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 0:00:55
Out[2]:
| geometry | aeroway | amenity | building | healthcare | historic | landuse | leisure | military | natural | office | shop | sport | tourism | water | waterway | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| feature_id | ||||||||||||||||
| node/59815899 | POINT (21.09668 52.17673) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | dam |
| node/61253022 | POINT (21.00581 52.17944) | None | parking_entrance | None | None | None | None | None | None | None | None | None | None | None | None | None |
| node/95003080 | POINT (20.96723 52.24442) | None | fuel | None | None | None | None | None | None | None | None | None | None | None | None | None |
| node/95100197 | POINT (20.93535 52.25315) | None | fountain | None | None | None | None | None | None | None | None | None | None | None | None | None |
| node/96657908 | POINT (20.92989 52.26453) | None | parking | None | None | None | None | None | None | None | None | None | None | None | None | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| way/961701759 | POLYGON ((21.21526 52.16437, 21.21523 52.16439... | None | None | detached | None | None | None | None | None | None | None | None | None | None | None | None |
| way/961701760 | POLYGON ((21.21448 52.16395, 21.21457 52.16399... | None | None | detached | None | None | None | None | None | None | None | None | None | None | None | None |
| way/961701762 | POLYGON ((21.21208 52.16365, 21.21207 52.16365... | None | None | detached | None | None | None | None | None | None | None | None | None | None | None | None |
| way/961701763 | POLYGON ((21.21348 52.16363, 21.21353 52.16358... | None | None | house | None | None | None | None | None | None | None | None | None | None | None | None |
| way/961701764 | POLYGON ((21.21149 52.16256, 21.21152 52.16257... | None | None | apartments | None | None | None | None | None | None | None | None | None | None | None | None |
345328 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()
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: 376.'
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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',
'military',
'no',
'permissive',
'permit',
'private',
'unknown',
'yes'],
'access_control': ['full', 'partial'],
'addr:TW:dataset': ['137998'],
'addr:city': ['London', '臺北市'],
'addr:country': ['CZ', 'DE', 'RU', 'TW', 'US']}
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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/3.10.18/x64/lib/python3.10/site-packages/quackosm/pb f_file_reader.py:2614: UserWarning: Select clause contains more than 100 columns (found 376 columns). Query might fail with insufficient memory resources. Consider applying more restrictive OsmTagsFilter for parsing. warnings.warn(
Finished operation in 0:00:29
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/1354649450 | POINT (16.36382 48.20387) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1354649451 | POINT (16.36286 48.2033) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1354649452 | POINT (16.36362 48.20375) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1354649456 | POINT (16.3622 48.20291) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1354652239 | POINT (16.36341 48.20388) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| way/419087317 | LINESTRING (16.37696 48.2118, 16.37703 48.2117... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/419902855 | LINESTRING (16.37198 48.21717, 16.37185 48.217... | customers | None | None | None | None | None | None | None | None | ... | yes | None | None | None | None | None | None | None | None | yes |
| way/420290632 | LINESTRING (16.37492 48.21331, 16.37521 48.213... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/420290639 | LINESTRING (16.37414 48.21294, 16.37437 48.21307) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/420863639 | LINESTRING (16.37462 48.21008, 16.37442 48.2102) | None | None | None | None | None | None | None | None | None | ... | building_passage | None | None | None | None | None | None | None | None | None |
23279 rows × 194 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/3.10.18/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/1135726216 | LINESTRING (-73.9824 40.77804, -73.98282 40.77... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/1136391283 | LINESTRING (-73.99032 40.76161, -73.99046 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/1136398322 | LINESTRING (-73.94759 40.78357, -73.94762 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/1136460180 | LINESTRING (-73.9829 40.76338, -73.98293 40.76... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/1136460181 | LINESTRING (-73.98304 40.76313, -73.98322 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
50809 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 0:00:22
Out[14]:
PosixPath('files/27255c31a829630e955fdd267713c8cf23ae8384764ce0f9d850bad99fee31a1_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/647029639 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ highway=traffic_signals │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.9471366 64.1421593) │ │ node/649707544 │ NULL │ NULL │ NULL │ amenity=fast_food │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8255422 64.1235888) │ │ node/649762012 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ public_transport=stop_position │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8226559 64.1233367) │ │ node/658052889 │ 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 (-21.7448043 64.1337358) │ │ node/659603820 │ 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 (-21.7670966 64.1243203) │ │ node/659603821 │ 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 (-21.7653526 64.1239808) │ │ node/659603822 │ 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 (-21.7627296 64.1227517) │ │ node/659603830 │ 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 (-21.7421181 64.1258596) │ │ node/661036955 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ highway=traffic_signals │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8470747 64.1200648) │ │ node/661194738 │ 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 (-21.8984664 64.1258399) │ │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ · │ │ node/7643090087 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ tourism=artwork │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.9046649 64.1457098) │ │ node/7644104100 │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bicycle_parking │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8824838 64.123059) │ │ node/7644104101 │ amenity=shelter │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.7637957 64.1303604) │ │ node/7644104102 │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bicycle_parking │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.7639167 64.1303925) │ │ node/7644104103 │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bicycle_parking │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.806887 64.1364058) │ │ node/7647105009 │ 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 (-21.8579813 64.1376656) │ │ node/7652671975 │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bicycle_parking │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8885086 64.1265407) │ │ node/7653666830 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bench │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8960574 64.1286251) │ │ node/7653666831 │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bench │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8961224 64.1286364) │ │ node/7653666833 │ NULL │ NULL │ NULL │ NULL │ NULL │ amenity=bicycle_parking │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ NULL │ POINT (-21.8965863 64.1285789) │ ├─────────────────┴─────────────────┴─────────────┴───────────┴───────────────────┴───────────┴─────────────────────────┴─────────┴───────────────┴─────────┴─────────┴─────────────┴─────────────┴───────────────┴─────────┴─────────┴───────────────────────────┴─────────┴─────────┴──────────┴──────────┴─────────────────┴─────────────────────────┴────────────────────────────────┴──────────────────┴─────────┴───────────────┴───────────┴────────────────────────────────┤ │ ? 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 │ ├───────────────────┤ │ 25370 │ └───────────────────┘
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/