צמצום של FeatureCollection

כדי לצבור נתונים בנכסים של FeatureCollection, משתמשים ב-featureCollection.reduceColumns(). לדוגמה, כדי לבדוק את מאפייני האזורים בקטעי הניקוז FeatureCollection, הקוד הזה מחשב את השגיאה הריבונית הממוצעת (RMSE) ביחס לאזור שחושב ב-Earth Engine:

Code Editor‏ (JavaScript)

// Load watersheds from a data table and filter to the continental US.
var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
  .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29));

// This function computes the squared difference between an area property
// and area computed directly from the feature's geometry.
var areaDiff = function(feature) {
  // Compute area in sq. km directly from the geometry.
  var area = feature.geometry().area().divide(1000 * 1000);
  // Compute the difference between computed area and the area property.
  var diff = area.subtract(ee.Number.parse(feature.get('areasqkm')));
  // Return the feature with the squared difference set to the 'diff' property.
  return feature.set('diff', diff.pow(2));
};

// Calculate RMSE for population of difference pairs.
var rmse = ee.Number(
  // Map the difference function over the collection.
  sheds.map(areaDiff)
  // Reduce to get the mean squared difference.
  .reduceColumns(ee.Reducer.mean(), ['diff'])
  .get('mean')
)
// Compute the square root of the mean square to get RMSE.
.sqrt();

// Print the result.
print('RMSE=', rmse);

הגדרת Python

בדף סביבת Python מפורט מידע על Python API ועל השימוש ב-geemap לפיתוח אינטראקטיבי.

import ee
import geemap.core as geemap

Colab (Python)

# Load watersheds from a data table and filter to the continental US.
sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds(
    ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)
)

# This function computes the squared difference between an area property
# and area computed directly from the feature's geometry.
def area_diff(feature):
  # Compute area in sq. km directly from the geometry.
  area = feature.geometry().area().divide(1000 * 1000)
  # Compute the difference between computed area and the area property.
  diff = area.subtract(ee.Number.parse(feature.get('areasqkm')))
  # Return the feature with the squared difference set to the 'diff' property.
  return feature.set('diff', diff.pow(2))

# Calculate RMSE for population of difference pairs.
rmse = (
    ee.Number(
        # Map the difference function over the collection.
        sheds.map(area_diff)
        # Reduce to get the mean squared difference.
        .reduceColumns(ee.Reducer.mean(), ['diff']).get('mean')
    )
    # Compute the square root of the mean square to get RMSE.
    .sqrt()
)

# Print the result.
display('RMSE=', rmse)

בדוגמה הזו, שימו לב שהערך המוחזר של reduceColumns() הוא מילון עם המפתח ‘mean’. כדי לקבל את הממוצע, צריך להמיר את התוצאה של dictionary.get() למספר באמצעות ee.Number() לפני שמנסים להפעיל עליה את sqrt(). למידע נוסף על מבני נתונים משניים ב-Earth Engine, אפשר לעיין במדריך הזה.

כדי להציג שכבות-על של תכונות בתמונות, משתמשים ב-featureCollection.reduceRegions(). לדוגמה, כדי לחשב את נפח המשקעים באזורי אגן הניקוז של ארה"ב, צריך להשתמש ב-reduceRegions() ואז ב-map():

Code Editor‏ (JavaScript)

// Load an image of daily precipitation in mm/day.
var precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first());

// Load watersheds from a data table and filter to the continental US.
var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
  .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29));

// Add the mean of each image as new properties of each feature.
var withPrecip = precip.reduceRegions(sheds, ee.Reducer.mean())
  .filter(ee.Filter.notNull(['prcp']));

// This function computes total rainfall in cubic meters.
var prcpVolume = function(feature) {
  // Precipitation in mm/day -> meters -> sq. meters.
  var volume = ee.Number(feature.get('prcp'))
    .divide(1000).multiply(feature.geometry().area());
  return feature.set('volume', volume);
};

var highVolume = withPrecip
  // Map the function over the collection.
  .map(prcpVolume)
  // Sort descending.
  .sort('volume', false)
  // Get only the 5 highest volume watersheds.
  .limit(5)
  // Extract the names to a list.
  .reduceColumns(ee.Reducer.toList(), ['name']).get('list');

// Print the resulting FeatureCollection.
print(highVolume);

הגדרת Python

בדף סביבת Python מפורט מידע על Python API ועל השימוש ב-geemap לפיתוח אינטראקטיבי.

import ee
import geemap.core as geemap

Colab (Python)

# Load an image of daily precipitation in mm/day.
precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first())

# Load watersheds from a data table and filter to the continental US.
sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds(
    ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)
)

# Add the mean of each image as new properties of each feature.
with_precip = precip.reduceRegions(sheds, ee.Reducer.mean()).filter(
    ee.Filter.notNull(['prcp'])
)


# This function computes total rainfall in cubic meters.
def prcp_volume(feature):
  # Precipitation in mm/day -> meters -> sq. meters.
  volume = (
      ee.Number(feature.get('prcp'))
      .divide(1000)
      .multiply(feature.geometry().area())
  )
  return feature.set('volume', volume)

high_volume = (
    # Map the function over the collection.
    with_precip.map(prcp_volume)
    # Sort descending and get only the 5 highest volume watersheds.
    .sort('volume', False).limit(5)
    # Extract the names to a list.
    .reduceColumns(ee.Reducer.toList(), ['name']).get('list')
)

# Print the resulting FeatureCollection.
display(high_volume)

למידע נוסף על צמצום אוספים של תכונות, ראו נתונים סטטיסטיים של עמודות FeatureCollection והמרה של וקטור לרצף רסטר.