ee.FeatureCollection.runBigQuery

运行 BigQuery 查询，提取结果并将其显示为 FeatureCollection。

用法返回
ee.FeatureCollection.runBigQuery(query, geometryColumn, maxBytesBilled)FeatureCollection
参数类型详细信息
query字符串要在 BigQuery 资源上执行的 GoogleSQL 查询。
geometryColumn字符串，默认值：null要用作主要地图项几何图形的列的名称。如果未指定，系统会使用第一个几何图形列。
maxBytesBilled长整数，默认值：100000000000处理查询时计费的字节数上限。任何超出此限制的 BigQuery 作业都将失败，并且不会产生费用。

示例

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 设置

如需了解 Python API 以及如何使用 geemap 进行交互式开发，请参阅 Python 环境页面。

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)