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.
In [1]:
Copied!
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¶
In [2]:
Copied!
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/3.10.14/x64/lib/python3.10/site-packages/srai/loaders/osm_loaders/osm_pbf_loader.py:128: FutureWarning: Use `convert_geometry_to_geodataframe` instead. Deprecated since 0.8.1 version. features_gdf = pbf_reader.get_features_gdf_from_geometry(
Downloading data from 'https://download.geofabrik.de/europe/poland/mazowieckie-latest.osm.pbf' to file '/home/runner/work/srai/srai/examples/loaders/files/Geofabrik_mazowieckie.osm.pbf'.
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SHA256 hash of downloaded file: 55af0dc58db6d75825e0dd138f542cbab67cac6bb0e25c907445958830c614cd Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
Finished operation in 0:01:58
Out[2]:
| geometry | aeroway | amenity | building | healthcare | historic | landuse | leisure | military | natural | office | shop | sport | tourism | water | waterway | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| feature_id | ||||||||||||||||
| node/31005854 | POINT (20.94595 52.17691) | None | restaurant | None | None | None | None | None | None | None | None | None | None | None | None | None |
| node/31156693 | POINT (20.95489 52.27100) | None | None | None | None | None | None | None | None | None | None | bakery | None | None | None | None |
| node/31917380 | POINT (21.01451 52.21653) | None | None | None | None | memorial | None | None | None | None | None | None | None | None | None | None |
| node/32599714 | POINT (21.01518 52.21904) | None | parking_entrance | None | None | None | None | None | None | None | None | None | None | None | None | None |
| node/477366355 | POINT (20.92955 52.34046) | None | restaurant | None | None | None | None | None | None | None | None | None | None | None | None | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| way/642924278 | POLYGON ((21.02076 52.21405, 21.02068 52.21401... | None | parking | None | None | None | None | None | None | None | None | None | None | None | None | None |
| way/642928875 | POLYGON ((21.04211 52.17704, 21.04212 52.17711... | None | None | apartments | None | None | None | None | None | None | None | None | None | None | None | None |
| way/643078227 | POLYGON ((21.01589 52.22871, 21.01591 52.22867... | None | None | retail | None | None | None | None | None | None | None | None | None | None | None | None |
| way/643081061 | POLYGON ((21.01640 52.22881, 21.01642 52.22877... | None | None | retail | None | None | None | None | None | None | None | None | None | None | None | None |
| way/643390349 | POLYGON ((21.03652 52.17997, 21.03494 52.17946... | None | None | None | None | None | None | None | None | grassland | None | None | None | None | None | None |
314175 rows × 16 columns
Plot features¶
Inspired by prettymaps
In [3]:
Copied!
clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.unary_union)
clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.unary_union)
In [4]:
Copied!
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¶
In [5]:
Copied!
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: 332.'
In [6]:
Copied!
{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'],
'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']}
In [7]:
Copied!
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),
)
In [8]:
Copied!
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/3.10.14/x64/lib/python3.10/site-packages/srai/loaders/osm_loaders/osm_pbf_loader.py:128: FutureWarning: Use `convert_geometry_to_geodataframe` instead. Deprecated since 0.8.1 version. features_gdf = pbf_reader.get_features_gdf_from_geometry( Downloading data from 'https://download.geofabrik.de/europe/austria-latest.osm.pbf' to file '/home/runner/work/srai/srai/examples/loaders/files/Geofabrik_austria.osm.pbf'.
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SHA256 hash of downloaded file: f444d0509a54af4d16184d251492780f1ffe2c444ded52e6b9c1b8b9e36503be Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/quackosm/pbf_file_reader.py:2393: UserWarning: Select clause contains more than 100 columns (found 332 columns). Query might fail with insufficient memory resources. Consider applying more restrictive OsmTagsFilter for parsing. warnings.warn(
Finished operation in 0:05:11
Out[8]:
| geometry | abandoned | access | admin_level | advertising | amenity | area:highway | artwork_type | atm | barrier | ... | tram | tunnel | type | vehicle | vending | waste | water | water_source | waterway | wheelchair | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| feature_id | |||||||||||||||||||||
| node/1250635482 | POINT (16.37883 48.20857) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1350200407 | POINT (16.37488 48.21051) | None | None | None | None | ice_cream | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | no |
| node/1360385587 | POINT (16.37741 48.20612) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1375294847 | POINT (16.35947 48.21124) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/1375294851 | POINT (16.35848 48.21226) | None | None | None | None | restaurant | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | no |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| relation/12512007 | POLYGON ((16.36456 48.21109, 16.36455 48.21110... | None | None | None | None | None | None | None | None | None | ... | None | None | multipolygon | None | None | None | None | None | None | None |
| relation/13052547 | POLYGON ((16.38105 48.20630, 16.38101 48.20628... | None | None | None | None | None | None | None | None | None | ... | None | None | multipolygon | None | None | None | None | None | None | None |
| relation/13052548 | POLYGON ((16.38122 48.20624, 16.38121 48.20622... | None | None | None | None | None | None | None | None | None | ... | None | None | multipolygon | None | None | None | None | None | None | None |
| relation/17553875 | POLYGON ((16.36461 48.20396, 16.36463 48.20397... | None | None | None | None | None | None | None | None | None | ... | None | None | multipolygon | None | None | None | None | None | None | None |
| relation/17664508 | POLYGON ((16.36939 48.20957, 16.36942 48.20959... | None | None | None | None | None | None | None | None | None | ... | None | None | multipolygon | None | None | None | None | None | None | None |
21740 rows × 174 columns
Plot features¶
Uses default preset colours from prettymaps
In [9]:
Copied!
clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)
clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)
In [10]:
Copied!
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¶
In [11]:
Copied!
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),
)
In [12]:
Copied!
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/3.10.14/x64/lib/python3.10/site-packages/srai/loaders/osm_loaders/osm_pbf_loader.py:128: FutureWarning: Use `convert_geometry_to_geodataframe` instead. Deprecated since 0.8.1 version. features_gdf = pbf_reader.get_features_gdf_from_geometry( Downloading data from 'https://download.geofabrik.de/north-america/us/new-york-latest.osm.pbf' to file '/home/runner/work/srai/srai/examples/loaders/files/Geofabrik_us_new-york.osm.pbf'.
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SHA256 hash of downloaded file: aa3944e9f0eb52d4d30287d0e625b96026bd4d0c784faf81f744ac2998b15d1d Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
Downloading data from 'https://download.geofabrik.de/north-america/us/new-jersey-latest.osm.pbf' to file '/home/runner/work/srai/srai/examples/loaders/files/Geofabrik_us_new-jersey.osm.pbf'.
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SHA256 hash of downloaded file: 969823d398a5427811f33c237567ae1876d603259eb68dee530626455327d802 Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
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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 | |||||||||||||||||||||
| way/266900662 | POLYGON ((-73.95228 40.77137, -73.95236 40.771... | None | building=yes | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/766496197 | LINESTRING (-73.95487 40.78345, -73.95493 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/1098847602 | LINESTRING (-73.97340 40.79274, -73.97344 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/2761520418 | POINT (-73.96855 40.79975) | None | None | amenity=restaurant | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/266904093 | POLYGON ((-73.94647 40.77372, -73.94648 40.773... | None | building=yes | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| way/426502694 | LINESTRING (-73.97161 40.77347, -73.97161 40.7... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/266909596 | POLYGON ((-73.95309 40.77530, -73.95312 40.775... | None | building=yes | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| way/815947026 | LINESTRING (-73.94041 40.80206, -73.94020 40.8... | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | highway=service | None | None | None |
| node/2477910751 | POINT (-73.94051 40.76549) | None | None | None | None | None | None | None | None | None | ... | None | None | None | None | None | None | None | None | None | None |
| node/8333740822 | POINT (-73.98534 40.77069) | None | None | None | None | None | None | None | None | None | ... | None | None | shop=books | None | None | None | None | None | None | None |
47692 rows × 27 columns
Plot features¶
Inspired by https://snazzymaps.com/style/14889/flat-pale
In [13]:
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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¶
In [14]:
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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/3.10.14/x64/lib/python3.10/site-packages/srai/loaders/osm_loaders/osm_pbf_loader.py:191: FutureWarning: Use `convert_geometry_to_parquet` instead. Deprecated since 0.8.1 version. geoparquet_file_path = pbf_reader.convert_geometry_filter_to_gpq( Downloading data from 'https://download.geofabrik.de/europe/iceland-latest.osm.pbf' to file '/home/runner/work/srai/srai/examples/loaders/files/Geofabrik_iceland.osm.pbf'.
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SHA256 hash of downloaded file: 60ad7fa2bb26be88ab39dcf2a4915132e3fbcc9af63cdeb2e656c811f511f670 Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.
Finished operation in 0:00:23
Out[14]:
PosixPath('files/f2f6702ed375b9cc7c1af2b83286016228f49730ff409f3e255b136a0fcce996_098931824b94bfe02e01bc4987422a39ec47bd3f6924e5325ddae0846badafba_exploded.parquet')
Read those features using DuckDB¶
In [15]:
Copied!
import duckdb
connection = duckdb.connect()
connection.load_extension("parquet")
connection.load_extension("spatial")
features_relation = connection.read_parquet(str(reykjavik_features_gpq)).project(
"* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
)
features_relation
import duckdb
connection = duckdb.connect()
connection.load_extension("parquet")
connection.load_extension("spatial")
features_relation = connection.read_parquet(str(reykjavik_features_gpq)).project(
"* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
)
features_relation
Out[15]:
┌─────────────────┬───────────────┬─────────────┬───┬─────────┬───────────────┬───────────┬──────────────────────┐ │ feature_id │ accommodation │ air_traffic │ … │ water │ water_traffic │ waterways │ geometry │ │ varchar │ varchar │ varchar │ │ varchar │ varchar │ varchar │ geometry │ ├─────────────────┼───────────────┼─────────────┼───┼─────────┼───────────────┼───────────┼──────────────────────┤ │ node/13472516 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.7847679… │ │ node/13489663 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.8324909… │ │ node/14586972 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.7644946… │ │ node/16570604 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.7909159… │ │ node/169087860 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9458058… │ │ node/181003495 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9519829… │ │ node/181003555 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9524324… │ │ node/181797458 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9525919… │ │ node/196382899 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9521497… │ │ node/196382907 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9496356… │ │ · │ · │ · │ · │ · │ · │ · │ · │ │ · │ · │ · │ · │ · │ · │ · │ · │ │ · │ · │ · │ · │ · │ · │ · │ · │ │ node/2004274549 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9006636… │ │ node/2004274553 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9074806… │ │ node/2004274554 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9069323… │ │ node/2004274557 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.8946369… │ │ node/2004274561 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.8954392… │ │ node/2004274564 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.8962997… │ │ node/2004274568 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.899306 … │ │ node/2004274575 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9093655… │ │ node/2004274576 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9047261… │ │ node/2004274578 │ NULL │ NULL │ … │ NULL │ NULL │ NULL │ POINT (-21.9033679… │ ├─────────────────┴───────────────┴─────────────┴───┴─────────┴───────────────┴───────────┴──────────────────────┤ │ ? rows (>9999 rows, 20 shown) 29 columns (7 shown) │ └────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
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 │ ├───────────────────┤ │ 25305 │ └───────────────────┘
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/