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.

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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 0:00:52

Out[2]:

	geometry	aeroway	amenity	building	healthcare	historic	landuse	leisure	military	natural	office	shop	sport	tourism	water	waterway
feature_id
node/1692272403	POINT (21.08287 52.26558)	None	None	None	None	None	None	None	None	None	None	None	None	museum	None	None
node/1692272423	POINT (21.08241 52.2657)	None	charging_station	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1692396880	POINT (21.04772 52.2375)	None	bicycle_parking	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1692401647	POINT (21.02456 52.2335)	None	None	None	None	None	None	None	None	None	None	bicycle	None	None	None	None
node/1692404943	POINT (21.02492 52.2329)	None	bicycle_parking	None	None	None	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/961142300	POLYGON ((21.11355 52.25622, 21.11354 52.25622...	None	None	retail	None	None	None	None	None	None	None	None	None	None	None	None
way/961142301	POLYGON ((21.1132 52.25792, 21.11322 52.25838,...	None	None	yes	None	None	None	None	None	None	None	None	None	None	None	None
way/961142302	POLYGON ((21.11967 52.25702, 21.11966 52.25706...	None	None	detached	None	None	None	None	None	None	None	None	None	None	None	None
way/961142303	POLYGON ((21.11848 52.25688, 21.11848 52.2569,...	None	None	detached	None	None	None	None	None	None	None	None	None	None	None	None
way/961142304	POLYGON ((21.11889 52.25544, 21.1189 52.25545,...	None	None	detached	None	None	None	None	None	None	None	None	None	None	None	None

326380 rows × 16 columns

Plot features¶

Inspired by prettymaps

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

/tmp/ipykernel_4390/1645165564.py:1: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
  clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.unary_union)

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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: 354.'

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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/3.10.15/x64/lib/python3.10/site-packages/quackosm/pb
f_file_reader.py:2614: UserWarning: Select clause contains more than 100 columns
(found 354 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	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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 ((16.36871 48.20327, 16.36857 48.203, ...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None

22667 rows × 189 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/3.10.15/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/2494098726	POINT (-73.98357 40.77559)	None	None	None	None	None	None	None	None	None	...	amenity=post_office	None	None	None	None	None	None	None	None	None
node/2494098730	POINT (-73.98352 40.77565)	None	None	None	None	None	None	None	None	None	...	None	None	shop=beauty	None	None	None	None	None	None	None
node/2723866660	POINT (-73.96713 40.76986)	None	None	amenity=cafe	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/2723866677	POINT (-73.96559 40.76912)	None	None	None	None	None	None	None	None	None	...	office=diplomatic	None	None	None	None	None	None	None	None	None
node/2723866723	POINT (-73.96369 40.76865)	None	None	None	None	None	None	None	None	None	...	None	None	shop=florist	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation/5265382	POLYGON ((-73.95045 40.78489, -73.95012 40.785...	None	building=yes	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/5389891	POLYGON ((-73.98383 40.77276, -73.9839 40.7727...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/5917031	POLYGON ((-73.97819 40.76298, -73.97856 40.762...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/6779497	POLYGON ((-73.98378 40.7671, -73.98385 40.767,...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/4623434	POLYGON ((-73.98243 40.77731, -73.98245 40.777...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

49317 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/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/580916853  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ tourism=attraction │ NULL                      │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9345086 64.1374733) │
│ node/582112543  │ 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 (-21.896997 64.1328301)  │
│ node/582112601  │ 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 (-21.8950065 64.1325403) │
│ node/1221993805 │ 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 (-21.9163661 64.1431158) │
│ node/1221993833 │ 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 (-21.9222174 64.1377959) │
│ node/1221993868 │ 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 (-21.9202618 64.1395186) │
│ node/1229213284 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ amenity=fuel │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL               │ NULL                      │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.9662321 64.1435561) │
│ node/1229320411 │ 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 (-21.8631485 64.126886)  │
│ node/1229320424 │ 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 (-21.863451 64.12618)    │
│ node/1229340591 │ 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 (-21.8729315 64.1332079) │
│        ·        │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                 │  ·                        │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│        ·        │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                 │  ·                        │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│        ·        │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                 │  ·                        │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│ node/2018349582 │ 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 (-21.7827723 64.097729)  │
│ node/2018349588 │ 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 (-21.844457 64.0982388)  │
│ node/2018349591 │ 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 (-21.7713334 64.0982995) │
│ node/2018349594 │ 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 (-21.8087048 64.0983843) │
│ node/2018349598 │ 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 (-21.8178784 64.0987291) │
│ node/2018349601 │ 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 (-21.8205695 64.0987297) │
│ node/2018349605 │ 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 (-21.8231854 64.0987393) │
│ node/2018349608 │ 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 (-21.8232813 64.0987432) │
│ node/2018349611 │ 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 (-21.8152231 64.098907)  │
│ node/2018349614 │ 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 (-21.7852045 64.0990287) │
├─────────────────┴───────────────┴─────────────┴───────────┴──────────┴───────────┴──────────────┴─────────┴───────────────┴─────────┴─────────┴─────────────┴─────────────┴───────────────┴─────────┴─────────┴───────────────────────────┴─────────┴─────────┴──────────┴──────────┴────────────────────┴───────────────────────────┴───────────┴──────────────────┴─────────┴───────────────┴───────────┴────────────────────────────────┤
│ ? 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       │
├───────────────────┤
│             25359 │
└───────────────────┘

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/

OSM PBF Loader¶

Using OSMPbfLoader to download data for a specific area¶

Download all features from HEX2VEC_FILTER in Warsaw, Poland¶

Plot features¶

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Plot features¶

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¶

Read those features using DuckDB¶

Count all buildings¶

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