New

The executive guide to generative AI

Read more
About usPartnersSupport|Login
« 扩展统计聚合 地理质心聚合 »
Elastic 文档 ›Elasticsearch 指南 [8.17] ›聚合 ›指标聚合

地理边界聚合

编辑

地理边界聚合

编辑

一种指标聚合，用于计算包含 地理坐标点 或 地理形状 字段所有值的地理边界框。

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)",
            "name": "NEMO Science Museum"
        },
        {
            "index": {
                "_id": 2
            }
        },
        {
            "location": "POINT (4.901618 52.369219)",
            "name": "Museum Het Rembrandthuis"
        },
        {
            "index": {
                "_id": 3
            }
        },
        {
            "location": "POINT (4.914722 52.371667)",
            "name": "Nederlands Scheepvaartmuseum"
        },
        {
            "index": {
                "_id": 4
            }
        },
        {
            "location": "POINT (4.405200 51.222900)",
            "name": "Letterenhuis"
        },
        {
            "index": {
                "_id": 5
            }
        },
        {
            "location": "POINT (2.336389 48.861111)",
            "name": "Musée du Louvre"
        },
        {
            "index": {
                "_id": 6
            }
        },
        {
            "location": "POINT (2.327000 48.860000)",
            "name": "Musée d'Orsay"
        }
    ],
)
print(resp1)

resp2 = client.search(
    index="museums",
    size="0",
    query={
        "match": {
            "name": "musée"
        }
    },
    aggs={
        "viewport": {
            "geo_bounds": {
                "field": "location",
                "wrap_longitude": True
            }
        }
    },
)
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)',
      name: 'NEMO Science Museum'
    },
    {
      index: {
        _id: 2
      }
    },
    {
      location: 'POINT (4.901618 52.369219)',
      name: 'Museum Het Rembrandthuis'
    },
    {
      index: {
        _id: 3
      }
    },
    {
      location: 'POINT (4.914722 52.371667)',
      name: 'Nederlands Scheepvaartmuseum'
    },
    {
      index: {
        _id: 4
      }
    },
    {
      location: 'POINT (4.405200 51.222900)',
      name: 'Letterenhuis'
    },
    {
      index: {
        _id: 5
      }
    },
    {
      location: 'POINT (2.336389 48.861111)',
      name: 'Musée du Louvre'
    },
    {
      index: {
        _id: 6
      }
    },
    {
      location: 'POINT (2.327000 48.860000)',
      name: "Musée d'Orsay"
    }
  ]
)
puts response

response = client.search(
  index: 'museums',
  size: 0,
  body: {
    query: {
      match: {
        name: 'musée'
      }
    },
    aggregations: {
      viewport: {
        geo_bounds: {
          field: 'location',
          wrap_longitude: true
        }
      }
    }
  }
)
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)",
      name: "NEMO Science Museum",
    },
    {
      index: {
        _id: 2,
      },
    },
    {
      location: "POINT (4.901618 52.369219)",
      name: "Museum Het Rembrandthuis",
    },
    {
      index: {
        _id: 3,
      },
    },
    {
      location: "POINT (4.914722 52.371667)",
      name: "Nederlands Scheepvaartmuseum",
    },
    {
      index: {
        _id: 4,
      },
    },
    {
      location: "POINT (4.405200 51.222900)",
      name: "Letterenhuis",
    },
    {
      index: {
        _id: 5,
      },
    },
    {
      location: "POINT (2.336389 48.861111)",
      name: "Musée du Louvre",
    },
    {
      index: {
        _id: 6,
      },
    },
    {
      location: "POINT (2.327000 48.860000)",
      name: "Musée d'Orsay",
    },
  ],
});
console.log(response1);

const response2 = await client.search({
  index: "museums",
  size: 0,
  query: {
    match: {
      name: "musée",
    },
  },
  aggs: {
    viewport: {
      geo_bounds: {
        field: "location",
        wrap_longitude: true,
      },
    },
  },
});
console.log(response2);
PUT /museums
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_point"
      }
    }
  }
}

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

POST /museums/_search?size=0
{
  "query": {
    "match": { "name": "musée" }
  },
  "aggs": {
    "viewport": {
      "geo_bounds": {
        "field": "location",    
        "wrap_longitude": true  
      }
    }
  }
}
Copy as curlTry in Elastic 

geo_bounds 聚合指定用于获取边界的字段，该字段必须是 地理坐标点 或 地理形状 类型。

wrap_longitude 是一个可选参数，用于指定是否允许边界框与国际日期变更线重叠。默认值为 true。

上面的聚合演示了如何计算名称与 “musée” 匹配的所有文档的 location 字段的边界框。

上面聚合的响应

{
  ...
  "aggregations": {
    "viewport": {
      "bounds": {
        "top_left": {
          "lat": 48.86111099738628,
          "lon": 2.3269999679178
        },
        "bottom_right": {
          "lat": 48.85999997612089,
          "lon": 2.3363889567553997
        }
      }
    }
  }
}

对 geo_shape 字段进行地理边界聚合

编辑

地理边界聚合也支持 geo_shape 字段。

如果 wrap_longitude 设置为 true （默认值），则边界框可以与国际日期变更线重叠，并返回 top_left 经度大于 top_right 经度的边界。

例如，地理边界框的右上角经度通常会大于左下角经度。但是，当区域跨越 180° 子午线时，左下角经度的值将大于右上角经度的值。有关更多信息，请参阅开放地理空间联盟网站上的 地理边界框。

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={
        "viewport": {
            "geo_bounds": {
                "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: {
      viewport: {
        geo_bounds: {
          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: {
    viewport: {
      geo_bounds: {
        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": {
    "viewport": {
      "geo_bounds": {
        "field": "geometry"
      }
    }
  }
}
Copy as curlTry in Elastic 
{
  ...
  "aggregations": {
    "viewport": {
      "bounds": {
        "top_left": {
          "lat": 52.39420966710895,
          "lon": 4.912349972873926
        },
        "bottom_right": {
          "lat": 52.374080987647176,
          "lon": 4.969425117596984
        }
      }
    }
  }
}
« 扩展统计聚合 地理质心聚合 »

On this page

Was this helpful?
Feedback