ee.FeatureCollection.runBigQuery

AI-generated Key Takeaways

• The ee.FeatureCollection.runBigQuery function executes a BigQuery query and returns the results as a FeatureCollection.

• Arguments for the function include the SQL query string, an optional geometryColumn to specify the geometry column, and an optional maxBytesBilled to set a limit on billed bytes.

• The examples demonstrate how to use runBigQuery in both JavaScript and Python to fetch data from BigQuery and visualize or analyze it.

Runs a BigQuery query, fetches the results and presents the them as a FeatureCollection.

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'));

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

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)