Add DuckDB Vector Tiles

Uncomment the following line to install leafmap if needed.

In [1]:

# %pip install "leafmap[duckdb]"

# %pip install "leafmap[duckdb]"

In [2]:

import leafmap.maplibregl as leafmap

import leafmap.maplibregl as leafmap

If you are running in a remote Jupyter environment, you need to configure leafmap with your JupyterHub URL. Uncomment the following line and replace https://your-jupyterhub-domain.com with your JupyterHub URL.

In [3]:

# leafmap.configure_jupyterhub("https://your-jupyterhub-domain.com")

# leafmap.configure_jupyterhub("https://your-jupyterhub-domain.com")

Download a sample GeoParquet dataset from Source Coop.

In [4]:

url = "https://data.source.coop/giswqs/nwi/wetlands/RI_Wetlands.parquet"
fileapth = leafmap.download_file(url)

url = "https://data.source.coop/giswqs/nwi/wetlands/RI_Wetlands.parquet" fileapth = leafmap.download_file(url)

Downloading...
From: https://data.source.coop/giswqs/nwi/wetlands/RI_Wetlands.parquet
To: /home/runner/work/leafmap/leafmap/docs/maplibre/RI_Wetlands.parquet

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Example 1: Load data from vector file(creates temporary database)

In [5]:

m = leafmap.Map(style="liberty", height="600px")
m.add_basemap("Esri.WorldImagery")
m.add_duckdb_layer(
    data=fileapth,  # Supports GeoParquet, GeoJSON, and GeoPackage, etc.
    layer_name="wetlands",
    layer_type="fill",
    paint={"fill-color": "#3388ff"},
    opacity=0.5,
    fit_bounds=True,
    use_view=False,  # For remote datasets, set use_view to True to avoid copying data to local
    min_zoom=None,  # For large datasets, set min_zoom to None to avoid zooming out too much
    quiet=False,
)
m

m = leafmap.Map(style="liberty", height="600px") m.add_basemap("Esri.WorldImagery") m.add_duckdb_layer( data=fileapth, # Supports GeoParquet, GeoJSON, and GeoPackage, etc. layer_name="wetlands", layer_type="fill", paint={"fill-color": "#3388ff"}, opacity=0.5, fit_bounds=True, use_view=False, # For remote datasets, set use_view to True to avoid copying data to local min_zoom=None, # For large datasets, set min_zoom to None to avoid zooming out too much quiet=False, ) m

Note: Using temporary database file for tile serving: /tmp/leafmap_duckdb_957fl51o.db
Initializing DuckDB database for layer 'wetlands'...

Auto-detected CRS: EPSG:5070 (NAD83 / Conus Albers)
  Sample coordinates: X=2045123.16, Y=2319961.41
  Use src_crs='EPSG:5070' to override if incorrect

Successfully loaded data into table 'features' (transformed from EPSG:5070 to EPSG:3857)
Total features: 55229

Starting DuckDB tile server on port 8000...

DuckDB tile server running at http://127.0.0.1:8000/
Tiles available at: http://127.0.0.1:8000/tiles/{z}/{x}/{y}.pbf
 * Serving Flask app 'leafmap.common'

 * Debug mode: off

Layer 'wetlands' added successfully!

Example 2: Load from existing DuckDB database (no data loading)

In [6]:

url = "https://data.gishub.org/duckdb/nyc_data.db.zip"
leafmap.download_file(url)

url = "https://data.gishub.org/duckdb/nyc_data.db.zip" leafmap.download_file(url)

Downloading...
From: https://data.gishub.org/duckdb/nyc_data.db.zip
To: /home/runner/work/leafmap/leafmap/docs/maplibre/nyc_data.db.zip

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Extracting files...

Out[6]:

'/home/runner/work/leafmap/leafmap/docs/maplibre/nyc_data.db.zip'

In [7]:

db_path = "nyc_data.db"

db_path = "nyc_data.db"

In [8]:

#
m = leafmap.Map(
    center=[-73.9031255, 40.7127753], zoom=9, style="liberty", height="600px"
)
m.add_duckdb_layer(
    database_path=db_path,
    table_name="nyc_neighborhoods",
    layer_type="fill",
    paint={"fill-color": "#ff0000"},
    opacity=0.5,
    fit_bounds=False,
    src_crs="EPSG:26918",
    quiet=True,
)
m

# m = leafmap.Map( center=[-73.9031255, 40.7127753], zoom=9, style="liberty", height="600px" ) m.add_duckdb_layer( database_path=db_path, table_name="nyc_neighborhoods", layer_type="fill", paint={"fill-color": "#ff0000"}, opacity=0.5, fit_bounds=False, src_crs="EPSG:26918", quiet=True, ) m