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Elastic 文档 ›Elasticsearch 指南 [8.17] ›聚合 ›指标聚合

地理质心聚合

编辑

地理质心聚合

编辑

一种度量聚合，它计算地理字段的所有坐标值的加权质心。

示例

resp = client.indices.create(
    index="museums",
    mappings={
        "properties": {
            "location": {
                "type": "geo_point"
            }
        }
    },
)
print(resp)

resp1 = client.bulk(
    index="museums",
    refresh=True,
    operations=[
        {
            "index": {
                "_id": 1
            }
        },
        {
            "location": "POINT (4.912350 52.374081)",
            "city": "Amsterdam",
            "name": "NEMO Science Museum"
        },
        {
            "index": {
                "_id": 2
            }
        },
        {
            "location": "POINT (4.901618 52.369219)",
            "city": "Amsterdam",
            "name": "Museum Het Rembrandthuis"
        },
        {
            "index": {
                "_id": 3
            }
        },
        {
            "location": "POINT (4.914722 52.371667)",
            "city": "Amsterdam",
            "name": "Nederlands Scheepvaartmuseum"
        },
        {
            "index": {
                "_id": 4
            }
        },
        {
            "location": "POINT (4.405200 51.222900)",
            "city": "Antwerp",
            "name": "Letterenhuis"
        },
        {
            "index": {
                "_id": 5
            }
        },
        {
            "location": "POINT (2.336389 48.861111)",
            "city": "Paris",
            "name": "Musée du Louvre"
        },
        {
            "index": {
                "_id": 6
            }
        },
        {
            "location": "POINT (2.327000 48.860000)",
            "city": "Paris",
            "name": "Musée d'Orsay"
        }
    ],
)
print(resp1)

resp2 = client.search(
    index="museums",
    size="0",
    aggs={
        "centroid": {
            "geo_centroid": {
                "field": "location"
            }
        }
    },
)
print(resp2)
response = client.indices.create(
  index: 'museums',
  body: {
    mappings: {
      properties: {
        location: {
          type: 'geo_point'
        }
      }
    }
  }
)
puts response

response = client.bulk(
  index: 'museums',
  refresh: true,
  body: [
    {
      index: {
        _id: 1
      }
    },
    {
      location: 'POINT (4.912350 52.374081)',
      city: 'Amsterdam',
      name: 'NEMO Science Museum'
    },
    {
      index: {
        _id: 2
      }
    },
    {
      location: 'POINT (4.901618 52.369219)',
      city: 'Amsterdam',
      name: 'Museum Het Rembrandthuis'
    },
    {
      index: {
        _id: 3
      }
    },
    {
      location: 'POINT (4.914722 52.371667)',
      city: 'Amsterdam',
      name: 'Nederlands Scheepvaartmuseum'
    },
    {
      index: {
        _id: 4
      }
    },
    {
      location: 'POINT (4.405200 51.222900)',
      city: 'Antwerp',
      name: 'Letterenhuis'
    },
    {
      index: {
        _id: 5
      }
    },
    {
      location: 'POINT (2.336389 48.861111)',
      city: 'Paris',
      name: 'Musée du Louvre'
    },
    {
      index: {
        _id: 6
      }
    },
    {
      location: 'POINT (2.327000 48.860000)',
      city: 'Paris',
      name: "Musée d'Orsay"
    }
  ]
)
puts response

response = client.search(
  index: 'museums',
  size: 0,
  body: {
    aggregations: {
      centroid: {
        geo_centroid: {
          field: 'location'
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "museums",
  mappings: {
    properties: {
      location: {
        type: "geo_point",
      },
    },
  },
});
console.log(response);

const response1 = await client.bulk({
  index: "museums",
  refresh: "true",
  operations: [
    {
      index: {
        _id: 1,
      },
    },
    {
      location: "POINT (4.912350 52.374081)",
      city: "Amsterdam",
      name: "NEMO Science Museum",
    },
    {
      index: {
        _id: 2,
      },
    },
    {
      location: "POINT (4.901618 52.369219)",
      city: "Amsterdam",
      name: "Museum Het Rembrandthuis",
    },
    {
      index: {
        _id: 3,
      },
    },
    {
      location: "POINT (4.914722 52.371667)",
      city: "Amsterdam",
      name: "Nederlands Scheepvaartmuseum",
    },
    {
      index: {
        _id: 4,
      },
    },
    {
      location: "POINT (4.405200 51.222900)",
      city: "Antwerp",
      name: "Letterenhuis",
    },
    {
      index: {
        _id: 5,
      },
    },
    {
      location: "POINT (2.336389 48.861111)",
      city: "Paris",
      name: "Musée du Louvre",
    },
    {
      index: {
        _id: 6,
      },
    },
    {
      location: "POINT (2.327000 48.860000)",
      city: "Paris",
      name: "Musée d'Orsay",
    },
  ],
});
console.log(response1);

const response2 = await client.search({
  index: "museums",
  size: 0,
  aggs: {
    centroid: {
      geo_centroid: {
        field: "location",
      },
    },
  },
});
console.log(response2);
PUT /museums
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_point"
      }
    }
  }
}

POST /museums/_bulk?refresh
{"index":{"_id":1}}
{"location": "POINT (4.912350 52.374081)", "city": "Amsterdam", "name": "NEMO Science Museum"}
{"index":{"_id":2}}
{"location": "POINT (4.901618 52.369219)", "city": "Amsterdam", "name": "Museum Het Rembrandthuis"}
{"index":{"_id":3}}
{"location": "POINT (4.914722 52.371667)", "city": "Amsterdam", "name": "Nederlands Scheepvaartmuseum"}
{"index":{"_id":4}}
{"location": "POINT (4.405200 51.222900)", "city": "Antwerp", "name": "Letterenhuis"}
{"index":{"_id":5}}
{"location": "POINT (2.336389 48.861111)", "city": "Paris", "name": "Musée du Louvre"}
{"index":{"_id":6}}
{"location": "POINT (2.327000 48.860000)", "city": "Paris", "name": "Musée d'Orsay"}

POST /museums/_search?size=0
{
  "aggs": {
    "centroid": {
      "geo_centroid": {
        "field": "location" 
      }
    }
  }
}
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geo_centroid 聚合指定用于计算质心的字段。（注意：字段必须是 地理点 类型）

上面的聚合演示了如何计算所有博物馆文档的 location 字段的质心。

以上聚合的响应

{
  ...
  "aggregations": {
    "centroid": {
      "location": {
        "lat": 51.00982965203002,
        "lon": 3.9662131341174245
      },
      "count": 6
    }
  }
}

当与作为其他桶聚合的子聚合组合时，geo_centroid 聚合更有趣。

示例

resp = client.search(
    index="museums",
    size="0",
    aggs={
        "cities": {
            "terms": {
                "field": "city.keyword"
            },
            "aggs": {
                "centroid": {
                    "geo_centroid": {
                        "field": "location"
                    }
                }
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'museums',
  size: 0,
  body: {
    aggregations: {
      cities: {
        terms: {
          field: 'city.keyword'
        },
        aggregations: {
          centroid: {
            geo_centroid: {
              field: 'location'
            }
          }
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "museums",
  size: 0,
  aggs: {
    cities: {
      terms: {
        field: "city.keyword",
      },
      aggs: {
        centroid: {
          geo_centroid: {
            field: "location",
          },
        },
      },
    },
  },
});
console.log(response);
POST /museums/_search?size=0
{
  "aggs": {
    "cities": {
      "terms": { "field": "city.keyword" },
      "aggs": {
        "centroid": {
          "geo_centroid": { "field": "location" }
        }
      }
    }
  }
}
Copy as curlTry in Elastic 

上面的示例使用 geo_centroid 作为 词项 桶聚合的子聚合，用于查找每个城市中博物馆的中心位置。

以上聚合的响应

{
  ...
  "aggregations": {
    "cities": {
      "sum_other_doc_count": 0,
      "doc_count_error_upper_bound": 0,
      "buckets": [
        {
          "key": "Amsterdam",
          "doc_count": 3,
          "centroid": {
            "location": {
              "lat": 52.371655656024814,
              "lon": 4.909563297405839
            },
            "count": 3
          }
        },
        {
          "key": "Paris",
          "doc_count": 2,
          "centroid": {
            "location": {
              "lat": 48.86055548675358,
              "lon": 2.3316944623366
            },
            "count": 2
          }
        },
        {
          "key": "Antwerp",
          "doc_count": 1,
          "centroid": {
            "location": {
              "lat": 51.22289997059852,
              "lon": 4.40519998781383
            },
            "count": 1
          }
        }
      ]
    }
  }
}

geo_shape 字段上的地理质心聚合

编辑

地理形状的质心度量比点的质心度量更精细。包含形状的特定聚合桶的质心是桶中最高维度形状类型的质心。例如，如果一个桶包含由多边形和线组成的形状，则线不参与质心度量。每种形状类型的质心计算方式都不同。通过 圆形 摄取的包络和圆形被视为多边形。

几何类型 质心计算

[多]点

所有坐标的等权重平均值

[多]线串

每个线段的质心的加权平均值，其中每个线段的权重是其以度为单位的长度

[多]边形

一个多边形的所有三角形质心的加权平均值，其中三角形由每两个连续顶点和起点形成。孔具有负权重。权重表示计算出的三角形的面积，单位为 deg^2

几何集合

所有具有最高维度的底层几何图形的质心。如果是多边形、直线和/或点，则忽略直线和/或点。如果是直线和点，则忽略点

示例

resp = client.indices.create(
    index="places",
    mappings={
        "properties": {
            "geometry": {
                "type": "geo_shape"
            }
        }
    },
)
print(resp)

resp1 = client.bulk(
    index="places",
    refresh=True,
    operations=[
        {
            "index": {
                "_id": 1
            }
        },
        {
            "name": "NEMO Science Museum",
            "geometry": "POINT(4.912350 52.374081)"
        },
        {
            "index": {
                "_id": 2
            }
        },
        {
            "name": "Sportpark De Weeren",
            "geometry": {
                "type": "Polygon",
                "coordinates": [
                    [
                        [
                            4.965305328369141,
                            52.39347642069457
                        ],
                        [
                            4.966979026794433,
                            52.391721758934835
                        ],
                        [
                            4.969425201416015,
                            52.39238958618537
                        ],
                        [
                            4.967944622039794,
                            52.39420969150824
                        ],
                        [
                            4.965305328369141,
                            52.39347642069457
                        ]
                    ]
                ]
            }
        }
    ],
)
print(resp1)

resp2 = client.search(
    index="places",
    size="0",
    aggs={
        "centroid": {
            "geo_centroid": {
                "field": "geometry"
            }
        }
    },
)
print(resp2)
response = client.indices.create(
  index: 'places',
  body: {
    mappings: {
      properties: {
        geometry: {
          type: 'geo_shape'
        }
      }
    }
  }
)
puts response

response = client.bulk(
  index: 'places',
  refresh: true,
  body: [
    {
      index: {
        _id: 1
      }
    },
    {
      name: 'NEMO Science Museum',
      geometry: 'POINT(4.912350 52.374081)'
    },
    {
      index: {
        _id: 2
      }
    },
    {
      name: 'Sportpark De Weeren',
      geometry: {
        type: 'Polygon',
        coordinates: [
          [
            [
              4.965305328369141,
              52.39347642069457
            ],
            [
              4.966979026794433,
              52.391721758934835
            ],
            [
              4.969425201416015,
              52.39238958618537
            ],
            [
              4.967944622039794,
              52.39420969150824
            ],
            [
              4.965305328369141,
              52.39347642069457
            ]
          ]
        ]
      }
    }
  ]
)
puts response

response = client.search(
  index: 'places',
  size: 0,
  body: {
    aggregations: {
      centroid: {
        geo_centroid: {
          field: 'geometry'
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "places",
  mappings: {
    properties: {
      geometry: {
        type: "geo_shape",
      },
    },
  },
});
console.log(response);

const response1 = await client.bulk({
  index: "places",
  refresh: "true",
  operations: [
    {
      index: {
        _id: 1,
      },
    },
    {
      name: "NEMO Science Museum",
      geometry: "POINT(4.912350 52.374081)",
    },
    {
      index: {
        _id: 2,
      },
    },
    {
      name: "Sportpark De Weeren",
      geometry: {
        type: "Polygon",
        coordinates: [
          [
            [4.965305328369141, 52.39347642069457],
            [4.966979026794433, 52.391721758934835],
            [4.969425201416015, 52.39238958618537],
            [4.967944622039794, 52.39420969150824],
            [4.965305328369141, 52.39347642069457],
          ],
        ],
      },
    },
  ],
});
console.log(response1);

const response2 = await client.search({
  index: "places",
  size: 0,
  aggs: {
    centroid: {
      geo_centroid: {
        field: "geometry",
      },
    },
  },
});
console.log(response2);
PUT /places
{
  "mappings": {
    "properties": {
      "geometry": {
        "type": "geo_shape"
      }
    }
  }
}

POST /places/_bulk?refresh
{"index":{"_id":1}}
{"name": "NEMO Science Museum", "geometry": "POINT(4.912350 52.374081)" }
{"index":{"_id":2}}
{"name": "Sportpark De Weeren", "geometry": { "type": "Polygon", "coordinates": [ [ [ 4.965305328369141, 52.39347642069457 ], [ 4.966979026794433, 52.391721758934835 ], [ 4.969425201416015, 52.39238958618537 ], [ 4.967944622039794, 52.39420969150824 ], [ 4.965305328369141, 52.39347642069457 ] ] ] } }

POST /places/_search?size=0
{
  "aggs": {
    "centroid": {
      "geo_centroid": {
        "field": "geometry"
      }
    }
  }
}
Copy as curlTry in Elastic 
{
  ...
  "aggregations": {
    "centroid": {
      "location": {
        "lat": 52.39296147599816,
        "lon": 4.967404240742326
      },
      "count": 2
    }
  }
}

将 geo_centroid 用作 geohash_grid 的子聚合

geohash_grid 聚合将文档（而不是单个地理点）放入桶中。如果文档的 geo_point 字段包含多个值，则该文档可能会被分配到多个桶，即使其一个或多个地理点在桶边界之外。

如果还使用了 geocentroid 子聚合，则每个质心都使用桶中的所有地理点计算，包括桶边界之外的地理点。这可能会导致质心位于桶边界之外。

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