OSM PBF Loader¶

OSMPbfLoader can really quickly parse full OSM extract in the form of *.osm.pbf file.

It can download and parse a lot of features much faster than the OSMOnlineLoader, but it's much more useful when a lot of different features are required at once (like when using predefined filters).

When only a single or few features are needed, OSMOnlineLoader might be a better choice, since OSMPbfLoader will use a full extract of all features in a given region and will have to iterate over all of them.

Run this notebook in Google Colab:

Remember to install the srai library before running the notebook:

%pip install srai[all]

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import geopandas as gpd
from shapely.geometry import Point, box

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.geometry import buffer_geometry
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.loaders.osm_loaders.filters import GEOFABRIK_LAYERS, HEX2VEC_FILTER
from srai.loaders.osm_loaders.filters.popular import get_popular_tags
from srai.regionalizers import geocode_to_region_gdf
import geopandas as gpd
from shapely.geometry import Point, box

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.geometry import buffer_geometry
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.loaders.osm_loaders.filters import GEOFABRIK_LAYERS, HEX2VEC_FILTER
from srai.loaders.osm_loaders.filters.popular import get_popular_tags
from srai.regionalizers import geocode_to_region_gdf

Using OSMPbfLoader to download data for a specific area¶

Download all features from `HEX2VEC_FILTER` in Warsaw, Poland¶

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loader = OSMPbfLoader()
warsaw_gdf = geocode_to_region_gdf("Warsaw, Poland")
warsaw_features_gdf = loader.load(warsaw_gdf, HEX2VEC_FILTER)
warsaw_features_gdf
loader = OSMPbfLoader()
warsaw_gdf = geocode_to_region_gdf("Warsaw, Poland")
warsaw_features_gdf = loader.load(warsaw_gdf, HEX2VEC_FILTER)
warsaw_features_gdf

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pyogrio/geopandas.py:662: UserWarning: 'crs' was not provided.  The output dataset will not have projection information defined and may not be usable in other systems.
  write(

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/geopandas/array.py:1638: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 (the single non-null crs provided).
  return GeometryArray(data, crs=_get_common_crs(to_concat))

Finished operation in 0:00:53

Out[2]:

	geometry	aeroway	amenity	building	healthcare	historic	landuse	leisure	military	natural	office	shop	sport	tourism	water	waterway
feature_id
node/1842824313	POINT (20.93532 52.28486)	None	post_office	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1843248304	POINT (21.01587 52.23087)	None	bicycle_parking	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1843254633	POINT (21.03134 52.23971)	None	bicycle_parking	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1846013889	POINT (21.08482 52.2216)	None	nightclub	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1846013914	POINT (21.01242 52.2206)	None	None	None	None	None	None	None	None	None	None	tailor	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/967888042	POLYGON ((21.02142 52.19196, 21.02151 52.19197...	None	None	semidetached_house	None	None	None	None	None	None	None	None	None	None	None	None
way/967888043	POLYGON ((21.02407 52.19257, 21.02409 52.19228...	None	None	apartments	None	yes	None	None	None	None	None	None	None	None	None	None
way/967888044	POLYGON ((21.02093 52.19337, 21.02095 52.19331...	None	None	apartments	None	None	None	None	None	None	None	None	None	None	None	None
way/967888045	POLYGON ((21.02307 52.19205, 21.02307 52.19205...	None	None	detached	None	yes	None	None	None	None	None	None	None	None	None	None
way/967888046	POLYGON ((21.02144 52.19173, 21.02148 52.19164...	None	None	house	None	None	None	None	None	None	None	None	None	None	None	None

332239 rows × 16 columns

Plot features¶

Inspired by prettymaps

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clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.union_all())
clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.union_all())

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ax = warsaw_gdf.plot(color="lavender", figsize=(16, 16))

# plot water
clipped_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="deepskyblue"
)

# plot greenery
clipped_features_gdf[
    clipped_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="mediumseagreen")

# plot buildings
clipped_features_gdf.dropna(subset=["building"], how="all").plot(
    ax=ax, color="dimgray", markersize=0.1
)

xmin, ymin, xmax, ymax = warsaw_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = warsaw_gdf.plot(color="lavender", figsize=(16, 16))

# plot water
clipped_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="deepskyblue"
)

# plot greenery
clipped_features_gdf[
    clipped_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="mediumseagreen")

# plot buildings
clipped_features_gdf.dropna(subset=["building"], how="all").plot(
    ax=ax, color="dimgray", markersize=0.1
)

xmin, ymin, xmax, ymax = warsaw_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

No description has been provided for this image

Download all features from popular tags based on OSMTagInfo in Vienna, Austria¶

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popular_tags = get_popular_tags(in_wiki_only=True)

num_keys = len(popular_tags)
f"Unique keys: {num_keys}."
popular_tags = get_popular_tags(in_wiki_only=True)

num_keys = len(popular_tags)
f"Unique keys: {num_keys}."

Out[5]:

'Unique keys: 360.'

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{k: popular_tags[k] for k in list(popular_tags)[:10]}
{k: popular_tags[k] for k in list(popular_tags)[:10]}

Out[6]:

{'4wd_only': ['yes'],
 'LandPro08:reviewed': ['no'],
 'abandoned': ['yes'],
 'abandoned:railway': ['rail'],
 'abutters': ['residential'],
 'access': ['agricultural',
  'customers',
  'delivery',
  'designated',
  'destination',
  'forestry',
  'no',
  'permissive',
  'permit',
  'private',
  'unknown',
  'yes'],
 'addr:TW:dataset': ['137998'],
 'addr:country': ['CZ', 'DE', 'RU', 'US'],
 'addr:state': ['AZ',
  'CA',
  'CO',
  'CT',
  'FL',
  'IN',
  'KY',
  'MD',
  'ME',
  'NC',
  'NJ',
  'NY',
  'PA',
  'TX'],
 'admin_level': ['10', '11', '2', '4', '5', '6', '7', '8', '9']}

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vienna_center_circle = buffer_geometry(Point(16.37009, 48.20931), meters=1000)
vienna_center_circle_gdf = gpd.GeoDataFrame(
    geometry=[vienna_center_circle],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["Vienna"], name=REGIONS_INDEX),
)
vienna_center_circle = buffer_geometry(Point(16.37009, 48.20931), meters=1000)
vienna_center_circle_gdf = gpd.GeoDataFrame(
    geometry=[vienna_center_circle],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["Vienna"], name=REGIONS_INDEX),
)

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loader = OSMPbfLoader()
vienna_features_gdf = loader.load(vienna_center_circle_gdf, popular_tags)
vienna_features_gdf
loader = OSMPbfLoader()
vienna_features_gdf = loader.load(vienna_center_circle_gdf, popular_tags)
vienna_features_gdf

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/geopandas/array.py:1638: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 (the single non-null crs provided).
  return GeometryArray(data, crs=_get_common_crs(to_concat))

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/quackosm/pb
f_file_reader.py:2614: UserWarning: Select clause contains more than 100 columns
(found 360 columns). Query might fail with insufficient memory resources. 
Consider applying more restrictive OsmTagsFilter for parsing.
  warnings.warn(

Finished operation in 0:00:30

Out[8]:

	geometry	abandoned	access	admin_level	advertising	amenity	area:highway	artwork_type	atm	barrier	...	tunnel	type	usage	vehicle	vending	waste	water	water_source	waterway	wheelchair
feature_id
node/33182886	POINT (16.36631 48.20782)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/33182965	POINT (16.37033 48.20359)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/33344361	POINT (16.36484 48.2159)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/33344362	POINT (16.36413 48.21558)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/34591548	POINT (16.36967 48.20235)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/994956644	POLYGON ((16.37064 48.21644, 16.37075 48.21649...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/996302612	LINESTRING (16.37587 48.21453, 16.3758 48.21462)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/997555943	LINESTRING (16.36499 48.21306, 16.36499 48.21292)	None	None	None	None	None	None	None	None	None	...	building_passage	None	None	None	None	None	None	None	None	None
way/997555944	LINESTRING (16.36499 48.2131, 16.36499 48.21306)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1000492276	LINESTRING (16.36618 48.20044, 16.36623 48.200...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

22730 rows × 191 columns

Plot features¶

Uses default preset colours from prettymaps

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clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)
clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)

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ax = vienna_center_circle_gdf.plot(color="#F2F4CB", figsize=(16, 16))

# plot water
clipped_vienna_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="#a8e1e6"
)

# plot streets
clipped_vienna_features_gdf.dropna(subset=["highway"], how="all").plot(
    ax=ax, color="#475657", markersize=0.1
)

# plot buildings
clipped_vienna_features_gdf.dropna(subset=["building"], how="all").plot(ax=ax, color="#FF5E5B")

# plot parkings
clipped_vienna_features_gdf[
    (clipped_vienna_features_gdf["amenity"] == "parking")
    | (clipped_vienna_features_gdf["highway"] == "pedestrian")
].plot(ax=ax, color="#2F3737", markersize=0.1)

# plot greenery
clipped_vienna_features_gdf[
    clipped_vienna_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="#8BB174")

xmin, ymin, xmax, ymax = vienna_center_circle_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = vienna_center_circle_gdf.plot(color="#F2F4CB", figsize=(16, 16))

# plot water
clipped_vienna_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="#a8e1e6"
)

# plot streets
clipped_vienna_features_gdf.dropna(subset=["highway"], how="all").plot(
    ax=ax, color="#475657", markersize=0.1
)

# plot buildings
clipped_vienna_features_gdf.dropna(subset=["building"], how="all").plot(ax=ax, color="#FF5E5B")

# plot parkings
clipped_vienna_features_gdf[
    (clipped_vienna_features_gdf["amenity"] == "parking")
    | (clipped_vienna_features_gdf["highway"] == "pedestrian")
].plot(ax=ax, color="#2F3737", markersize=0.1)

# plot greenery
clipped_vienna_features_gdf[
    clipped_vienna_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="#8BB174")

xmin, ymin, xmax, ymax = vienna_center_circle_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

Download all grouped features based on Geofabrik layers in New York, USA¶

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manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
manhattan_bbox_gdf = gpd.GeoDataFrame(
    geometry=[manhattan_bbox],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["New York"], name=REGIONS_INDEX),
)
manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
manhattan_bbox_gdf = gpd.GeoDataFrame(
    geometry=[manhattan_bbox],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["New York"], name=REGIONS_INDEX),
)

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loader = OSMPbfLoader()
new_york_features_gdf = loader.load(manhattan_bbox_gdf, GEOFABRIK_LAYERS)
new_york_features_gdf
loader = OSMPbfLoader()
new_york_features_gdf = loader.load(manhattan_bbox_gdf, GEOFABRIK_LAYERS)
new_york_features_gdf

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/geopandas/array.py:1638: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 (the single non-null crs provided).
  return GeometryArray(data, crs=_get_common_crs(to_concat))
/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/quackosm/osm_extracts/__init__.py:602: GeometryNotCoveredWarning: Skipping extract because of low IoU value (bbbike_newyork, 0.000187).
  warnings.warn(

Finished operation in 0:00:34

Out[12]:

	geometry	accommodation	buildings	catering	education	fuel_parking	health	highway_links	landuse	leisure	...	public	railways	shopping	tourism	traffic	transport	very_small_roads	water	water_traffic	waterways
feature_id
node/462004948	POINT (-73.95553 40.77949)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	railway=station	None	None	None	None
node/480491548	POINT (-73.96358 40.78135)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492539	POINT (-73.96863 40.77474)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492646	POINT (-73.96828 40.77501)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492947	POINT (-73.9665 40.7781)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/1151920424	LINESTRING (-73.96395 40.8029, -73.9639 40.802...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1151920425	LINESTRING (-73.96383 40.80284, -73.96379 40.8...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1151920426	LINESTRING (-73.96287 40.80417, -73.96284 40.8...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1153095181	LINESTRING (-73.96958 40.7629, -73.96956 40.76...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1153095182	LINESTRING (-73.96964 40.76279, -73.96965 40.7...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

49432 rows × 27 columns

Plot features¶

Inspired by https://snazzymaps.com/style/14889/flat-pale

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ax = manhattan_bbox_gdf.plot(color="#e7e7df", figsize=(16, 16))

# plot greenery
new_york_features_gdf[new_york_features_gdf["leisure"] == "leisure=park"].plot(
    ax=ax, color="#bae5ce"
)

# plot water
new_york_features_gdf.dropna(subset=["water", "waterways"], how="all").plot(ax=ax, color="#c7eced")

# plot streets
new_york_features_gdf.dropna(subset=["paths_unsuitable_for_cars"], how="all").plot(
    ax=ax, color="#e7e7df", linewidth=1
)
new_york_features_gdf.dropna(
    subset=["very_small_roads", "highway_links", "minor_roads"], how="all"
).plot(ax=ax, color="#fff", linewidth=2)
new_york_features_gdf.dropna(subset=["major_roads"], how="all").plot(
    ax=ax, color="#fac9a9", linewidth=3
)

# plot buildings
new_york_features_gdf.dropna(subset=["buildings"], how="all").plot(ax=ax, color="#cecebd")

xmin, ymin, xmax, ymax = manhattan_bbox_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = manhattan_bbox_gdf.plot(color="#e7e7df", figsize=(16, 16))

# plot greenery
new_york_features_gdf[new_york_features_gdf["leisure"] == "leisure=park"].plot(
    ax=ax, color="#bae5ce"
)

# plot water
new_york_features_gdf.dropna(subset=["water", "waterways"], how="all").plot(ax=ax, color="#c7eced")

# plot streets
new_york_features_gdf.dropna(subset=["paths_unsuitable_for_cars"], how="all").plot(
    ax=ax, color="#e7e7df", linewidth=1
)
new_york_features_gdf.dropna(
    subset=["very_small_roads", "highway_links", "minor_roads"], how="all"
).plot(ax=ax, color="#fff", linewidth=2)
new_york_features_gdf.dropna(subset=["major_roads"], how="all").plot(
    ax=ax, color="#fac9a9", linewidth=3
)

# plot buildings
new_york_features_gdf.dropna(subset=["buildings"], how="all").plot(ax=ax, color="#cecebd")

xmin, ymin, xmax, ymax = manhattan_bbox_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

Using OSMPbfLoader to download data for a specific area and transforming it to GeoParquet file¶

Download all grouped features based on Geofabrik layers in Reykjavík, Iceland¶

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loader = OSMPbfLoader()
reykjavik_gdf = geocode_to_region_gdf("Reykjavík, IS")
reykjavik_features_gpq = loader.load_to_geoparquet(reykjavik_gdf, GEOFABRIK_LAYERS)
reykjavik_features_gpq
loader = OSMPbfLoader()
reykjavik_gdf = geocode_to_region_gdf("Reykjavík, IS")
reykjavik_features_gpq = loader.load_to_geoparquet(reykjavik_gdf, GEOFABRIK_LAYERS)
reykjavik_features_gpq

/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/geopandas/array.py:1638: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as WGS 84 (the single non-null crs provided).
  return GeometryArray(data, crs=_get_common_crs(to_concat))

Finished operation in 0:00:22

Out[14]:

PosixPath('files/3b68f6ecc515eba6f588efa104d7eee92b450cf9c7e54f445afae421e604cf3f_098931824b94bfe02e01bc4987422a39ec47bd3f6924e5325ddae0846badafba_exploded.parquet')

Read those features using DuckDB¶

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import duckdb

connection = duckdb.connect()

connection.load_extension("parquet")
connection.load_extension("spatial")

features_relation = connection.read_parquet(str(reykjavik_features_gpq))
features_relation
import duckdb

connection = duckdb.connect()

connection.load_extension("parquet")
connection.load_extension("spatial")

features_relation = connection.read_parquet(str(reykjavik_features_gpq))
features_relation

Out[15]:

┌─────────────────┬───────────────┬─────────────┬───────────┬──────────────┬───────────┬──────────────┬──────────────────┬───────────────┬─────────┬─────────┬─────────────┬─────────────┬─────────────────┬─────────────┬──────────────┬───────────────────────────┬─────────┬─────────┬──────────┬────────────────────┬─────────────────────┬────────────────────────┬───────────┬──────────────────┬─────────┬───────────────┬───────────┬────────────────────────────────┐
│   feature_id    │ accommodation │ air_traffic │ buildings │   catering   │ education │ fuel_parking │      health      │ highway_links │ landuse │ leisure │ major_roads │ minor_roads │     miscpoi     │    money    │   natural    │ paths_unsuitable_for_cars │  pofw   │ public  │ railways │      shopping      │       tourism       │        traffic         │ transport │ very_small_roads │  water  │ water_traffic │ waterways │            geometry            │
│     varchar     │    varchar    │   varchar   │  varchar  │   varchar    │  varchar  │   varchar    │     varchar      │    varchar    │ varchar │ varchar │   varchar   │   varchar   │     varchar     │   varchar   │   varchar    │          varchar          │ varchar │ varchar │ varchar  │      varchar       │       varchar       │        varchar         │  varchar  │     varchar      │ varchar │    varchar    │  varchar  │            geometry            │
├─────────────────┼───────────────┼─────────────┼───────────┼──────────────┼───────────┼──────────────┼──────────────────┼───────────────┼─────────┼─────────┼─────────────┼─────────────┼─────────────────┼─────────────┼──────────────┼───────────────────────────┼─────────┼─────────┼──────────┼────────────────────┼─────────────────────┼────────────────────────┼───────────┼──────────────────┼─────────┼───────────────┼───────────┼────────────────────────────────┤
│ node/288037466  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9592124 64.1386272) │
│ node/288037479  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9633126 64.1389854) │
│ node/288037485  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9623437 64.140269)  │
│ node/288037508  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.964693 64.1407153)  │
│ node/288037511  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9520576 64.1367153) │
│ node/288037537  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9587812 64.1362093) │
│ node/310242628  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8922379 64.1284641) │
│ node/310242658  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ amenity=pharmacy │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8871717 64.1282437) │
│ node/310242659  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ amenity=atm │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8869499 64.1285056) │
│ node/310242661  │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ shop=shoes         │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8872672 64.1281469) │
│       ·         │  ·            │  ·          │  ·        │  ·           │  ·        │  ·           │  ·               │  ·            │  ·      │  ·      │  ·          │  ·          │  ·              │  ·          │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·                 │  ·                  │  ·                     │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·           │  ·        │  ·           │  ·               │  ·            │  ·      │  ·      │  ·          │  ·          │  ·              │  ·          │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·                 │  ·                  │  ·                     │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·           │  ·        │  ·           │  ·               │  ·            │  ·      │  ·      │  ·          │  ·          │  ·              │  ·          │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·                 │  ·                  │  ·                     │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│ node/6486040159 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8606526 64.1297733) │
│ node/6486040168 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8569205 64.1280503) │
│ node/6486040169 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=crossing       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.857719 64.1283394)  │
│ node/6489455817 │ NULL          │ NULL        │ NULL      │ amenity=cafe │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9435557 64.150968)  │
│ node/6491107180 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ tourism=information │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8982235 64.1335319) │
│ node/6672834943 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ shop=laundry       │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8757894 64.1459611) │
│ node/6672834947 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ amenity=toilets │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8755546 64.1459616) │
│ node/6679972738 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ highway=turning_circle │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8633952 64.1258772) │
│ node/6679972751 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ natural=tree │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL               │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8626014 64.1254421) │
│ node/6926652910 │ NULL          │ NULL        │ NULL      │ NULL         │ NULL      │ NULL         │ NULL             │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL            │ NULL        │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ amenity=car_rental │ NULL                │ NULL                   │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9348811 64.1374936) │
├─────────────────┴───────────────┴─────────────┴───────────┴──────────────┴───────────┴──────────────┴──────────────────┴───────────────┴─────────┴─────────┴─────────────┴─────────────┴─────────────────┴─────────────┴──────────────┴───────────────────────────┴─────────┴─────────┴──────────┴────────────────────┴─────────────────────┴────────────────────────┴───────────┴──────────────────┴─────────┴───────────────┴───────────┴────────────────────────────────┤
│ ? rows (>9999 rows, 20 shown)                                                                                                                                                                                                                                                                                                                                                                                                                                   29 columns │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Count all buildings¶

In [16]:

Copied!

features_relation.filter("buildings IS NOT NULL").count("feature_id")
features_relation.filter("buildings IS NOT NULL").count("feature_id")

Out[16]:

┌───────────────────┐
│ count(feature_id) │
│       int64       │
├───────────────────┤
│             25361 │
└───────────────────┘

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Count all buildings¶

Download all features from `HEX2VEC_FILTER` in Warsaw, Poland¶