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 0:00:07

Finished operation in 0:00:09

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original_data
original_data

Out[4]:

	names	subtype	class	sources	source_tags	level	wikidata	is_intermittent	is_salt	geometry	version	bbox
id
f3ff6909-6205-369d-9da8-f9aac25e43db	None	ocean	ocean	[{'property': '', 'dataset': 'OpenStreetMap', ...	None	NaN	None	None	True	POLYGON ((-72.99934 40.68825, -72.99934 40.747...	1	{'xmin': -74.00066375732422, 'xmax': -72.99933...
7198db90-6b71-3792-8947-b581d6c5b5c4	None	human_made	swimming_pool	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(access, private), (leisure, swimming_pool)]	NaN	None	None	None	POLYGON ((-73.72785 40.66063, -73.72784 40.660...	0	{'xmin': -73.7278823852539, 'xmax': -73.727821...
d3a3c77d-8078-3d98-b7b7-323323bacf44	{'primary': 'Hook Creek', 'common': None, 'rul...	canal	drain	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(tunnel, culvert), (waterway, drain)]	-1.0	None	None	None	LINESTRING (-73.72846 40.66632, -73.72851 40.6...	0	{'xmin': -73.728515625, 'xmax': -73.7254257202...
722d3468-4f50-37a7-b809-1f0c1e7814ab	None	human_made	swimming_pool	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(access, private), (leisure, swimming_pool)]	NaN	None	None	None	POLYGON ((-73.72678 40.65868, -73.72682 40.658...	0	{'xmin': -73.7268295288086, 'xmax': -73.726760...
61f8df08-22b7-309b-a39d-1e27ffdea4c1	None	human_made	swimming_pool	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(access, private), (leisure, swimming_pool)]	NaN	None	None	None	POLYGON ((-73.72903 40.66007, -73.72902 40.660...	0	{'xmin': -73.72909545898438, 'xmax': -73.72901...
...	...	...	...	...	...	...	...	...	...	...	...	...
1b55cc6a-4875-35bb-973c-7f78107e9349	None	human_made	swimming_pool	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(access, private), (leisure, swimming_pool), ...	NaN	None	None	None	POLYGON ((-73.81194 40.88809, -73.81196 40.888...	0	{'xmin': -73.81196594238281, 'xmax': -73.81179...
f6710717-836e-3537-8dc4-b1d301691427	None	water	water	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(area, yes), (intermittent, no), (natural, wa...	NaN	None	False	None	POLYGON ((-73.91054 40.91505, -73.91045 40.915...	1	{'xmin': -73.94308471679688, 'xmax': -73.88537...
79dced11-730d-34b5-bb9c-803ddfeb40f0	{'primary': 'Hudson River', 'common': [('es', ...	river	river	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(boat, yes), (canoe, yes), (canoe:description...	NaN	None	False	None	LINESTRING (-73.88916 41.04345, -73.89008 41.0...	0	{'xmin': -73.93392944335938, 'xmax': -73.88914...
04b2eb53-8a9d-3c89-8c42-2afe32559c7d	None	ocean	ocean	[{'property': '', 'dataset': 'OpenStreetMap', ...	None	NaN	None	None	True	POLYGON ((-73.64509 41.0005, -73.64611 41.0005...	1	{'xmin': -74.00066375732422, 'xmax': -72.99933...
6b67cfae-059c-387b-9391-14e8d4946a7b	{'primary': 'Long Island Sound', 'common': [('...	physical	bay	[{'property': '', 'dataset': 'OpenStreetMap', ...	[(natural, bay), (gnis:feature_id, 977427), (w...	NaN	Q867460	None	None	POLYGON ((-72.23354 41.16044, -72.23316 41.160...	2	{'xmin': -73.80953979492188, 'xmax': -71.85736...

49260 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
f3ff6909-6205-369d-9da8-f9aac25e43db	POLYGON ((-72.99934 40.68825, -72.99934 40.747...	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
f6710717-836e-3537-8dc4-b1d301691427	POLYGON ((-73.91054 40.91505, -73.91045 40.915...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
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
04b2eb53-8a9d-3c89-8c42-2afe32559c7d	POLYGON ((-73.64509 41.0005, -73.64611 41.0005...	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 ((-72.23354 41.16044, -72.23316 41.160...	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

49260 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      46846
base|water|stream|stream                   677
base|water|water|water                     500
base|water|pond|pond                       353
base|water|river|river                     210
base|water|water|wastewater                209
base|water|canal|ditch                     109
base|water|reservoir|basin                  74
base|water|physical|bay                     54
base|water|water|tidal_channel              53
base|water|canal|drain                      39
base|water|water|fairway                    33
base|water|physical|cape                    30
base|water|canal|canal                      17
base|water|physical|waterfall               13
base|water|ocean|ocean                      10
base|water|physical|shoal                    9
base|water|lake|lake                         6
base|water|reservoir|reservoir               6
base|water|physical|strait                   5
base|water|human_made|reflecting_pool        4
base|water|spring|spring                     2
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 0:00:07

Out[8]:

base|water|human_made|swimming_pool    46846
base|water|stream|stream                 677
base|land_cover|shrub                    505
base|water|water|water                   500
base|land_cover|barren                   475
base|land_cover|forest                   442
base|water|pond|pond                     353
base|water|river|river                   210
base|water|water|wastewater              209
base|land_cover|wetland                  129
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 0:01:47

Out[10]:

buildings|building                              802916
base|infrastructure|barrier|kerb                128766
base|infrastructure|transportation|crossing     119176
buildings|building|residential|garage           104633
base|infrastructure|transit|parking_space        91669
buildings|building|residential|detached          76228
transportation|segment|road|footway              75404
transportation|segment|road|residential          59594
base|land|tree|tree                              56081
transportation|segment|road|footway|sidewalk     53024
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 0:00:11

Out[12]:

base|water|canal           165
base|water|human_made    46851
base|water|lake              6
base|water|ocean            10
base|water|physical        111
base|water|pond            353
base|water|reservoir        80
base|water|river           210
base|water|spring            2
base|water|stream          677
base|water|wastewater        0
base|water|water           795
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 0:00:43

Out[13]:

base|infrastructure        529817
base|land                   62758
base|land_cover              1714
base|land_use               44652
base|water                  49260
buildings|building        1091617
places|place               205915
transportation|segment     309737
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 0:00:08

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                                            49260
places|place|accommodation                             3316
places|place|active_life                               8100
places|place|arts_and_entertainment                    6226
places|place|attractions_and_activities                9351
places|place|automotive                                5561
places|place|beauty_and_spa                           13633
places|place|business_to_business                      8536
places|place|eat_and_drink                            42480
places|place|education                                10132
places|place|financial_service                         9706
places|place|health_and_medical                       25909
places|place|home_service                              7772
places|place|mass_media                                1930
places|place|pets                                      1166
places|place|private_establishments_and_corporates     3076
places|place|professional_services                    24072
places|place|public_service_and_government             9907
places|place|real_estate                               5458
places|place|religious_organization                    6778
places|place|retail                                   43700
places|place|structure_and_geography                    776
places|place|travel                                    6119
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 0:00:10

Out[15]:

	geometry	categories	confidence
id
d1d9bdd1-c030-40b1-9ae9-84935d0fd1fb	POINT (-74.25304 40.48667)	{'primary': 'lighthouse', 'alternate': ['landm...	0.770604
3a528566-cbfb-410f-ac23-07cf529fdf43	POINT (-74.23893 40.49926)	{'primary': 'seafood_restaurant', 'alternate':...	0.905618
20f52a3a-c772-4e00-aa88-b59738b596e4	POINT (-74.24071 40.49981)	{'primary': 'department_store', 'alternate': N...	0.790186
25526275-c5a8-46ba-bc83-f071b8551d19	POINT (-74.24232 40.49959)	{'primary': 'garbage_collection_service', 'alt...	0.770000
7290c346-53f6-494c-bd47-536260cd0356	POINT (-74.24486 40.49938)	{'primary': 'playground', 'alternate': ['park']}	0.946945
...	...	...	...
9c6ffc77-830a-484b-9779-1a7351e16f71	POINT (-73.75866 40.59265)	{'primary': 'beach', 'alternate': ['park', 'sp...	0.805213
e0b67409-7469-477d-9485-caae3b4b7a55	POINT (-73.75863 40.59271)	{'primary': 'landmark_and_historical_building'...	0.962183
c1fe438b-20d5-45a6-bbaa-7ff00f9e75eb	POINT (-73.75774 40.59271)	{'primary': 'garbage_collection_service', 'alt...	0.770000
e995402e-fdc5-4f53-820c-ef1b70b22453	POINT (-73.75387 40.59336)	{'primary': 'hospital', 'alternate': ['health_...	0.976375
a45bac2c-e6cb-4f4f-bfce-aff65ad931d1	POINT (-73.75333 40.59327)	{'primary': 'senior_citizen_services', 'altern...	0.951227

205915 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', 'monument'], 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 0:01:02

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
20f52a3a-c772-4e00-aa88-b59738b596e4	POINT (-74.24071 40.49981)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
25526275-c5a8-46ba-bc83-f071b8551d19	POINT (-74.24232 40.49959)	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9c6ffc77-830a-484b-9779-1a7351e16f71	POINT (-73.75866 40.59265)	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 (-73.75863 40.59271)	True	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
e995402e-fdc5-4f53-820c-ef1b70b22453	POINT (-73.75387 40.59336)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
a45bac2c-e6cb-4f4f-bfce-aff65ad931d1	POINT (-73.75333 40.59327)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

205915 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|monument                             True
places|place|attractions_and_activities|lighthouse                           True
places|place|pets|pet_services|emergency_pet_hospital                       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|eat_and_drink|restaurant                                                    16863
places|place|health_and_medical                                                          12485
places|place|health_and_medical|doctor                                                   10826
places|place|retail                                                                       7904
places|place|beauty_and_spa|beauty_salon                                                  7079
                                                                                         ...  
places|place|retail|food|bakery|flatbread                                                    0
places|place|active_life|sports_and_recreation_venue|diving_center                           0
places|place|active_life|sports_and_recreation_venue|diving_center|free_diving_center        0
places|place|retail|food|back_shop                                                           0
places|place|attractions_and_activities|stargazing_area                                      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|religious_organization|church_cathedral                                                           3679
places|place|health_and_medical|doctor                                                                         3616
places|place|eat_and_drink|restaurant|pizza_restaurant                                                         2895
places|place|beauty_and_spa|hair_salon                                                                         2780
places|place|private_establishments_and_corporates|corporate_office                                            2578
                                                                                                               ... 
places|place|health_and_medical|traditional_chinese_medicine                                                      0
places|place|business_to_business|business_manufacturing_and_supply|b2b_rubber_and_plastics|plastic_company       0
places|place|eat_and_drink|restaurant|meatball_restaurant                                                         0
places|place|public_service_and_government|railway_service                                                        0
places|place|professional_services|metal_detector_services                                                        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 0:01:04

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
799b7a13-60e6-48eb-9397-e770ddfe23af	POINT (-74.25421 40.50553)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
2bab82d8-c3f4-45e3-8c39-98b07c064d4e	POINT (-74.24495 40.50832)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
547e2bed-376a-438c-a34f-9440ba4de7a7	POINT (-74.24448 40.50991)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18f9d264-a2bb-4145-b4ec-4a770d35e978	POINT (-73.74513 40.59795)	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

66505 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
20f52a3a-c772-4e00-aa88-b59738b596e4	POINT (-74.24071 40.49981)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	True	False	False
25526275-c5a8-46ba-bc83-f071b8551d19	POINT (-74.24232 40.49959)	False	False	False	False	False	False	False	False	False	...	False	False	False	True	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9c6ffc77-830a-484b-9779-1a7351e16f71	POINT (-73.75866 40.59265)	False	True	False	True	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
e0b67409-7469-477d-9485-caae3b4b7a55	POINT (-73.75863 40.59271)	True	False	False	True	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	True	False	False	False	False	False	False
e995402e-fdc5-4f53-820c-ef1b70b22453	POINT (-73.75387 40.59336)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	True	False	False	False
a45bac2c-e6cb-4f4f-bfce-aff65ad931d1	POINT (-73.75333 40.59327)	False	False	False	False	False	False	False	False	False	...	False	False	False	False	True	False	False	False	False	False

205915 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 0:00:04

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

529817 rows × 164 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 0:00:04

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

529817 rows × 121 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.

In [26]:

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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']