AI-generated Key Takeaways
-
The
ee.FeatureCollection.runBigQueryfunction executes a BigQuery query and returns the results as a FeatureCollection. -
Arguments for the function include the SQL
querystring, an optionalgeometryColumnto specify the geometry column, and an optionalmaxBytesBilledto set a limit on billed bytes. -
The examples demonstrate how to use
runBigQueryin both JavaScript and Python to fetch data from BigQuery and visualize or analyze it.
| Usage | Returns |
|---|---|
ee.FeatureCollection.runBigQuery(query, geometryColumn
, maxBytesBilled
)
|
FeatureCollection |
| Argument | Type | Details |
|---|---|---|
query
|
String | GoogleSQL query to perform on the BigQuery resources. |
geometryColumn
|
String, default: null | The name of the column to use as the main feature geometry. If not specified, the first geometry column will be used. |
maxBytesBilled
|
Long, default: 100000000000 | Maximum number of bytes billed while processing the query. Any BigQuery job that exceeds this limit will fail and won't be billed. |
Examples
Code Editor (JavaScript)
// Get places from Overture Maps Dataset in BigQuery public data. Map . setCenter ( - 3.69 , 40.41 , 12 ) var mapGeometry = ee . Geometry ( Map . getBounds ( true )). toGeoJSONString (); var sql = "SELECT geometry, names.primary as name, categories.primary as category " + " FROM bigquery-public-data.overture_maps.place " + " WHERE ST_INTERSECTS(geometry, ST_GEOGFROMGEOJSON('" + mapGeometry + "'))" ; var features = ee . FeatureCollection . runBigQuery ({ query : sql , geometryColumn : 'geometry' }); // Display all relevant features on the map. Map . addLayer ( features , { 'color' : 'black' }, 'Places from Overture Maps Dataset' ); // Create a histogram of the categories and print it. var propertyOfInterest = 'category' ; var histogram = features . filter ( ee . Filter . notNull ([ propertyOfInterest ])) . aggregate_histogram ( propertyOfInterest ); print ( histogram ); // Create a frequency chart for the histogram. var categories = histogram . keys (). map ( function ( k ) { return ee . Feature ( null , { key : k , value : histogram . get ( k ) }); }); var sortedCategories = ee . FeatureCollection ( categories ). sort ( 'value' , false ); print ( ui . Chart . feature . byFeature ( sortedCategories ). setChartType ( 'Table' ));
import ee import geemap.core as geemap
Colab (Python)
import json import pandas as pd # Get places from Overture Maps Dataset in BigQuery public data. location = ee . Geometry . Point ( - 3.69 , 40.41 ) map_geometry = json . dumps ( location . buffer ( 5e3 ) . getInfo ()) sql = f """SELECT geometry, names.primary as name, categories.primary as category FROM bigquery-public-data.overture_maps.place WHERE ST_INTERSECTS(geometry, ST_GEOGFROMGEOJSON(' { map_geometry } '))""" features = ee . FeatureCollection . runBigQuery ( query = sql , geometryColumn = "geometry" ) # Display all relevant features on the map. m = geemap . Map () m . center_object ( location , 13 ) m . add_layer ( features , { 'color' : 'black' }, 'Places from Overture Maps Dataset' ) display ( m ) # Create a histogram of the place categories. property_of_interest = 'category' histogram = ( features . filter ( ee . Filter . notNull ([ property_of_interest ]) ) . aggregate_histogram ( property_of_interest ) ) . getInfo () # Display the histogram as a pandas DataFrame. df = pd . DataFrame ( list ( histogram . items ()), columns = [ 'category' , 'frequency' ]) df = df . sort_values ( by = [ 'frequency' ], ascending = False , ignore_index = True ) display ( df )
