GeoHash grid Aggregation

A multi-bucket aggregation that works on geo_point fields and groups points into buckets that represent cells in a grid. The resulting grid can be sparse and only contains cells that have matching data. Each cell is labeled using a geohash which is of user-definable precision.

• High precision geohashes have a long string length and represent cells that cover only a small area.
• Low precision geohashes have a short string length and represent cells that each cover a large area.

Geohashes used in this aggregation can have a choice of precision between 1 and 12.

The highest-precision geohash of length 12 produces cells that cover less than a square metre 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.

The specified field must be of type geo_point (which can only be set explicitly in the mappings) and it can also hold an array of geo_point fields, in which case all points will be taken into account during aggregation.

Simple low-precision request

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": {
      "geohash_grid": {
        "field": "location",
        "precision": 3
      }
    }
  }
}

Response:

{
  ...
  "aggregations": {
  "large-grid": {
    "buckets": [
      {
        "key": "u17",
        "doc_count": 3
      },
      {
        "key": "u09",
        "doc_count": 2
      },
      {
        "key": "u15",
        "doc_count": 1
      }
    ]
  }
}
}

High-precision requests

When requesting detailed buckets (typically for displaying a “zoomed in” map) a filter like geo_bounding_box should be applied to narrow the subject area otherwise potentially millions of buckets will be created and returned.

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": {
          "geohash_grid": {
            "field": "location",
            "precision": 8
          }
        }
      }
    }
  }
}

The geohashes returned by the geohash_grid aggregation can be also used for zooming in. To zoom into the first geohash u17 returned in the previous example, it should be specified as both top_left and bottom_right corner:

POST /museums/_search?size=0
{
  "aggregations": {
    "zoomed-in": {
      "filter": {
        "geo_bounding_box": {
          "location": {
            "top_left": "u17",
            "bottom_right": "u17"
          }
        }
      },
      "aggregations": {
        "zoom1": {
          "geohash_grid": {
            "field": "location",
            "precision": 8
          }
        }
      }
    }
  }
}

{
  ...
  "aggregations": {
    "zoomed-in": {
      "doc_count": 3,
      "zoom1": {
        "buckets": [
          {
            "key": "u173zy3j",
            "doc_count": 1
          },
          {
            "key": "u173zvfz",
            "doc_count": 1
          },
          {
            "key": "u173zt90",
            "doc_count": 1
          }
        ]
      }
    }
  }
}

For “zooming in” on the system that don’t support geohashes, the bucket keys should be translated into bounding boxes using one of available geohash libraries. For example, for javascript the node-geohash library can be used:

var geohash = require('ngeohash');
// bbox will contain [ 52.03125, 4.21875, 53.4375, 5.625 ]
//                   [   minlat,  minlon,  maxlat, maxlon]
var bbox = geohash.decode_bbox('u17');

Requests with additional bounding box filtering

The geohash_grid aggregation supports an optional bounds parameter that restricts the points considered to those that fall within the bounds provided. The bounds parameter accepts the bounding box in all the same 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 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.

POST /museums/_search?size=0
{
  "aggregations": {
    "tiles-in-bounds": {
      "geohash_grid": {
        "field": "location",
        "precision": 8,
        "bounds": {
          "top_left": "53.4375, 4.21875",
          "bottom_right": "52.03125, 5.625"
        }
      }
    }
  }
}

{
  ...
  "aggregations": {
    "tiles-in-bounds": {
      "buckets": [
        {
          "key": "u173zy3j",
          "doc_count": 1
        },
        {
          "key": "u173zvfz",
          "doc_count": 1
        },
        {
          "key": "u173zt90",
          "doc_count": 1
        }
      ]
    }
  }
}

Cell dimensions at the equator

The table below shows the metric dimensions for cells covered by various string lengths of geohash. Cell dimensions vary with latitude and so the table is for the worst-case scenario at the equator.

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:

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