Indexing spatial data

To support the ability to retrieve the data based on spatial coordinates, the spatial search has been introduced.

Creating indexes

To take an advantage of the spatial search, first we need to create an index with a spatial field. To mark field as the spatial field, we need to use SpatialGenerate method:

object SpatialGenerate(double lat, double lng);

object SpatialGenerate(string fieldName, double lat, double lng);

object SpatialGenerate(string fieldName, string shapeWKT);

object SpatialGenerate(string fieldName, string shapeWKT, SpatialSearchStrategy strategy);

object SpatialGenerate(string fieldName, string shapeWKT, SpatialSearchStrategy strategy, int maxTreeLevel);

public enum SpatialSearchStrategy


  • fieldName is a name of the field containing the shape to use for filtering (if the overload with no fieldName is used, then the name is set to default value: __spatial)
  • lat/lng are latitude/longitude coordinates
  • shapeWKT is a shape in the WKT format
  • strategy is a spatial search strategy (default: GeohashPrefixTree)
  • maxTreeLevel is a integer that indicates the maximum number of levels to be used in the PrefixTree and controls the precision of shape representation (9 for GeohashPrefixTree and 23 for QuadPrefixTree)

In our example we will use Event class and a very simple index defined below.

public class Event
	public string Id { get; set; }

	public string Name { get; set; }

	public double Latitude { get; set; }

	public double Longitude { get; set; }

public class Events_ByNameAndCoordinates : AbstractIndexCreationTask<Event>
	public Events_ByNameAndCoordinates()
		Map = events => from e in events
						select new
							Name = e.Name,
							__ = SpatialGenerate("Coordinates", e.Latitude, e.Longitude)

If our Event contains the WKT property already:

public class EventWithWKT
	public string Id { get; set; }

	public string Name { get; set; }

	public string WKT { get; set; }

then can define our field using the Spatial method in the AbstractIndexCreationTask:

public class EventsWithWKT_ByNameAndWKT : AbstractIndexCreationTask<EventWithWKT>
	public EventsWithWKT_ByNameAndWKT()
		Map = events => from e in events
						select new
							Name = e.Name,
							WKT = e.WKT

		Spatial(x => x.WKT, options => options.Geography.Default());

where under options we got access to our geography and Cartesian factories:

public class SpatialOptionsFactory
	public GeographySpatialOptionsFactory Geography;

	public CartesianSpatialOptionsFactory Cartesian;

// GeohashPrefixTree strategy with maxTreeLevel set to 9
SpatialOptions Default(SpatialUnits circleRadiusUnits = SpatialUnits.Kilometers);

SpatialOptions BoundingBoxIndex(SpatialUnits circleRadiusUnits = SpatialUnits.Kilometers);

SpatialOptions GeohashPrefixTreeIndex(int maxTreeLevel, SpatialUnits circleRadiusUnits = SpatialUnits.Kilometers);

SpatialOptions QuadPrefixTreeIndex(int maxTreeLevel, SpatialUnits circleRadiusUnits = SpatialUnits.Kilometers);
SpatialOptions BoundingBoxIndex();

SpatialOptions QuadPrefixTreeIndex(int maxTreeLevel, SpatialBounds bounds);

Spatial search strategies


Geohash is a latitude/longitude representation system that describes earth as a grid with 32 cells, assigning an alphanumeric character to each grid cell. Each grid cell is further divided into 32 smaller chunks, and each chunk has an alphanumeric character assigned as well, and so on.

E.g. The location of 'New York' in the United States is represented by the following geohash: DR5REGY6R and it represents the 40.7144 -74.0060 coordinates. Removing characters from the end of geohash will decrease the precision level.

More information about geohash uses, decoding algorithm and limitations can be found here.


QuadTree represents earth as a grid with exactly four cells and similarly to geohash, each grid cell (sometimes called bucket) has a letter assigned and is divided further into 4 more cells and so on.

More information about QuadTree can be found here.


More information about BoundingBox can be found here.


GeohashPrefixTree is a default SpatialSearchStrategy. Doing any changes to the strategy after index has been created will trigger re-indexation process.


By default the precision level (maxTreeLevel) for GeohashPrefixTree is set to 9 and for QuadPrefixTree the value is 23, which means that the coordinates are represented by a 9 or 23 character string. The difference exists, because the QuadTree representation would be much less precise if the level would be the same.

Geohash precision values

Level E-W distance at equator N-S distance at equator
12 ~3.7cm ~1.8cm
11 ~14.9cm ~14.9cm
10 ~1.19m ~0.60m
9 ~4.78m ~4.78m
8 ~38.2m ~19.1m
7 ~152.8m ~152.8m
6 ~1.2km ~0.61km
5 ~4.9km ~4.9km
4 ~39km ~19.6km
3 ~157km ~157km
2 ~1252km ~626km
1 ~5018km ~5018km

Quadtree precision values

Level Distance at equator
30 ~4cm
29 ~7cm
28 ~15cm
27 ~30cm
26 ~60cm
25 ~1.19m
24 ~2.39m
23 ~4.78m
22 ~9.56m
21 ~19.11m
20 ~38.23m
19 ~76.23m
18 ~152.92m
17 ~305.84m
16 ~611.67m
15 ~1.22km
14 ~2.45km
13 ~4.89km
12 ~9.79km
11 ~19.57km
10 ~39.15km
9 ~78.29km
8 ~156.58km
7 ~313.12km
6 ~625.85km
5 ~1249km
4 ~2473km
3 ~4755km
2 ~7996km
1 ~15992km

Format support

From version 2.5 RavenDB also supports indexing of GeoJSON objects.

var point = new
	type = "Point",
	coordinates = new[] { -10d, 45d }

session.Store(new SpatialDoc { Shape = point });

Beside the WKT and GeoJSON following formats are also supported:

session.Store(new SpatialDoc { Point = new[] { -10d, 45d } });
session.Store(new SpatialDoc { Point = new { X = -10d, Y = 45d } });
session.Store(new SpatialDoc { Point = new { Latitude = 45d, Longitude = -10d } });
session.Store(new SpatialDoc { Point = new { lat = 45d, lon = -10d } });
session.Store(new SpatialDoc { Point = new { lat = 45d, lng = -10d } });
session.Store(new SpatialDoc { Point = new { Lat = 45d, Long = -10d } });
session.Store(new SpatialDoc { Point = "geo:45.0,-10.0;u=2.0" }); // Geo URI

Third-party spatial library integration

To integrate with other spatial libraries, the document store must be configured to use a custom library-specific JsonConverter which reads/writes WKT or GeoJSON.

Examples of such converters can be found at Simon Bartlett's github repository page.


Let's assume that we have a SpatialDoc with a corresponding index available:

public class SpatialDoc_ByShapeAndPoint : AbstractIndexCreationTask<SpatialDoc>
	public SpatialDoc_ByShapeAndPoint()
		Map = docs => from spatial in docs
					  select new
						  Shape = spatial.Shape,
						  Point = spatial.Point

		Spatial(x => x.Shape, options => options.Geography.Default());
		Spatial(x => x.Point, options => options.Cartesian.BoundingBoxIndex());

To find all results that are within radius of or intersect specified shape query as follows:

IList<SpatialDoc> results = session
	.Query<SpatialDoc, SpatialDoc_ByShapeAndPoint>()
	.Spatial(x => x.Shape, criteria => criteria.WithinRadius(500, 30, 30))


You can read more about spatial search in a dedicated querying article available here.



From RavenDB 2.0 the distance by default is measured in kilometers in contrast to the miles used in previous versions.