AI-generated Key Takeaways
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The
loadBigQueryTable
function reads data from a BigQuery table and returns aFeatureCollection
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The function requires the path to the BigQuery table and optionally accepts a parameter for the geometry column.
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Examples demonstrate how to use
loadBigQueryTable
in both JavaScript and Python to load and display geographical features from a BigQuery table.
Usage | Returns |
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ee.FeatureCollection.loadBigQueryTable(table, geometryColumn) | FeatureCollection |
Argument | Type | Details |
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table | String | Path to BigQuery table in a `project.dataset.table` format. |
geometryColumn | String, default: null | The name of the column to use as the main feature geometry. If not specified, the first column with GEOGRAPHY type will be used. |
Examples
Code Editor (JavaScript)
// Load stations from the New York Subway System. var features = ee.FeatureCollection.loadBigQueryTable({ table: 'bigquery-public-data.new_york_subway.stations', geometryColumn: 'station_geom', }); // Display all relevant features on the map. Map.setCenter(-73.90, 40.73, 11); Map.addLayer(features, {'color': 'black'}, 'Stations from New York Subway System'); // Print all stations in the "Astoria" line. var line = features.filter(ee.Filter.eq('line', 'Astoria')); print(line); Map.addLayer(line, {'color': 'yellow'}, 'Astoria line');
import ee import geemap.core as geemap
Colab (Python)
# Load stations from the New York Subway System. features = ee.FeatureCollection.loadBigQueryTable( table="bigquery-public-data.new_york_subway.stations", geometryColumn="station_geom") # Display all relevant features on the map. m = geemap.Map() m.set_center(-73.90, 40.73, 11) m.add_layer( features, {'color': 'black'}, 'Stations from New York Subway System') # Print all stations in the "Astoria" line. line = features.filter(ee.Filter.eq('line', 'Astoria')) display(line) m.add_layer(line, {'color': 'yellow'}, 'Astoria line') m