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Geo-shape datatypeedit

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.

Mapping Optionsedit

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.

Option Description Default

tree

[6.6] Deprecated in 6.6. PrefixTrees no longer used Name of the PrefixTree implementation to be used: geohash for GeohashPrefixTree and quadtree for QuadPrefixTree. Note: This parameter is only relevant for term and recursive strategies.

quadtree

precision

[6.6] Deprecated in 6.6. PrefixTrees no longer used This parameter may be used instead of tree_levels to set an appropriate value for the tree_levels parameter. The value specifies the desired precision and Elasticsearch will calculate the best tree_levels value to honor this precision. The value should be a number followed by an optional distance unit. Valid distance units include: in, inch, yd, yard, mi, miles, km, kilometers, m,meters, cm,centimeters, mm, millimeters. Note: This parameter is only relevant for term and recursive strategies.

50m

tree_levels

[6.6] Deprecated in 6.6. PrefixTrees no longer used Maximum number of layers to be used by the PrefixTree. This can be used to control the precision of shape representations andtherefore how many terms are indexed. Defaults to the default value of the chosen PrefixTree implementation. Since this parameter requires a certain level of understanding of the underlying implementation, users may use the precision parameter instead. However, Elasticsearch only uses the tree_levels parameter internally and this is what is returned via the mapping API even if you use the precision parameter. Note: This parameter is only relevant for term and recursive strategies.

various

strategy

[6.6] Deprecated in 6.6. PrefixTrees no longer used The strategy parameter defines the approach for how to represent shapes at indexing and search time. It also influences the capabilities available so it is recommended to let Elasticsearch set this parameter automatically. There are two strategies available: recursive, and term. Recursive and Term strategies are deprecated and will be removed in a future version. While they are still available, the Term strategy supports point types only (the points_only parameter will be automatically set to true) while Recursive strategy supports all shape types. (IMPORTANT: see Prefix trees for more detailed information about these strategies)

recursive

distance_error_pct

[6.6] Deprecated in 6.6. PrefixTrees no longer used Used as a hint to the PrefixTree about how precise it should be. Defaults to 0.025 (2.5%) with 0.5 as the maximum supported value. PERFORMANCE NOTE: This value will default to 0 if a precision or tree_level definition is explicitly defined. This guarantees spatial precision at the level defined in the mapping. This can lead to significant memory usage for high resolution shapes with low error (e.g., large shapes at 1m with < 0.001 error). To improve indexing performance (at the cost of query accuracy) explicitly define tree_level or precision along with a reasonable distance_error_pct, noting that large shapes will have greater false positives. Note: This parameter is only relevant for term and recursive strategies.

0.025

orientation

Optionally define how to interpret vertex order for polygons / multipolygons. This parameter defines one of two coordinate system rules (Right-hand or Left-hand) each of which can be specified in three different ways. 1. Right-hand rule: right, ccw, counterclockwise, 2. Left-hand rule: left, cw, clockwise. The default orientation (counterclockwise) complies with the OGC standard which defines outer ring vertices in counterclockwise order with inner ring(s) vertices (holes) in clockwise order. Setting this parameter in the geo_shape mapping explicitly sets vertex order for the coordinate list of a geo_shape field but can be overridden in each individual GeoJSON or WKT document.

ccw

points_only

[6.6] Deprecated in 6.6. PrefixTrees no longer used Setting this option to true (defaults to false) configures the geo_shape field type for point shapes only (NOTE: Multi-Points are not yet supported). This optimizes index and search performance for the geohash and quadtree when it is known that only points will be indexed. At present geo_shape queries can not be executed on geo_point field types. This option bridges the gap by improving point performance on a geo_shape field so that geo_shape queries are optimal on a point only field.

false

ignore_malformed

If true, malformed GeoJSON or WKT shapes are ignored. If false (default), malformed GeoJSON and WKT shapes throw an exception and reject the entire document.

false

ignore_z_value

If true (default) three dimension points will be accepted (stored in source) but only latitude and longitude values will be indexed; the third dimension is ignored. If false, geo-points containing any more than latitude and longitude (two dimensions) values throw an exception and reject the whole document.

true

coerce

If true unclosed linear rings in polygons will be automatically closed.

false

Indexing approachedit

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.

Prefix treesedit

[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:

  • GeohashPrefixTree - Uses geohashes for grid squares. Geohashes are base32 encoded strings of the bits of the latitude and longitude interleaved. So the longer the hash, the more precise it is. Each character added to the geohash represents another tree level and adds 5 bits of precision to the geohash. A geohash represents a rectangular area and has 32 sub rectangles. The maximum number of levels in Elasticsearch is 24; the default is 9.
  • QuadPrefixTree - Uses a quadtree for grid squares. Similar to geohash, quad trees interleave the bits of the latitude and longitude the resulting hash is a bit set. A tree level in a quad tree represents 2 bits in this bit set, one for each coordinate. The maximum number of levels for the quad trees in Elasticsearch is 29; the default is 21.
Spatial strategiesedit

[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:

  • What type of Shapes can be indexed?
  • What types of Query Operations and Shapes can be used?
  • Does it support more than one Shape per field?

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

Strategy Supported Shapes Supported Queries Multiple Shapes

recursive

All

INTERSECTS, DISJOINT, WITHIN, CONTAINS

Yes

term

Points

INTERSECTS

Yes

Accuracyedit

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.

Exampleedit
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.

Performance considerations with Prefix Treesedit

[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.

Geo-shape queries on geo-shapes implemented with PrefixTrees will not be executed if search.allow_expensive_queries is set to false.

Input Structureedit

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:

GeoJSON Type WKT Type Elasticsearch Type Description

Point

POINT

point

A single geographic coordinate. Note: Elasticsearch uses WGS-84 coordinates only.

LineString

LINESTRING

linestring

An arbitrary line given two or more points.

Polygon

POLYGON

polygon

A closed polygon whose first and last point must match, thus requiring n + 1 vertices to create an n-sided polygon and a minimum of 4 vertices.

MultiPoint

MULTIPOINT

multipoint

An array of unconnected, but likely related points.

MultiLineString

MULTILINESTRING

multilinestring

An array of separate linestrings.

MultiPolygon

MULTIPOLYGON

multipolygon

An array of separate polygons.

GeometryCollection

GEOMETRYCOLLECTION

geometrycollection

A GeoJSON shape similar to the multi* shapes except that multiple types can coexist (e.g., a Point and a LineString).

N/A

BBOX

envelope

A bounding rectangle, or envelope, specified by specifying only the top left and bottom right points.

N/A

N/A

circle

A circle specified by a center point and radius with units, which default to METERS.

For all types, both the inner type and coordinates fields are required.

In GeoJSON and WKT, and therefore Elasticsearch, the correct coordinate order is longitude, latitude (X, Y) within coordinate arrays. This differs from many Geospatial APIs (e.g., Google Maps) that generally use the colloquial latitude, longitude (Y, X).

Pointedit

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)"
}
LineStringedit

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.

Polygonedit

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))"
}

IMPORTANT NOTE: WKT does not enforce a specific order for vertices thus ambiguous polygons around the dateline and poles are possible. GeoJSON mandates that the outer polygon must be counterclockwise and interior shapes must be clockwise, which agrees with the Open Geospatial Consortium (OGC) Simple Feature Access specification for vertex ordering.

Elasticsearch accepts both clockwise and counterclockwise polygons if they appear not to cross the dateline (i.e. they cross less than 180° of longitude), but for polygons that do cross the dateline (or for other polygons wider than 180°) Elasticsearch requires the vertex ordering to comply with the OGC and GeoJSON specifications. 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 the GeoJSON standard to eliminate ambiguity resulting in a polygon that crosses the dateline.

POST /example/_doc
{
    "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:

POST /example/_doc
{
    "location" : {
        "type" : "polygon",
        "orientation" : "clockwise",
        "coordinates" : [
            [ [100.0, 0.0], [100.0, 1.0], [101.0, 1.0], [101.0, 0.0], [100.0, 0.0] ]
        ]
    }
}
MultiPointedit

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)"
}
MultiLineStringedit

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))"
}
MultiPolygonedit

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)))"
}
Geometry Collectionedit

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))"
}
Envelopeedit

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)"
}
Circleedit

Elasticsearch supports a circle type, which consists of a center point with a radius. Note that this circle representation can only be indexed when using the recursive Prefix Tree strategy. For the default Indexing approach circles should be approximated using a POLYGON.

POST /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.

Sorting and Retrieving index Shapesedit

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.