Geoshape field type

The geo_shape data type facilitates the indexing of and searching with arbitrary geoshapes 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 a geo_shape query.

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

Geoshape types are indexed by decomposing the shape into a triangular mesh and indexing each triangle as a 7 dimension point in a BKD tree. This provides near perfect spatial resolution (down to 1e-7 decimal degree precision) since all spatial relations are computed using an encoded vector representation of the original shape instead of a raster-grid representation as used by the Prefix trees indexing approach. Performance of the tessellator primarily depends on the number of vertices that define the polygon/multi-polygon. While this is the default indexing technique prefix trees can still be used by setting the tree or strategy parameters according to the appropriate Mapping Options. Note that these parameters are now deprecated and will be removed in a future version.

IMPORTANT NOTES

CONTAINS relation query - when using the new default vector indexing strategy, geo_shape queries with relation defined as contains are supported for indices created with ElasticSearch 7.5.0 or higher.

[6.6] Deprecated in 6.6. PrefixTrees no longer used To efficiently represent shapes in an inverted 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. Since this approach causes precision issues with indexed shape, it has been deprecated in favor of a vector indexing approach that indexes the shapes as a triangular mesh (see Indexing approach).

Multiple PrefixTree implementations are provided:

[6.6] Deprecated in 6.6. PrefixTrees no longer used The indexing implementation selected relies on a SpatialStrategy for choosing how to decompose the shapes (either as grid squares or a tessellated triangular mesh). Each strategy answers the following:

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

Recursive and Term strategies do not provide 100% accuracy and depending on how they are configured it may return some false positives for INTERSECTS, WITHIN and CONTAINS queries, and some false negatives for DISJOINT 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.

PUT /example
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_shape"
      }
    }
  }
}

This mapping definition maps the location field to the geo_shape type using the default vector implementation. It provides approximately 1e-7 decimal degree precision.

[6.6] Deprecated in 6.6. PrefixTrees no longer used With prefix trees, Elasticsearch uses the paths in the tree as terms in the inverted index and in queries. The higher the level (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.

Shapes can be represented using either the GeoJSON or Well-Known Text (WKT) format. The following table provides a mapping of GeoJSON and WKT to Elasticsearch types:

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. The following is an example of a point in GeoJSON.

POST /example/_doc
{
  "location" : {
    "type" : "Point",
    "coordinates" : [-77.03653, 38.897676]
  }
}

The following is an example of a point in WKT:

POST /example/_doc
{
  "location" : "POINT (-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. The following is an example of a linestring in GeoJSON.

POST /example/_doc
{
  "location" : {
    "type" : "LineString",
    "coordinates" : [[-77.03653, 38.897676], [-77.009051, 38.889939]]
  }
}

The following is an example of a linestring in WKT:

POST /example/_doc
{
  "location" : "LINESTRING (-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). The following is an example of a polygon in GeoJSON.

POST /example/_doc
{
  "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 following is an example of a polygon in WKT:

POST /example/_doc
{
  "location" : "POLYGON ((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"). The following is a GeoJSON example of a polygon with a hole:

POST /example/_doc
{
  "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] ]
    ]
  }
}

The following is an example of a polygon with a hole in WKT:

POST /example/_doc
{
  "location" : "POLYGON ((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 polygon’s orientation indicates the order of its vertices: RIGHT (counterclockwise) or LEFT (clockwise). Elasticsearch uses a polygon’s orientation to determine if it crosses the international dateline (+/-180° longitude).

You can set a default orientation for WKT polygons using the orientation mapping parameter. This is because the WKT specification doesn’t specify or enforce a default orientation.

GeoJSON polygons use a default orientation of RIGHT, regardless of orientation mapping parameter’s value. This is because the GeoJSON specification mandates that an outer polygon use a counterclockwise orientation and interior shapes use a clockwise orientation.

You can override the default orientation for GeoJSON polygons using the document-level orientation parameter. For example, the following indexing request specifies a document-level orientation of LEFT.

POST /example/_doc
{
  "location" : {
    "type" : "Polygon",
    "orientation" : "LEFT",
    "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] ]
    ]
  }
}

Elasticsearch only uses a polygon’s orientation to determine if it crosses the international dateline. If the difference between a polygon’s minimum longitude and the maximum longitude is less than 180°, the polygon doesn’t cross the dateline and its orientation has no effect.

If the difference between a polygon’s minimum longitude and the maximum longitude is 180° or greater, Elasticsearch checks whether the polygon’s document-level orientation differs from the default orientation. If the orientation differs, Elasticsearch considers the polygon to cross the international dateline and splits the polygon at the dateline.

The following is an example of a list of GeoJSON points:

POST /example/_doc
{
  "location" : {
    "type" : "MultiPoint",
    "coordinates" : [
      [102.0, 2.0], [103.0, 2.0]
    ]
  }
}

The following is an example of a list of WKT points:

POST /example/_doc
{
  "location" : "MULTIPOINT (102.0 2.0, 103.0 2.0)"
}

The following is an example of a list of GeoJSON linestrings:

POST /example/_doc
{
  "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] ]
    ]
  }
}

The following is an example of a list of WKT linestrings:

POST /example/_doc
{
  "location" : "MULTILINESTRING ((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))"
}

The following is an example of a list of GeoJSON polygons (second polygon contains a hole):

POST /example/_doc
{
  "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]] ]
    ]
  }
}

The following is an example of a list of WKT polygons (second polygon contains a hole):

POST /example/_doc
{
  "location" : "MULTIPOLYGON (((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)))"
}

The following is an example of a collection of GeoJSON geometry objects:

POST /example/_doc
{
  "location" : {
    "type": "GeometryCollection",
    "geometries": [
      {
        "type": "Point",
        "coordinates": [100.0, 0.0]
      },
      {
        "type": "LineString",
        "coordinates": [ [101.0, 0.0], [102.0, 1.0] ]
      }
    ]
  }
}

The following is an example of a collection of WKT geometry objects:

POST /example/_doc
{
  "location" : "GEOMETRYCOLLECTION (POINT (100.0 0.0), LINESTRING (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 in the format [[minLon, maxLat], [maxLon, minLat]]:

POST /example/_doc
{
  "location" : {
    "type" : "envelope",
    "coordinates" : [ [100.0, 1.0], [101.0, 0.0] ]
  }
}

The following is an example of an envelope using the WKT BBOX format:

NOTE: WKT specification expects the following order: minLon, maxLon, maxLat, minLat.

POST /example/_doc
{
  "location" : "BBOX (100.0, 102.0, 2.0, 0.0)"
}

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

The circle type requires a geo_shape field mapping with the deprecated recursive Prefix Tree strategy.

PUT /circle-example
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_shape",
        "strategy": "recursive"
      }
    }
  }
}

The following request indexes a circle geo-shape.

POST /circle-example/_doc
{
  "location" : {
    "type" : "circle",
    "coordinates" : [101.0, 1.0],
    "radius" : "100m"
  }
}

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

NOTE: Neither GeoJSON or WKT support a point-radius circle type.

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.