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Elastic Docs ›Elasticsearch Guide [8.15] ›Aggregations ›Metrics aggregations

Cartesian-centroid aggregation

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Cartesian-centroid aggregation

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A metric aggregation that computes the weighted centroid from all coordinate values for point and shape fields.

Example:

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

resp1 = client.bulk(
    index="museums",
    refresh=True,
    operations=[
        {
            "index": {
                "_id": 1
            }
        },
        {
            "location": "POINT (491.2350 5237.4081)",
            "city": "Amsterdam",
            "name": "NEMO Science Museum"
        },
        {
            "index": {
                "_id": 2
            }
        },
        {
            "location": "POINT (490.1618 5236.9219)",
            "city": "Amsterdam",
            "name": "Museum Het Rembrandthuis"
        },
        {
            "index": {
                "_id": 3
            }
        },
        {
            "location": "POINT (491.4722 5237.1667)",
            "city": "Amsterdam",
            "name": "Nederlands Scheepvaartmuseum"
        },
        {
            "index": {
                "_id": 4
            }
        },
        {
            "location": "POINT (440.5200 5122.2900)",
            "city": "Antwerp",
            "name": "Letterenhuis"
        },
        {
            "index": {
                "_id": 5
            }
        },
        {
            "location": "POINT (233.6389 4886.1111)",
            "city": "Paris",
            "name": "Musée du Louvre"
        },
        {
            "index": {
                "_id": 6
            }
        },
        {
            "location": "POINT (232.7000 4886.0000)",
            "city": "Paris",
            "name": "Musée d'Orsay"
        }
    ],
)
print(resp1)

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

response = client.bulk(
  index: 'museums',
  refresh: true,
  body: [
    {
      index: {
        _id: 1
      }
    },
    {
      location: 'POINT (491.2350 5237.4081)',
      city: 'Amsterdam',
      name: 'NEMO Science Museum'
    },
    {
      index: {
        _id: 2
      }
    },
    {
      location: 'POINT (490.1618 5236.9219)',
      city: 'Amsterdam',
      name: 'Museum Het Rembrandthuis'
    },
    {
      index: {
        _id: 3
      }
    },
    {
      location: 'POINT (491.4722 5237.1667)',
      city: 'Amsterdam',
      name: 'Nederlands Scheepvaartmuseum'
    },
    {
      index: {
        _id: 4
      }
    },
    {
      location: 'POINT (440.5200 5122.2900)',
      city: 'Antwerp',
      name: 'Letterenhuis'
    },
    {
      index: {
        _id: 5
      }
    },
    {
      location: 'POINT (233.6389 4886.1111)',
      city: 'Paris',
      name: 'Musée du Louvre'
    },
    {
      index: {
        _id: 6
      }
    },
    {
      location: 'POINT (232.7000 4886.0000)',
      city: 'Paris',
      name: "Musée d'Orsay"
    }
  ]
)
puts response

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

const response1 = await client.bulk({
  index: "museums",
  refresh: "true",
  operations: [
    {
      index: {
        _id: 1,
      },
    },
    {
      location: "POINT (491.2350 5237.4081)",
      city: "Amsterdam",
      name: "NEMO Science Museum",
    },
    {
      index: {
        _id: 2,
      },
    },
    {
      location: "POINT (490.1618 5236.9219)",
      city: "Amsterdam",
      name: "Museum Het Rembrandthuis",
    },
    {
      index: {
        _id: 3,
      },
    },
    {
      location: "POINT (491.4722 5237.1667)",
      city: "Amsterdam",
      name: "Nederlands Scheepvaartmuseum",
    },
    {
      index: {
        _id: 4,
      },
    },
    {
      location: "POINT (440.5200 5122.2900)",
      city: "Antwerp",
      name: "Letterenhuis",
    },
    {
      index: {
        _id: 5,
      },
    },
    {
      location: "POINT (233.6389 4886.1111)",
      city: "Paris",
      name: "Musée du Louvre",
    },
    {
      index: {
        _id: 6,
      },
    },
    {
      location: "POINT (232.7000 4886.0000)",
      city: "Paris",
      name: "Musée d'Orsay",
    },
  ],
});
console.log(response1);

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

POST /museums/_bulk?refresh
{"index":{"_id":1}}
{"location": "POINT (491.2350 5237.4081)", "city": "Amsterdam", "name": "NEMO Science Museum"}
{"index":{"_id":2}}
{"location": "POINT (490.1618 5236.9219)", "city": "Amsterdam", "name": "Museum Het Rembrandthuis"}
{"index":{"_id":3}}
{"location": "POINT (491.4722 5237.1667)", "city": "Amsterdam", "name": "Nederlands Scheepvaartmuseum"}
{"index":{"_id":4}}
{"location": "POINT (440.5200 5122.2900)", "city": "Antwerp", "name": "Letterenhuis"}
{"index":{"_id":5}}
{"location": "POINT (233.6389 4886.1111)", "city": "Paris", "name": "Musée du Louvre"}
{"index":{"_id":6}}
{"location": "POINT (232.7000 4886.0000)", "city": "Paris", "name": "Musée d'Orsay"}

POST /museums/_search?size=0
{
  "aggs": {
    "centroid": {
      "cartesian_centroid": {
        "field": "location" 
      }
    }
  }
}

The cartesian_centroid aggregation specifies the field to use for computing the centroid, which must be a Point or a Shape type.

The above aggregation demonstrates how one would compute the centroid of the location field for all museums' documents.

The response for the above aggregation:

{
  ...
  "aggregations": {
    "centroid": {
      "location": {
        "x": 396.6213124593099,
        "y": 5100.982991536458
      },
      "count": 6
    }
  }
}

The cartesian_centroid aggregation is more interesting when combined as a sub-aggregation to other bucket aggregations.

Example:

resp = client.search(
    index="museums",
    size="0",
    aggs={
        "cities": {
            "terms": {
                "field": "city.keyword"
            },
            "aggs": {
                "centroid": {
                    "cartesian_centroid": {
                        "field": "location"
                    }
                }
            }
        }
    },
)
print(resp)
const response = await client.search({
  index: "museums",
  size: 0,
  aggs: {
    cities: {
      terms: {
        field: "city.keyword",
      },
      aggs: {
        centroid: {
          cartesian_centroid: {
            field: "location",
          },
        },
      },
    },
  },
});
console.log(response);
POST /museums/_search?size=0
{
  "aggs": {
    "cities": {
      "terms": { "field": "city.keyword" },
      "aggs": {
        "centroid": {
          "cartesian_centroid": { "field": "location" }
        }
      }
    }
  }
}

The above example uses cartesian_centroid as a sub-aggregation to a terms bucket aggregation for finding the central location for museums in each city.

The response for the above aggregation:

{
  ...
  "aggregations": {
    "cities": {
      "sum_other_doc_count": 0,
      "doc_count_error_upper_bound": 0,
      "buckets": [
        {
          "key": "Amsterdam",
          "doc_count": 3,
          "centroid": {
            "location": {
              "x": 490.9563293457031,
              "y": 5237.16552734375
            },
            "count": 3
          }
        },
        {
          "key": "Paris",
          "doc_count": 2,
          "centroid": {
            "location": {
              "x": 233.16944885253906,
              "y": 4886.0556640625
            },
            "count": 2
          }
        },
        {
          "key": "Antwerp",
          "doc_count": 1,
          "centroid": {
            "location": {
              "x": 440.5199890136719,
              "y": 5122.2900390625
            },
            "count": 1
          }
        }
      ]
    }
  }
}

Cartesian Centroid Aggregation on shape fields

edit

The centroid metric for shapes is more nuanced than for points. The centroid of a specific aggregation bucket containing shapes is the centroid of the highest-dimensionality shape type in the bucket. For example, if a bucket contains shapes consisting of polygons and lines, then the lines do not contribute to the centroid metric. Each type of shape’s centroid is calculated differently. Envelopes and circles ingested via the Circle are treated as polygons.

Geometry Type Centroid Calculation

[Multi]Point

equally weighted average of all the coordinates

[Multi]LineString

a weighted average of all the centroids of each segment, where the weight of each segment is its length in the same units as the coordinates

[Multi]Polygon

a weighted average of all the centroids of all the triangles of a polygon where the triangles are formed by every two consecutive vertices and the starting-point. holes have negative weights. weights represent the area of the triangle is calculated in the square of the units of the coordinates

GeometryCollection

The centroid of all the underlying geometries with the highest dimension. If Polygons and Lines and/or Points, then lines and/or points are ignored. If Lines and Points, then points are ignored

Example:

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

resp1 = client.bulk(
    index="places",
    refresh=True,
    operations=[
        {
            "index": {
                "_id": 1
            }
        },
        {
            "name": "NEMO Science Museum",
            "geometry": "POINT(491.2350 5237.4081)"
        },
        {
            "index": {
                "_id": 2
            }
        },
        {
            "name": "Sportpark De Weeren",
            "geometry": {
                "type": "Polygon",
                "coordinates": [
                    [
                        [
                            496.5305328369141,
                            5239.347642069457
                        ],
                        [
                            496.6979026794433,
                            5239.172175893484
                        ],
                        [
                            496.9425201416015,
                            5239.238958618537
                        ],
                        [
                            496.7944622039794,
                            5239.420969150824
                        ],
                        [
                            496.5305328369141,
                            5239.347642069457
                        ]
                    ]
                ]
            }
        }
    ],
)
print(resp1)

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

response = client.bulk(
  index: 'places',
  refresh: true,
  body: [
    {
      index: {
        _id: 1
      }
    },
    {
      name: 'NEMO Science Museum',
      geometry: 'POINT(491.2350 5237.4081)'
    },
    {
      index: {
        _id: 2
      }
    },
    {
      name: 'Sportpark De Weeren',
      geometry: {
        type: 'Polygon',
        coordinates: [
          [
            [
              496.5305328369141,
              5239.347642069457
            ],
            [
              496.6979026794433,
              5239.172175893484
            ],
            [
              496.9425201416015,
              5239.238958618537
            ],
            [
              496.7944622039794,
              5239.420969150824
            ],
            [
              496.5305328369141,
              5239.347642069457
            ]
          ]
        ]
      }
    }
  ]
)
puts response

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

const response1 = await client.bulk({
  index: "places",
  refresh: "true",
  operations: [
    {
      index: {
        _id: 1,
      },
    },
    {
      name: "NEMO Science Museum",
      geometry: "POINT(491.2350 5237.4081)",
    },
    {
      index: {
        _id: 2,
      },
    },
    {
      name: "Sportpark De Weeren",
      geometry: {
        type: "Polygon",
        coordinates: [
          [
            [496.5305328369141, 5239.347642069457],
            [496.6979026794433, 5239.172175893484],
            [496.9425201416015, 5239.238958618537],
            [496.7944622039794, 5239.420969150824],
            [496.5305328369141, 5239.347642069457],
          ],
        ],
      },
    },
  ],
});
console.log(response1);

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

POST /places/_bulk?refresh
{"index":{"_id":1}}
{"name": "NEMO Science Museum", "geometry": "POINT(491.2350 5237.4081)" }
{"index":{"_id":2}}
{"name": "Sportpark De Weeren", "geometry": { "type": "Polygon", "coordinates": [ [ [ 496.5305328369141, 5239.347642069457 ], [ 496.6979026794433, 5239.1721758934835 ], [ 496.9425201416015, 5239.238958618537 ], [ 496.7944622039794, 5239.420969150824 ], [ 496.5305328369141, 5239.347642069457 ] ] ] } }

POST /places/_search?size=0
{
  "aggs": {
    "centroid": {
      "cartesian_centroid": {
        "field": "geometry"
      }
    }
  }
}
{
  ...
  "aggregations": {
    "centroid": {
      "location": {
        "x": 496.74041748046875,
        "y": 5239.29638671875
      },
      "count": 2
    }
  }
}
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