Filtering a FeatureCollection

  • Filtering a FeatureCollection is similar to filtering an ImageCollection and can be done using filterDate(), filterBounds(), or the general filter() method with an ee.Filter.

  • The provided code examples demonstrate filtering a FeatureCollection first by geographical boundaries (continental US) and then by a numerical property (areasqkm) to select larger watersheds.

  • The examples show how to load a FeatureCollection, define a geometry for spatial filtering, and apply filterBounds() and filter() methods in both JavaScript and Python.

Filtering a FeatureCollection is analogous to filtering an ImageCollection. (See the Filtering an ImageCollection section). There are the featureCollection.filterDate(), and featureCollection.filterBounds() convenience methods and the featureCollection.filter() method for use with any applicable ee.Filter. For example:

Code Editor (JavaScript)

// Load watersheds from a data table.
var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
  // Convert 'areasqkm' property from string to number.
  .map(function(feature){
    var num = ee.Number.parse(feature.get('areasqkm'));
    return feature.set('areasqkm', num);
  });

// Define a region roughly covering the continental US.
var continentalUS = ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29);

// Filter the table geographically: only watersheds in the continental US.
var filtered = sheds.filterBounds(continentalUS);

// Check the number of watersheds after filtering for location.
print('Count after filter:', filtered.size());

// Filter to get only larger continental US watersheds.
var largeSheds = filtered.filter(ee.Filter.gt('areasqkm', 25000));

// Check the number of watersheds after filtering for size and location.
print('Count after filtering by size:', largeSheds.size());

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)

# Load watersheds from a data table.
sheds = (
    ee.FeatureCollection('USGS/WBD/2017/HUC06')
    # Convert 'areasqkm' property from string to number.
    .map(
        lambda feature: feature.set(
            'areasqkm', ee.Number.parse(feature.get('areasqkm'))
        )
    )
)

# Define a region roughly covering the continental US.
continental_us = ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)

# Filter the table geographically: only watersheds in the continental US.
filtered = sheds.filterBounds(continental_us)

# Check the number of watersheds after filtering for location.
display('Count after filter:', filtered.size())

# Filter to get only larger continental US watersheds.
large_sheds = filtered.filter(ee.Filter.gt('areasqkm', 25000))

# Check the number of watersheds after filtering for size and location.
display('Count after filtering by size:', large_sheds.size())