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分组的减少次数和区域统计信息
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
您可以使用 reducer.group()
按指定输入的值对缩减器的输出进行分组,从而获取 Image
或 FeatureCollection
的每个区域中的统计信息。例如,如需计算每个州的总人口和住宅单元数量,此示例会按如下方式对人口普查区 FeatureCollection
的缩减输出进行分组:
Code Editor (JavaScript)
// Load a collection of US census blocks.
var blocks = ee.FeatureCollection('TIGER/2010/Blocks');
// Compute sums of the specified properties, grouped by state code.
var sums = blocks
.filter(ee.Filter.and(
ee.Filter.neq('pop10', null),
ee.Filter.neq('housing10', null)))
.reduceColumns({
selectors: ['pop10', 'housing10', 'statefp10'],
reducer: ee.Reducer.sum().repeat(2).group({
groupField: 2,
groupName: 'state-code',
})
});
// Print the resultant Dictionary.
print(sums);
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)
# Load a collection of US census blocks.
blocks = ee.FeatureCollection('TIGER/2010/Blocks')
# Compute sums of the specified properties, grouped by state code.
sums = blocks.filter(
ee.Filter.And(
ee.Filter.neq('pop10', None), ee.Filter.neq('housing10', None)
)
).reduceColumns(
selectors=['pop10', 'housing10', 'statefp10'],
reducer=ee.Reducer.sum()
.repeat(2)
.group(groupField=2, groupName='state-code'),
)
# Print the resultant Dictionary.
display(sums)
groupField
参数是选择器数组中输入的索引,其中包含用于分组的代码;groupName
参数指定用于存储分组变量值的属性的名称。由于不会针对每个输入自动重复 reducer,因此需要进行 repeat(2)
调用。
如需对 image.reduceRegions()
的输出进行分组,您可以指定一个分组带,以按整数像素值定义组。这种类型的计算有时称为“区域统计信息”,其中区域被指定为分组带,统计信息由缩减器确定。在以下示例中,美国夜间灯光的变化按土地覆盖类别分组:
Code Editor (JavaScript)
// Load a region representing the United States
var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')
.filter(ee.Filter.eq('country_na', 'United States'));
// Load MODIS land cover categories in 2001.
var landcover = ee.Image('MODIS/051/MCD12Q1/2001_01_01')
// Select the IGBP classification band.
.select('Land_Cover_Type_1');
// Load nightlights image inputs.
var nl2001 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F152001')
.select('stable_lights');
var nl2012 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012')
.select('stable_lights');
// Compute the nightlights decadal difference, add land cover codes.
var nlDiff = nl2012.subtract(nl2001).addBands(landcover);
// Grouped a mean reducer: change of nightlights by land cover category.
var means = nlDiff.reduceRegion({
reducer: ee.Reducer.mean().group({
groupField: 1,
groupName: 'code',
}),
geometry: region.geometry(),
scale: 1000,
maxPixels: 1e8
});
// Print the resultant Dictionary.
print(means);
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)
# Load a region representing the United States
region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filter(
ee.Filter.eq('country_na', 'United States')
)
# Load MODIS land cover categories in 2001.
landcover = ee.Image('MODIS/051/MCD12Q1/2001_01_01').select(
# Select the IGBP classification band.
'Land_Cover_Type_1'
)
# Load nightlights image inputs.
nl_2001 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F152001').select(
'stable_lights'
)
nl_2012 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012').select(
'stable_lights'
)
# Compute the nightlights decadal difference, add land cover codes.
nl_diff = nl_2012.subtract(nl_2001).addBands(landcover)
# Grouped a mean reducer: change of nightlights by land cover category.
means = nl_diff.reduceRegion(
reducer=ee.Reducer.mean().group(groupField=1, groupName='code'),
geometry=region.geometry(),
scale=1000,
maxPixels=1e8,
)
# Print the resultant Dictionary.
display(means)
请注意,在此示例中,groupField
是包含用于对输出进行分组的区域的频段的索引。第一个频段的索引为 0,第二个频段的索引为 1,以此类推。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-25。
[null,null,["最后更新时间 (UTC):2025-07-25。"],[[["\u003cp\u003eUse \u003ccode\u003ereducer.group()\u003c/code\u003e with \u003ccode\u003ereduceColumns()\u003c/code\u003e on \u003ccode\u003eFeatureCollection\u003c/code\u003e to compute statistics for groups based on a property, like calculating total population per state.\u003c/p\u003e\n"],["\u003cp\u003eUtilize \u003ccode\u003ereducer.group()\u003c/code\u003e with \u003ccode\u003ereduceRegion()\u003c/code\u003e on \u003ccode\u003eImage\u003c/code\u003e to compute zonal statistics, such as averaging nightlight changes within different land cover categories.\u003c/p\u003e\n"],["\u003cp\u003eSpecify the \u003ccode\u003egroupField\u003c/code\u003e argument in \u003ccode\u003ereducer.group()\u003c/code\u003e as the index of the selector or band containing the grouping categories.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003egroupName\u003c/code\u003e argument in \u003ccode\u003ereducer.group()\u003c/code\u003e determines the name of the property storing the grouping variable's value in the output dictionary.\u003c/p\u003e\n"],["\u003cp\u003eRemember to use \u003ccode\u003erepeat()\u003c/code\u003e with \u003ccode\u003ereduceColumns()\u003c/code\u003e when applying multiple reducers, ensuring calculations are performed for each selected property.\u003c/p\u003e\n"]]],[],null,["# Grouped Reductions and Zonal Statistics\n\nYou can get statistics in each zone of an `Image` or\n`FeatureCollection` by using `reducer.group()` to group the output\nof a reducer by the value of a specified input. For example, to compute the total\npopulation and number of housing units in each state, this example groups the output of\na reduction of a census block `FeatureCollection` as follows:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a collection of US census blocks.\nvar blocks = ee.FeatureCollection('TIGER/2010/Blocks');\n\n// Compute sums of the specified properties, grouped by state code.\nvar sums = blocks\n .filter(ee.Filter.and(\n ee.Filter.neq('pop10', null),\n ee.Filter.neq('housing10', null)))\n .reduceColumns({\n selectors: ['pop10', 'housing10', 'statefp10'],\n reducer: ee.Reducer.sum().repeat(2).group({\n groupField: 2,\n groupName: 'state-code',\n })\n});\n\n// Print the resultant Dictionary.\nprint(sums);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load a collection of US census blocks.\nblocks = ee.FeatureCollection('TIGER/2010/Blocks')\n\n# Compute sums of the specified properties, grouped by state code.\nsums = blocks.filter(\n ee.Filter.And(\n ee.Filter.neq('pop10', None), ee.Filter.neq('housing10', None)\n )\n).reduceColumns(\n selectors=['pop10', 'housing10', 'statefp10'],\n reducer=ee.Reducer.sum()\n .repeat(2)\n .group(groupField=2, groupName='state-code'),\n)\n\n# Print the resultant Dictionary.\ndisplay(sums)\n```\n\nThe `groupField` argument is the index of the input in the selectors array\nthat contains the codes by which to group, the `groupName` argument specifies\nthe name of the property to store the value of the grouping variable. Since the reducer\nis not automatically repeated for each input, the `repeat(2)` call is needed.\n\nTo group output of `image.reduceRegions()` you can specify a grouping band\nthat defines groups by integer pixel values. This type of computation is sometimes called\n\"zonal statistics\" where the zones are specified as the grouping band and the statistic\nis determined by the reducer. In the following example, change in nightlights in the\nUnited States is grouped by land cover category:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a region representing the United States\nvar region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')\n .filter(ee.Filter.eq('country_na', 'United States'));\n\n// Load MODIS land cover categories in 2001.\nvar landcover = ee.Image('MODIS/051/MCD12Q1/2001_01_01')\n // Select the IGBP classification band.\n .select('Land_Cover_Type_1');\n\n// Load nightlights image inputs.\nvar nl2001 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F152001')\n .select('stable_lights');\nvar nl2012 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012')\n .select('stable_lights');\n\n// Compute the nightlights decadal difference, add land cover codes.\nvar nlDiff = nl2012.subtract(nl2001).addBands(landcover);\n\n// Grouped a mean reducer: change of nightlights by land cover category.\nvar means = nlDiff.reduceRegion({\n reducer: ee.Reducer.mean().group({\n groupField: 1,\n groupName: 'code',\n }),\n geometry: region.geometry(),\n scale: 1000,\n maxPixels: 1e8\n});\n\n// Print the resultant Dictionary.\nprint(means);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load a region representing the United States\nregion = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filter(\n ee.Filter.eq('country_na', 'United States')\n)\n\n# Load MODIS land cover categories in 2001.\nlandcover = ee.Image('MODIS/051/MCD12Q1/2001_01_01').select(\n # Select the IGBP classification band.\n 'Land_Cover_Type_1'\n)\n\n# Load nightlights image inputs.\nnl_2001 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F152001').select(\n 'stable_lights'\n)\nnl_2012 = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012').select(\n 'stable_lights'\n)\n\n# Compute the nightlights decadal difference, add land cover codes.\nnl_diff = nl_2012.subtract(nl_2001).addBands(landcover)\n\n# Grouped a mean reducer: change of nightlights by land cover category.\nmeans = nl_diff.reduceRegion(\n reducer=ee.Reducer.mean().group(groupField=1, groupName='code'),\n geometry=region.geometry(),\n scale=1000,\n maxPixels=1e8,\n)\n\n# Print the resultant Dictionary.\ndisplay(means)\n```\n\nNote that in this example, the `groupField` is the index of the band\ncontaining the zones by which to group the output. The first band is index 0, the second\nis index 1, etc."]]