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

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/1217538044	POINT (21.02408 52.20043)	None	None	None	None	None	None	None	None	None	None	hairdresser	None	None	None	None
node/1222395705	POINT (21.05842 52.13094)	None	pharmacy	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1222850392	POINT (21.02294 52.20118)	None	None	None	None	None	None	None	None	None	None	hairdresser	None	None	None	None
node/1222850410	POINT (21.02325 52.20128)	None	atm	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1222850425	POINT (21.02307 52.20219)	None	None	None	None	None	None	None	None	None	None	bakery	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/1375049608	POLYGON ((20.90344 52.23987, 20.90323 52.23988...	None	None	None	None	None	None	None	None	scrub	None	None	None	None	None	None
way/1375049610	POLYGON ((20.90326 52.23982, 20.9032 52.23983,...	None	None	None	None	None	None	garden	None	None	None	None	None	None	None	None
way/1375049616	POLYGON ((20.9034 52.23942, 20.90315 52.23944,...	None	None	None	None	None	None	garden	None	None	None	None	None	None	None	None
way/1375049619	POLYGON ((20.90271 52.2396, 20.90271 52.2396, ...	None	None	yes	None	None	None	None	None	None	None	None	None	None	None	None
way/1375049620	POLYGON ((20.90223 52.2395, 20.90222 52.23949,...	None	None	None	None	None	None	garden	None	None	None	None	None	None	None	None

335809 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: 367.'

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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:city': ['London'],
 'addr:country': ['CZ', 'DE', 'RU', 'TW', 'US'],
 'addr:state': ['AZ',
  'CA',
  'CO',
  'CT',
  'FL',
  'IN',
  'KY',
  'MD',
  'ME',
  'NC',
  'NJ',
  'NY',
  'PA',
  'TX']}

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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/3.10.17/x64/lib/python3.10/site-packages/quackosm/pb
f_file_reader.py:2614: UserWarning: Select clause contains more than 100 columns
(found 367 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	access	admin_level	advertising	amenity	area:highway	artwork_type	atm	barrier	bench	...	tunnel	type	usage	vehicle	vending	waste	water	water_source	waterway	wheelchair
feature_id
node/1262234512	POINT (16.37167 48.20792)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	yes
node/1306550378	POINT (16.37075 48.20813)	None	None	None	drinking_water	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	yes
node/1308579742	POINT (16.37889 48.20807)	None	None	None	bar	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/1308606007	POINT (16.3708 48.21521)	None	None	None	restaurant	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	limited
node/1308610739	POINT (16.37117 48.21391)	None	None	None	bar	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	limited
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation/8075616	POLYGON ((16.36914 48.20459, 16.36915 48.20459...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/8075617	POLYGON ((16.36821 48.20446, 16.36824 48.20447...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/8075646	POLYGON ((16.36712 48.20596, 16.36709 48.20594...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/11951565	POLYGON ((16.36197 48.21226, 16.36197 48.21226...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/8075633	POLYGON ((16.36868 48.20321, 16.36857 48.203, ...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None

22844 rows × 190 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()

No description has been provided for this image

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/3.10.17/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 0:00:35

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/1222937573	LINESTRING (-73.93806 40.80287, -73.93809 40.8...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1222937574	LINESTRING (-73.93817 40.80255, -73.93821 40.8...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1222937575	LINESTRING (-73.93807 40.80234, -73.93806 40.8...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1222937576	POLYGON ((-73.93876 40.80276, -73.9389 40.8025...	None	building=apartments	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
way/1222937579	POLYGON ((-73.93983 40.80295, -73.93915 40.802...	None	None	None	None	None	None	None	landuse=residential	None	...	None	None	None	None	None	None	None	None	None	None

50276 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

Finished operation in 0:00:23

Out[14]:

PosixPath('files/4e8a6f659f407cf02e3da5661575f63ce8a59a7d27bbf3e8751b97e290553593_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/13472516   │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL    │ NULL    │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL    │ highway=motorway_junction │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7847679 64.1235709) │
│ node/13489663   │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL    │ NULL    │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL    │ highway=motorway_junction │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8324909 64.1237812) │
│ node/14586972   │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL    │ NULL    │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL    │ highway=motorway_junction │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7644946 64.1320481) │
│ node/16570604   │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL    │ NULL    │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL    │ highway=motorway_junction │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7909159 64.1243715) │
│ node/169087860  │ 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 (-21.9458058 64.1408098) │
│ node/181003495  │ 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 (-21.9519829 64.1278967) │
│ node/181003555  │ 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 (-21.9524324 64.1277162) │
│ node/181797458  │ 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 (-21.9525919 64.1349487) │
│ node/196382899  │ 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 (-21.9521497 64.13449)   │
│ node/196382907  │ 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 (-21.9496356 64.1327529) │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·      │  ·      │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·      │          ·                │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·      │  ·      │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·      │          ·                │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·      │  ·      │  ·           │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·      │          ·                │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│ node/6299752284 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL    │ NULL    │ NULL         │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL    │ highway=street_lamp       │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9089819 64.1453061) │
│ node/6299758987 │ 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 (-21.9112094 64.1451391) │
│ node/6299758988 │ 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 (-21.9108632 64.1451626) │
│ node/6299758989 │ 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 (-21.9107912 64.1451687) │
│ node/6299758990 │ 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 (-21.9107231 64.1451792) │
│ node/6301344112 │ 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 (-21.8910855 64.1442354) │
│ node/6301344113 │ 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 (-21.8907261 64.1441957) │
│ node/6301344114 │ 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 (-21.8903506 64.1441138) │
│ node/6301344115 │ 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 (-21.8900287 64.1440553) │
│ node/6301344116 │ 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 (-21.889841 64.1439875)  │
├─────────────────┴───────────────┴─────────────┴───────────┴──────────┴───────────┴──────────────┴─────────┴───────────────┴─────────┴─────────┴─────────────┴─────────────┴─────────┴─────────┴──────────────┴───────────────────────────┴─────────┴─────────┴──────────┴──────────┴─────────┴───────────────────────────┴───────────┴──────────────────┴─────────┴───────────────┴───────────┴────────────────────────────────┤
│ ? 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       │
├───────────────────┤
│             25363 │
└───────────────────┘

You can see more examples of how to use PbfFileReader from the QuackOSM library in the docs: https://kraina-ai.github.io/quackosm/latest/examples/pbf_file_reader/

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Count all buildings¶

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