elasticsearch/docs/reference/aggregations/bucket/geotilegrid-aggregation.asciidoc

[[search-aggregations-bucket-geotilegrid-aggregation]]
=== Geotile grid aggregation
++++
<titleabbrev>Geotile grid</titleabbrev>
++++

A multi-bucket aggregation that groups <<geo-point,`geo_point`>> and
<<geo-shape,`geo_shape`>> values into buckets that represent a grid.
The resulting grid can be sparse and only
contains cells that have matching data. Each cell corresponds to a
{wikipedia}/Tiled_web_map[map tile] as used by many online map
sites. Each cell is labeled using a "{zoom}/{x}/{y}" format, where zoom is equal
to the user-specified precision.

* High precision keys have a larger range for x and y, and represent tiles that
cover only a small area.
* Low precision keys have a smaller range for x and y, and represent tiles that
each cover a large area.

See https://wiki.openstreetmap.org/wiki/Zoom_levels[Zoom level documentation]
on how precision (zoom) correlates to size on the ground. Precision for this
aggregation can be between 0 and 29, inclusive.

WARNING: The highest-precision geotile of length 29 produces cells that cover
less than a 10cm by 10cm of land and so high-precision requests can be very
costly in terms of RAM and result sizes. Please see the example below on how
to first filter the aggregation to a smaller geographic area before requesting
high-levels of detail.

You can only use `geotile_grid` to aggregate an explicitly mapped `geo_point` or
`geo_shape` field. If the `geo_point` field contains an array, `geotile_grid`
aggregates all the array values.


==== Simple low-precision request

[source,console,id=geotilegrid-aggregation-example]
--------------------------------------------------
PUT /museums
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_point"
      }
    }
  }
}

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

POST /museums/_search?size=0
{
  "aggregations": {
    "large-grid": {
      "geotile_grid": {
        "field": "location",
        "precision": 8
      }
    }
  }
}
--------------------------------------------------

Response:

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "large-grid": {
      "buckets": [
        {
          "key": "8/131/84",
          "doc_count": 3
        },
        {
          "key": "8/129/88",
          "doc_count": 2
        },
        {
          "key": "8/131/85",
          "doc_count": 1
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

==== High-precision requests

When requesting detailed buckets (typically for displaying a "zoomed in" map)
a filter like <<query-dsl-geo-bounding-box-query,geo_bounding_box>> should be
applied to narrow the subject area otherwise potentially millions of buckets
will be created and returned.

[source,console]
--------------------------------------------------
POST /museums/_search?size=0
{
  "aggregations": {
    "zoomed-in": {
      "filter": {
        "geo_bounding_box": {
          "location": {
            "top_left": "52.4, 4.9",
            "bottom_right": "52.3, 5.0"
          }
        }
      },
      "aggregations": {
        "zoom1": {
          "geotile_grid": {
            "field": "location",
            "precision": 22
          }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[continued]

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "zoomed-in": {
      "doc_count": 3,
      "zoom1": {
        "buckets": [
          {
            "key": "22/2154412/1378379",
            "doc_count": 1
          },
          {
            "key": "22/2154385/1378332",
            "doc_count": 1
          },
          {
            "key": "22/2154259/1378425",
            "doc_count": 1
          }
        ]
      }
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

==== Requests with additional bounding box filtering

The `geotile_grid` aggregation supports an optional `bounds` parameter
that restricts the cells considered to those that intersects the
bounds provided. The `bounds` parameter accepts the bounding box in
all the same <<query-dsl-geo-bounding-box-query-accepted-formats,accepted formats>> of the
bounds specified in the Geo Bounding Box Query. This bounding box can be used with or
without an additional `geo_bounding_box` query for filtering the points prior to aggregating.
It is an independent bounding box that can intersect with, be equal to, or be disjoint
to any additional `geo_bounding_box` queries defined in the context of the aggregation.

[source,console,id=geotilegrid-aggregation-with-bounds]
--------------------------------------------------
POST /museums/_search?size=0
{
  "aggregations": {
    "tiles-in-bounds": {
      "geotile_grid": {
        "field": "location",
        "precision": 22,
        "bounds": {
          "top_left": "52.4, 4.9",
          "bottom_right": "52.3, 5.0"
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[continued]

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "tiles-in-bounds": {
      "buckets": [
        {
          "key": "22/2154412/1378379",
          "doc_count": 1
        },
        {
          "key": "22/2154385/1378332",
          "doc_count": 1
        },
        {
          "key": "22/2154259/1378425",
          "doc_count": 1
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

[discrete]
[role="xpack"]
==== Aggregating `geo_shape` fields

Aggregating on <<geo-shape>> fields works just as it does for points, except that a single
shape can be counted for in multiple tiles. A shape will contribute to the count of matching values
if any part of its shape intersects with that tile. Below is an image that demonstrates this:


image:images/spatial/geoshape_grid.png[]

==== Options

[horizontal]
field::         Mandatory. The name of the field indexed with GeoPoints.

precision::     Optional. The integer zoom of the key used to define
                cells/buckets in the results. Defaults to 7.
                Values outside of [0,29] will be rejected.

bounds:         Optional. The bounding box to filter the points in the bucket.

size::          Optional. The maximum number of geohash buckets to return
                (defaults to 10,000). When results are trimmed, buckets are
                prioritised based on the volumes of documents they contain.

shard_size::    Optional. To allow for more accurate counting of the top cells
                returned in the final result the aggregation defaults to
                returning `max(10,(size x number-of-shards))` buckets from each
                shard. If this heuristic is undesirable, the number considered
                from each shard can be over-ridden using this parameter.