[null,null,["最后更新时间 (UTC):2025-07-25。"],[[["\u003cp\u003eCollections in Earth Engine can be joined based on spatial relationships like proximity or intersection using the \u003ccode\u003eee.Join\u003c/code\u003e module.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003ewithinDistance()\u003c/code\u003e filter joins features within a specified distance, while the \u003ccode\u003eintersects()\u003c/code\u003e filter joins features that overlap geographically.\u003c/p\u003e\n"],["\u003cp\u003eSpatial joins can be used for various analyses, such as finding power plants within a certain distance of a park or counting power plants within each state boundary.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003esaveAll()\u003c/code\u003e join method creates a new property containing a list of all matching features from the secondary collection, enabling further analysis and visualization.\u003c/p\u003e\n"]]],[],null,["# Spatial Joins\n\nCollections can be joined by spatial location as well as by property values. To join based\non spatial location, use a `withinDistance()` filter with `.geo` join\nfields specified. The `.geo` field indicates that the item's\ngeometry is to be used to compute the distance metric. For example, consider the task of\nfinding all\n[power plants](https://developers.google.com/earth-engine/datasets/catalog/WRI_GPPD_power_plants) within 100 kilometers\nof Yosemite National Park, USA. For that purpose, use a filter on the geometry\nfields, with the maximum distance set to 100 kilometers using the `distance`\nparameter:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a primary collection: protected areas (Yosemite National Park).\nvar primary = ee.FeatureCollection(\"WCMC/WDPA/current/polygons\")\n .filter(ee.Filter.eq('NAME', 'Yosemite National Park'));\n\n// Load a secondary collection: power plants.\nvar powerPlants = ee.FeatureCollection('WRI/GPPD/power_plants');\n\n// Define a spatial filter, with distance 100 km.\nvar distFilter = ee.Filter.withinDistance({\n distance: 100000,\n leftField: '.geo',\n rightField: '.geo',\n maxError: 10\n});\n\n// Define a saveAll join.\nvar distSaveAll = ee.Join.saveAll({\n matchesKey: 'points',\n measureKey: 'distance'\n});\n\n// Apply the join.\nvar spatialJoined = distSaveAll.apply(primary, powerPlants, distFilter);\n\n// Print the result.\nprint(spatialJoined);\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 primary collection: protected areas (Yosemite National Park).\nprimary = ee.FeatureCollection('WCMC/WDPA/current/polygons').filter(\n ee.Filter.eq('NAME', 'Yosemite National Park')\n)\n\n# Load a secondary collection: power plants.\npower_plants = ee.FeatureCollection('WRI/GPPD/power_plants')\n\n# Define a spatial filter, with distance 100 km.\ndist_filter = ee.Filter.withinDistance(\n distance=100000, leftField='.geo', rightField='.geo', maxError=10\n)\n\n# Define a saveAll join.\ndist_save_all = ee.Join.saveAll(matchesKey='points', measureKey='distance')\n\n# Apply the join.\nspatial_joined = dist_save_all.apply(primary, power_plants, dist_filter)\n\n# Print the result.\ndisplay(spatial_joined)\n```\n\nNote that the previous example joins a `FeatureCollection` to another\n`FeatureCollection`. The `saveAll()` join sets a property\n(`points`) on each feature in the `primary` collection which\nstores a list of the points within 100 km of the feature. The distance of each point to\nthe feature is stored in the `distance` property of each joined point.\n\nSpatial joins can also be used to identify which features\nin one collection intersect those in another. For example, consider two feature\ncollections: a `primary` collection containing polygons representing the\nboundaries of US states, a `secondary` collection containing point locations\nrepresenting power plants. Suppose there is need to determine the number intersecting each\nstate. This can be accomplished with a spatial join as follows:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load the primary collection: US state boundaries.\nvar states = ee.FeatureCollection('TIGER/2018/States');\n\n// Load the secondary collection: power plants.\nvar powerPlants = ee.FeatureCollection('WRI/GPPD/power_plants');\n\n// Define a spatial filter as geometries that intersect.\nvar spatialFilter = ee.Filter.intersects({\n leftField: '.geo',\n rightField: '.geo',\n maxError: 10\n});\n\n// Define a save all join.\nvar saveAllJoin = ee.Join.saveAll({\n matchesKey: 'power_plants',\n});\n\n// Apply the join.\nvar intersectJoined = saveAllJoin.apply(states, powerPlants, spatialFilter);\n\n// Add power plant count per state as a property.\nintersectJoined = intersectJoined.map(function(state) {\n // Get \"power_plant\" intersection list, count how many intersected this state.\n var nPowerPlants = ee.List(state.get('power_plants')).size();\n // Return the state feature with a new property: power plant count.\n return state.set('n_power_plants', nPowerPlants);\n});\n\n// Make a bar chart for the number of power plants per state.\nvar chart = ui.Chart.feature.byFeature(intersectJoined, 'NAME', 'n_power_plants')\n .setChartType('ColumnChart')\n .setSeriesNames({n_power_plants: 'Power plants'})\n .setOptions({\n title: 'Power plants per state',\n hAxis: {title: 'State'},\n vAxis: {title: 'Frequency'}});\n\n// Print the chart to the console.\nprint(chart);\n```\n\nIn the previous example, note that the `intersects()` filter doesn't store\na distance as the `withinDistance()` filter does. The output should look\nsomething like Figure 1.\nFigure 1. Bar chart showing the number of power plants intersecting each US state."]]