field("categories").is_null())
minimal_confidence_filter = pc.field("confidence") >= pc.scalar(0.75)
combined_filter = category_not_null_filter & minimal_confidence_filter

original_places_data = convert_geometry_to_geodataframe(
    "places",
    "place",
    amsterdam,
    pyarrow_filter=combined_filter,
    columns_to_download=["id", "geometry", "categories", "confidence"],
)
original_places_data
import pyarrow.compute as pc

category_not_null_filter = pc.invert(pc.field("categories").is_null())
minimal_confidence_filter = pc.field("confidence") >= pc.scalar(0.75)
combined_filter = category_not_null_filter & minimal_confidence_filter

original_places_data = convert_geometry_to_geodataframe(
    "places",
    "place",
    amsterdam,
    pyarrow_filter=combined_filter,
    columns_to_download=["id", "geometry", "categories", "confidence"],
)
original_places_data

Finished operation in 0:00:08

Out[15]:

	geometry	categories	confidence
id
d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb	POINT (-74.25304 40.48667)	{'primary': 'lighthouse', 'alternate': ['landm...	0.939995
3a528566-cbfb-410f-ac23-07cf529fdf43	POINT (-74.23893 40.49926)	{'primary': 'italian_restaurant', 'alternate':...	0.950394
7290c346-53f6-494c-bd47-536260cd0356	POINT (-74.24486 40.49938)	{'primary': 'park', 'alternate': ['playground'...	0.995064
5023af81-eac2-415c-bea3-17dd86566f4e	POINT (-74.25182 40.499)	{'primary': 'landmark_and_historical_building'...	0.939995
dd5c25f1-e8d5-4379-a0d2-3eb116ac82e4	POINT (-74.25322 40.5032)	{'primary': 'history_museum', 'alternate': ['m...	0.995064
...	...	...	...
86f5dc3e-b1e4-44d7-9f1b-5fd07bd16aba	POINT (-73.76206 40.59256)	{'primary': 'amusement_park', 'alternate': ['b...	0.770000
e0b67409-7469-477d-9485-caae3b4b7a55	POINT (-73.75876 40.59279)	{'primary': 'landmark_and_historical_building'...	0.978538
c1fe438b-20d5-45a6-bbaa-7ff00f9e75eb	POINT (-73.75774 40.59271)	{'primary': 'garbage_collection_service', 'alt...	0.770000
bf1c92a6-5a6d-4fe5-8ba0-d913eac86edb	POINT (-73.75544 40.59341)	{'primary': 'public_relations', 'alternate': [...	0.894118
dde201d3-ed19-4d3d-af46-0e6a7098d17e	POINT (-73.75333 40.59327)	{'primary': 'social_service_organizations', 'a...	0.978538

186017 rows × 3 columns

In [16]:

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first_index = (
    # Find first object with at least one alternate category
    original_places_data[original_places_data.categories.str.get("alternate").str.len() > 1]
    .iloc[0]
    .name
)

first_index, original_places_data.loc[first_index].categories
first_index = (
    # Find first object with at least one alternate category
    original_places_data[original_places_data.categories.str.get("alternate").str.len() > 1]
    .iloc[0]
    .name
)

first_index, original_places_data.loc[first_index].categories

Out[16]:

('d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb',
 {'primary': 'lighthouse',
  'alternate': array(['landmark_and_historical_building', 'museum'], dtype=object)})

In [17]:

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wide_form_places_data = convert_geometry_to_wide_form_geodataframe("places", "place", amsterdam)
wide_form_places_data
wide_form_places_data = convert_geometry_to_wide_form_geodataframe("places", "place", amsterdam)
wide_form_places_data

Finished operation in 0:00:58

Out[17]:

	geometry	places\|place\|accommodation	places\|place\|accommodation\|bed_and_breakfast	places\|place\|accommodation\|cabin	places\|place\|accommodation\|campground	places\|place\|accommodation\|cottage	places\|place\|accommodation\|guest_house	places\|place\|accommodation\|health_retreats	places\|place\|accommodation\|holiday_rental_home	places\|place\|accommodation\|hostel	...	places\|place\|travel\|transportation\|transport_interchange	places\|place\|travel\|transportation\|water_taxi	places\|place\|travel\|travel_services	places\|place\|travel\|travel_services\|luggage_storage	places\|place\|travel\|travel_services\|passport_and_visa_services	places\|place\|travel\|travel_services\|passport_and_visa_services\|visa_agent	places\|place\|travel\|travel_services\|travel_agents	places\|place\|travel\|travel_services\|travel_agents\|sightseeing_tour_agency	places\|place\|travel\|travel_services\|visitor_center	places\|place\|travel\|vacation_rental_agents
id
d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb	POINT (-74.25304 40.48667)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3a528566-cbfb-410f-ac23-07cf529fdf43	POINT (-74.23893 40.49926)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7290c346-53f6-494c-bd47-536260cd0356	POINT (-74.24486 40.49938)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5023af81-eac2-415c-bea3-17dd86566f4e	POINT (-74.25182 40.499)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
dd5c25f1-e8d5-4379-a0d2-3eb116ac82e4	POINT (-74.25322 40.5032)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
86f5dc3e-b1e4-44d7-9f1b-5fd07bd16aba	POINT (-73.76206 40.59256)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
e0b67409-7469-477d-9485-caae3b4b7a55	POINT (-73.75876 40.59279)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
c1fe438b-20d5-45a6-bbaa-7ff00f9e75eb	POINT (-73.75774 40.59271)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
bf1c92a6-5a6d-4fe5-8ba0-d913eac86edb	POINT (-73.75544 40.59341)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
dde201d3-ed19-4d3d-af46-0e6a7098d17e	POINT (-73.75333 40.59327)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

186017 rows × 2117 columns

As you can see, only those features existing in the categories column are True and the rest is False.

In [18]:

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wide_form_places_data.loc[first_index].drop("geometry").sort_values(ascending=False)
wide_form_places_data.loc[first_index].drop("geometry").sort_values(ascending=False)

Out[18]:

places|place|attractions_and_activities|landmark_and_historical_building     True
places|place|attractions_and_activities|museum                               True
places|place|attractions_and_activities|lighthouse                           True
places|place|pets|pet_services|farrier_services                             False
places|place|professional_services|emergency_service                        False
                                                                            ...  
places|place|eat_and_drink|bar|milkshake_bar                                False
places|place|eat_and_drink|bar|milk_bar                                     False
places|place|eat_and_drink|bar|lounge                                       False
places|place|eat_and_drink|bar|kombucha                                     False
places|place|travel|vacation_rental_agents                                  False
Name: d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb, Length: 2116, dtype: object

You can use places_use_primary_category_only to use only single category per feature without altenatives.

In [19]:

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primary_only_wide_form_places_data = convert_geometry_to_wide_form_geodataframe(
    "places",
    "place",
    amsterdam,
    places_use_primary_category_only=True,
)
primary_only_wide_form_places_data.loc[first_index].drop("geometry").sort_values(ascending=False)
primary_only_wide_form_places_data = convert_geometry_to_wide_form_geodataframe(
    "places",
    "place",
    amsterdam,
    places_use_primary_category_only=True,
)
primary_only_wide_form_places_data.loc[first_index].drop("geometry").sort_values(ascending=False)

Finished operation in 0:00:12

Out[19]:

places|place|attractions_and_activities|lighthouse                                                 True
places|place|professional_services|construction_services|stone_and_masonry|masonry_contractors    False
places|place|professional_services|electrical_consultant                                          False
places|place|professional_services|elder_care_planning                                            False
places|place|professional_services|editorial_services                                             False
                                                                                                  ...  
places|place|eat_and_drink|bar|lounge                                                             False
places|place|eat_and_drink|bar|kombucha                                                           False
places|place|eat_and_drink|bar|irish_pub                                                          False
places|place|eat_and_drink|bar|hotel_bar                                                          False
places|place|travel|vacation_rental_agents                                                        False
Name: d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb, Length: 2116, dtype: object

Below you can see the difference in the counts of True values across all columns.

In [20]:

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wide_form_places_data.drop(columns="geometry").sum().sort_values(ascending=False)
wide_form_places_data.drop(columns="geometry").sum().sort_values(ascending=False)

Out[20]:

places|place|professional_services                            20659
places|place|eat_and_drink|restaurant                         17973
places|place|health_and_medical                               14241
places|place|beauty_and_spa|beauty_salon                       9406
places|place|beauty_and_spa                                    9027
                                                              ...  
places|place|eat_and_drink|restaurant|fish_restaurant             0
places|place|real_estate|kitchen_incubator                        0
places|place|real_estate|housing_cooperative                      0
places|place|eat_and_drink|restaurant|guamanian_restaurant        0
places|place|retail|food|coffee_and_tea_supplies                  0
Length: 2116, dtype: int64

In [21]:

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primary_only_wide_form_places_data.drop(columns="geometry").sum().sort_values(ascending=False)
primary_only_wide_form_places_data.drop(columns="geometry").sum().sort_values(ascending=False)

Out[21]:

places|place|professional_services                                   4690
places|place|religious_organization|church_cathedral                 3080
places|place|beauty_and_spa|beauty_salon                             3000
places|place|public_service_and_government|community_services        2816
places|place|health_and_medical|dentist                              2285
                                                                     ... 
places|place|active_life|sports_and_recreation_venue|futsal_field       0
places|place|eat_and_drink|bar|milkshake_bar                            0
places|place|professional_services|product_design                       0
places|place|eat_and_drink|bar|milk_bar                                 0
places|place|arts_and_entertainment|stadium_arena|tennis_stadium        0
Length: 2116, dtype: int64

You can also change the minimal confidence value with places_minimal_confidence parameter.

In [22]:

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convert_geometry_to_wide_form_geodataframe(
    "places", "place", amsterdam, places_minimal_confidence=0.95
)
convert_geometry_to_wide_form_geodataframe(
    "places", "place", amsterdam, places_minimal_confidence=0.95
)

Finished operation in 0:00:59

Out[22]:

	geometry	places\|place\|accommodation	places\|place\|accommodation\|bed_and_breakfast	places\|place\|accommodation\|cabin	places\|place\|accommodation\|campground	places\|place\|accommodation\|cottage	places\|place\|accommodation\|guest_house	places\|place\|accommodation\|health_retreats	places\|place\|accommodation\|holiday_rental_home	places\|place\|accommodation\|hostel	...	places\|place\|travel\|transportation\|transport_interchange	places\|place\|travel\|transportation\|water_taxi	places\|place\|travel\|travel_services	places\|place\|travel\|travel_services\|luggage_storage	places\|place\|travel\|travel_services\|passport_and_visa_services	places\|place\|travel\|travel_services\|passport_and_visa_services\|visa_agent	places\|place\|travel\|travel_services\|travel_agents	places\|place\|travel\|travel_services\|travel_agents\|sightseeing_tour_agency	places\|place\|travel\|travel_services\|visitor_center	places\|place\|travel\|vacation_rental_agents
id
8f12e63e-1c93-4c60-a7bc-555d801104c3	POINT (-74.25125 40.50058)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
dd5c25f1-e8d5-4379-a0d2-3eb116ac82e4	POINT (-74.25322 40.5032)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
e011bf43-1757-4b53-af9c-13ca7cb715d0	POINT (-74.25416 40.50543)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
f541d0aa-d4bf-42a2-90e2-6bde6f3e3271	POINT (-74.25427 40.50549)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7290c346-53f6-494c-bd47-536260cd0356	POINT (-74.24486 40.49938)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
a76f8690-d0ec-481d-a688-070ea8606c66	POINT (-73.74031 40.59847)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7f421a62-b7d1-467d-813f-1c6bc9b56d28	POINT (-73.73994 40.59916)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
a14f3cd0-961c-4c19-b4dd-ca802ebae349	POINT (-73.74217 40.59955)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
f44ebbb1-bff7-41a3-b5b1-06a67944602a	POINT (-73.74159 40.59542)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
60fd5f49-19b3-4c7c-a825-5026a0b55651	POINT (-73.74218 40.59545)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

85743 rows × 2117 columns

Full hierarchy of the places dataset is derived from the official taxonomy available here.

You can limit it to get less columns, with grouped categories.

In [23]:

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convert_geometry_to_wide_form_geodataframe("places", "place", amsterdam, hierarchy_depth=1)
convert_geometry_to_wide_form_geodataframe("places", "place", amsterdam, hierarchy_depth=1)

Finished operation in 0:00:07

Out[23]:

	geometry	places\|place\|accommodation	places\|place\|active_life	places\|place\|arts_and_entertainment	places\|place\|attractions_and_activities	places\|place\|automotive	places\|place\|beauty_and_spa	places\|place\|business_to_business	places\|place\|eat_and_drink	places\|place\|education	...	places\|place\|mass_media	places\|place\|pets	places\|place\|private_establishments_and_corporates	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
id
d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb	POINT (-74.25304 40.48667)	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3a528566-cbfb-410f-ac23-07cf529fdf43	POINT (-74.23893 40.49926)	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
7290c346-53f6-494c-bd47-536260cd0356	POINT (-74.24486 40.49938)	False	True	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5023af81-eac2-415c-bea3-17dd86566f4e	POINT (-74.25182 40.499)	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
dd5c25f1-e8d5-4379-a0d2-3eb116ac82e4	POINT (-74.25322 40.5032)	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
86f5dc3e-b1e4-44d7-9f1b-5fd07bd16aba	POINT (-73.76206 40.59256)	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	True	False	False
e0b67409-7469-477d-9485-caae3b4b7a55	POINT (-73.75876 40.59279)	False	False	False	True	False	False	False	False	False	...	False	False	False	False	False	True	False	False	False	False
c1fe438b-20d5-45a6-bbaa-7ff00f9e75eb	POINT (-73.75774 40.59271)	False	False	False	False	False	False	False	False	False	...	False	False	False	True	False	False	False	False	False	False
bf1c92a6-5a6d-4fe5-8ba0-d913eac86edb	POINT (-73.75544 40.59341)	False	False	False	False	False	False	False	False	False	...	False	False	False	True	False	False	False	False	False	False
dde201d3-ed19-4d3d-af46-0e6a7098d17e	POINT (-73.75333 40.59327)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	True	False	False	False	False	False

186017 rows × 23 columns

Pruning final list of columns¶

By default, OvertureMaestro includes all possible columns regardless of whether any features of a given category exist.

This is done to keep the overall schema consistent for different geographical regions and simplifying the feature engineering process.

However, there is a dedicated parameter include_all_possible_columns that can be set to False to keep only columns based on actually existing features.

In [24]:

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convert_geometry_to_wide_form_geodataframe(
    "base", "infrastructure", amsterdam, include_all_possible_columns=True  # default value
)
convert_geometry_to_wide_form_geodataframe(
    "base", "infrastructure", amsterdam, include_all_possible_columns=True  # default value
)

Finished operation in 0:00:06

Out[24]:

	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	...	base\|infrastructure\|utility\|utility_pole	base\|infrastructure\|utility\|water_tower	base\|infrastructure\|waste_management\|recycling	base\|infrastructure\|waste_management\|waste_basket	base\|infrastructure\|waste_management\|waste_disposal	base\|infrastructure\|water\|breakwater	base\|infrastructure\|water\|dam	base\|infrastructure\|water\|drinking_water	base\|infrastructure\|water\|fountain	base\|infrastructure\|water\|weir
id
25627f2a-664d-35ca-a7c5-90c5fea051e7	POINT (-74.25192 40.50054)	False	False	False	False	False	False	False	False	False	...	False	False	False	True	False	False	False	False	False	False
957f3e16-47bd-3a44-87cb-fd90531b2e2a	LINESTRING (-74.25409 40.5025, -74.25407 40.50...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
8274f299-be66-3bf2-aea0-ad4069582a12	LINESTRING (-74.25379 40.50246, -74.25378 40.5...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
aa1a50f1-2a37-34f0-bc72-62b32cf116f5	LINESTRING (-74.25388 40.50239, -74.25386 40.5...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
517cf5aa-6097-3195-9a0c-5bdaa3fafc5d	LINESTRING (-74.2538 40.50239, -74.25382 40.50...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
c2f6482b-2976-3841-98bc-30953ba98092	POLYGON ((-73.93049 40.55266, -73.93047 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3bb1a4c5-8e49-357b-9831-42694aecc364	POLYGON ((-73.93052 40.55265, -73.9305 40.5526...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
a3794d61-4b03-353e-89a6-e84ca36de853	POLYGON ((-73.93043 40.55267, -73.93041 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7d7fc443-8f5f-3efc-9d96-f35607c87c78	POLYGON ((-73.9304 40.55268, -73.93038 40.5526...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
089a3ed4-6829-3838-9b94-18583e45542a	POLYGON ((-73.93037 40.55269, -73.93035 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

504296 rows × 161 columns

In [25]:

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convert_geometry_to_wide_form_geodataframe(
    "base", "infrastructure", amsterdam, include_all_possible_columns=False
)
convert_geometry_to_wide_form_geodataframe(
    "base", "infrastructure", amsterdam, include_all_possible_columns=False
)

Finished operation in 0:00:06

Out[25]:

	geometry	base\|infrastructure\|aerialway\|aerialway_station	base\|infrastructure\|aerialway\|cable_car	base\|infrastructure\|aerialway\|pylon	base\|infrastructure\|airport\|airport_gate	base\|infrastructure\|airport\|apron	base\|infrastructure\|airport\|helipad	base\|infrastructure\|airport\|heliport	base\|infrastructure\|airport\|international_airport	base\|infrastructure\|airport\|runway	...	base\|infrastructure\|utility\|utility_pole	base\|infrastructure\|utility\|water_tower	base\|infrastructure\|waste_management\|recycling	base\|infrastructure\|waste_management\|waste_basket	base\|infrastructure\|waste_management\|waste_disposal	base\|infrastructure\|water\|breakwater	base\|infrastructure\|water\|dam	base\|infrastructure\|water\|drinking_water	base\|infrastructure\|water\|fountain	base\|infrastructure\|water\|weir
id
25627f2a-664d-35ca-a7c5-90c5fea051e7	POINT (-74.25192 40.50054)	False	False	False	False	False	False	False	False	False	...	False	False	False	True	False	False	False	False	False	False
957f3e16-47bd-3a44-87cb-fd90531b2e2a	LINESTRING (-74.25409 40.5025, -74.25407 40.50...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
8274f299-be66-3bf2-aea0-ad4069582a12	LINESTRING (-74.25379 40.50246, -74.25378 40.5...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
aa1a50f1-2a37-34f0-bc72-62b32cf116f5	LINESTRING (-74.25388 40.50239, -74.25386 40.5...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
517cf5aa-6097-3195-9a0c-5bdaa3fafc5d	LINESTRING (-74.2538 40.50239, -74.25382 40.50...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
c2f6482b-2976-3841-98bc-30953ba98092	POLYGON ((-73.93049 40.55266, -73.93047 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3bb1a4c5-8e49-357b-9831-42694aecc364	POLYGON ((-73.93052 40.55265, -73.9305 40.5526...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
a3794d61-4b03-353e-89a6-e84ca36de853	POLYGON ((-73.93043 40.55267, -73.93041 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
7d7fc443-8f5f-3efc-9d96-f35607c87c78	POLYGON ((-73.9304 40.55268, -73.93038 40.5526...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
089a3ed4-6829-3838-9b94-18583e45542a	POLYGON ((-73.93037 40.55269, -73.93035 40.552...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

504296 rows × 118 columns

Getting a full list of possible column names¶

You can also preview the final list of columns before downloading the data using get_all_possible_column_names function.

You can specify the release, theme and type, as well as hierarchy_depth.

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from overturemaestro.advanced_functions.wide_form import get_all_possible_column_names

get_all_possible_column_names(theme="base", type="water")
from overturemaestro.advanced_functions.wide_form import get_all_possible_column_names

get_all_possible_column_names(theme="base", type="water")

Out[26]:

['base|water|canal|canal',
 'base|water|canal|ditch',
 'base|water|canal|drain',
 'base|water|canal|moat',
 'base|water|human_made|fish_pass',
 'base|water|human_made|reflecting_pool',
 'base|water|human_made|salt_pond',
 'base|water|human_made|swimming_pool',
 'base|water|lake|lagoon',
 'base|water|lake|lake',
 'base|water|lake|oxbow',
 'base|water|ocean|ocean',
 'base|water|physical|bay',
 'base|water|physical|cape',
 'base|water|physical|ocean',
 'base|water|physical|sea',
 'base|water|physical|shoal',
 'base|water|physical|strait',
 'base|water|physical|waterfall',
 'base|water|pond|fishpond',
 'base|water|pond|pond',
 'base|water|reservoir|basin',
 'base|water|reservoir|reservoir',
 'base|water|reservoir|water_storage',
 'base|water|river|river',
 'base|water|spring|blowhole',
 'base|water|spring|geyser',
 'base|water|spring|hot_spring',
 'base|water|spring|spring',
 'base|water|stream|stream',
 'base|water|wastewater|sewage',
 'base|water|water|dock',
 'base|water|water|fairway',
 'base|water|water|tidal_channel',
 'base|water|water|wastewater',
 'base|water|water|water']

With all parameters empty, function will return a full list of all possible columns with maximal depth.

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columns = get_all_possible_column_names()
len(columns)
columns = get_all_possible_column_names()
len(columns)

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columns[:10]
columns[:10]

Out[28]:

['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',
 'base|infrastructure|aerialway|platter']

You can also specify different hierarchy_depth values.

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get_all_possible_column_names(theme="buildings", type="building", hierarchy_depth=1)
get_all_possible_column_names(theme="buildings", type="building", hierarchy_depth=1)

Out[29]:

['buildings|building',
 'buildings|building|agricultural',
 'buildings|building|civic',
 'buildings|building|commercial',
 'buildings|building|education',
 'buildings|building|entertainment',
 'buildings|building|industrial',
 'buildings|building|medical',
 'buildings|building|military',
 'buildings|building|outbuilding',
 'buildings|building|religious',
 'buildings|building|residential',
 'buildings|building|service',
 'buildings|building|transportation']