ee.FeatureCollection.runBigQuery

 // 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 
  
 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 
 )