Geo-Shape datatype

The geo_shape datatype facilitates the indexing of and searching with arbitrary geo shapes such as rectangles and polygons. It should be used when either the data being indexed or the queries being executed contain shapes other than just points.

You can query documents using this type using geo_shape Query.

The geo_shape mapping maps geo_json geometry objects to the geo_shape type. To enable it, users must explicitly map fields to the geo_shape type.

To efficiently represent shapes in the index, Shapes are converted into a series of hashes representing grid squares (commonly referred to as "rasters") using implementations of a PrefixTree. The tree notion comes from the fact that the PrefixTree uses multiple grid layers, each with an increasing level of precision to represent the Earth. This can be thought of as increasing the level of detail of a map or image at higher zoom levels.

Multiple PrefixTree implementations are provided:

The PrefixTree implementations rely on a SpatialStrategy for decomposing the provided Shape(s) into approximated grid squares. Each strategy answers the following:

The following Strategy implementations (with corresponding capabilities) are provided:

Geo_shape does not provide 100% accuracy and depending on how it is configured it may return some false positives or false negatives for certain queries. To mitigate this, it is important to select an appropriate value for the tree_levels parameter and to adjust expectations accordingly. For example, a point may be near the border of a particular grid cell and may thus not match a query that only matches the cell right next to it — even though the shape is very close to the point.

{
    "properties": {
        "location": {
            "type": "geo_shape",
            "tree": "quadtree",
            "precision": "1m"
        }
    }
}

This mapping maps the location field to the geo_shape type using the quad_tree implementation and a precision of 1m. Elasticsearch translates this into a tree_levels setting of 26.

Elasticsearch uses the paths in the prefix tree as terms in the index and in queries. The higher the level is (and thus the precision), the more terms are generated. Of course, calculating the terms, keeping them in memory, and storing them on disk all have a price. Especially with higher tree levels, indices can become extremely large even with a modest amount of data. Additionally, the size of the features also matters. Big, complex polygons can take up a lot of space at higher tree levels. Which setting is right depends on the use case. Generally one trades off accuracy against index size and query performance.

The defaults in Elasticsearch for both implementations are a compromise between index size and a reasonable level of precision of 50m at the equator. This allows for indexing tens of millions of shapes without overly bloating the resulting index too much relative to the input size.

The GeoJSON format is used to represent shapes as input as follows:

A point is a single geographic coordinate, such as the location of a building or the current position given by a smartphone’s Geolocation API.

{
    "location" : {
        "type" : "point",
        "coordinates" : [-77.03653, 38.897676]
    }
}

A linestring defined by an array of two or more positions. By specifying only two points, the linestring will represent a straight line. Specifying more than two points creates an arbitrary path.

{
    "location" : {
        "type" : "linestring",
        "coordinates" : [[-77.03653, 38.897676], [-77.009051, 38.889939]]
    }
}

The above linestring would draw a straight line starting at the White House to the US Capitol Building.

A polygon is defined by a list of a list of points. The first and last points in each (outer) list must be the same (the polygon must be closed).

{
    "location" : {
        "type" : "polygon",
        "coordinates" : [
            [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ]
        ]
    }
}

The first array represents the outer boundary of the polygon, the other arrays represent the interior shapes ("holes"):

{
    "location" : {
        "type" : "polygon",
        "coordinates" : [
            [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ],
            [ [100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8], [100.2, 0.2] ]
        ]
    }
}

IMPORTANT NOTE: GeoJSON does not mandate a specific order for vertices thus ambiguous polygons around the dateline and poles are possible. To alleviate ambiguity the Open Geospatial Consortium (OGC) Simple Feature Access specification defines the following vertex ordering:

For polygons that do not cross the dateline, vertex order will not matter in Elasticsearch. For polygons that do cross the dateline, Elasticsearch requires vertex ordering to comply with the OGC specification. Otherwise, an unintended polygon may be created and unexpected query/filter results will be returned.

The following provides an example of an ambiguous polygon. Elasticsearch will apply OGC standards to eliminate ambiguity resulting in a polygon that crosses the dateline.

{
    "location" : {
        "type" : "polygon",
        "coordinates" : [
            [ [-177.0, 10.0], [176.0, 15.0], [172.0, 0.0], [176.0, -15.0], [-177.0, -10.0], [-177.0, 10.0] ],
            [ [178.2, 8.2], [-178.8, 8.2], [-180.8, -8.8], [178.2, 8.8] ]
        ]
    }
}

An orientation parameter can be defined when setting the geo_shape mapping (see Mapping Options). This will define vertex order for the coordinate list on the mapped geo_shape field. It can also be overridden on each document. The following is an example for overriding the orientation on a document:

{
    "location" : {
        "type" : "polygon",
        "orientation" : "clockwise",
        "coordinates" : [
            [ [-177.0, 10.0], [176.0, 15.0], [172.0, 0.0], [176.0, -15.0], [-177.0, -10.0], [-177.0, 10.0] ],
            [ [178.2, 8.2], [-178.8, 8.2], [-180.8, -8.8], [178.2, 8.8] ]
        ]
    }
}

A list of geojson points.

{
    "location" : {
        "type" : "multipoint",
        "coordinates" : [
            [102.0, 2.0], [103.0, 2.0]
        ]
    }
}

A list of geojson linestrings.

{
    "location" : {
        "type" : "multilinestring",
        "coordinates" : [
            [ [102.0, 2.0], [103.0, 2.0], [103.0, 3.0], [102.0, 3.0] ],
            [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0] ],
            [ [100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8] ]
        ]
    }
}

A list of geojson polygons.

{
    "location" : {
        "type" : "multipolygon",
        "coordinates" : [
            [ [[102.0, 2.0], [103.0, 2.0], [103.0, 3.0], [102.0, 3.0], [102.0, 2.0]] ],

            [ [[100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0]],
              [[100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8], [100.2, 0.2]] ]
        ]
    }
}

A collection of geojson geometry objects.

{
    "location" : {
        "type": "geometrycollection",
        "geometries": [
            {
                "type": "point",
                "coordinates": [100.0, 0.0]
            },
            {
                "type": "linestring",
                "coordinates": [ [101.0, 0.0], [102.0, 1.0] ]
            }
        ]
    }
}

Elasticsearch supports an envelope type, which consists of coordinates for upper left and lower right points of the shape to represent a bounding rectangle:

{
    "location" : {
        "type" : "envelope",
        "coordinates" : [ [-45.0, 45.0], [45.0, -45.0] ]
    }
}

Elasticsearch supports a circle type, which consists of a center point with a radius:

{
    "location" : {
        "type" : "circle",
        "coordinates" : [-45.0, 45.0],
        "radius" : "100m"
    }
}

Note: The inner radius field is required. If not specified, then the units of the radius will default to METERS.

Due to the complex input structure and index representation of shapes, it is not currently possible to sort shapes or retrieve their fields directly. The geo_shape value is only retrievable through the _source field.