Geospatial Queries

This page discusses Stardog’s geospatial support.

Page Contents

1. Overview
2. Enabling Geospatial Support
3. Precision & Accuracy
4. Geospatial Data
5. SPARQL Integration
6. Geospatial Datatypes
7. Enhanced Polygons

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Overview

Stardog supports geospatial queries over data encoded using WGS84 or the OGC’s GeoSPARQL vocabulary. Any RDF data stored in Stardog using one or both of these vocabularies will be automatically indexed for geospatial queries.

Geospatial: A Primer is a great place to start.

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Enabling Geospatial Support

To get started using Stardog’s geospatial support, you’ll need to create a database with geospatial support enabled. You can do this by setting the database configuration option spatial.enabled to true:

$ stardog-admin db create -o spatial.enabled=true -n mySpatialDb

Similarly in Java, you can set the option using GeospatialOptions#SPATIAL_ENABLED when creating the database programmatically:

aAdminConnection.disk("mySpatialDb")
                .set(GeospatialOptions.SPATIAL_ENABLED, true)
                .create()

Precision & Accuracy

When creating a database with geospatial support, you can specify the precision with which the features are indexed. The database property spatial.precision or programmatically via GeospatialOptions#SPATIAL_PRECISION, which can only be specified when the database is created, can control the index precision. The default value is 11 which yields sub-meter precision; a value of 8 will give a precision +/- 50m. Setting the precision value lower than the default can improve the performance of spatial queries at the cost of accuracy.

Geospatial Data

The WGS84 or OGC vocabularies can be used to encode geospatial features within your dataset. When data is committed, Stardog will look for these vocabularies and automatically extract all features and insert them into the geospatial index. Here is an example of using WKT to define the location of the White House:

:whiteHouse a geo:Feature ;
    rdfs:label "White House" ;
    geo:hasGeometry :whiteHouseGeo .

:whiteHouseGeo a geo:Geometry ;
    geo:asWKT "Point(-77.03653 38.897676 )"^^geo:wktLiteral .

Note that for WKT formatted points, the location is <long, lat>. The location of the White House can also be encoded using the WGS 84 vocabulary:

:whiteHouse a :Location ;
    rdfs:label "White House" ;
    wgs:lat "38.897676"^^xsd:float ;
    wgs:long "-77.03653"^^xsd:float .

SPARQL Integration

Once your data has been indexed, you can perform several type of geospatial queries on the data. These are seamlessly integrated into SPARQL so you can query for non-spatial information about features in your dataset alongside the geospatial queries.

The operators supported by Stardog are:

• geof:relate
• geof:distance
• geof:within
• geof:nearby
• geof:area.

The geof namespace is http://www.opengis.net/def/function/geosparql/.

This query gets all features within 2km of Stardog HQ in DC:

select ?name where {
  ?loc rdfs:label ?name .
  ?loc geo:hasGeometry ?feature .
  ?hq geo:hasGeometry ?hqGeo ; rdfs:label "Stardog HQ" .
  ?feature geof:nearby (?hqGeo 2 <http://qudt.org/vocab/unit#Kilometer>).
}

Geospatial Datatypes

The QUDT ontology, namespace http://qudt.org/vocab/unit#, is used to specify units for distances:

• Kilometer
• Meter
• Centimeter
• MileUSStatute
• Yard
• Foot
• Inch.

Additionally, the OGC units vocabulary http://www.opengis.net/def/uom/OGC/1.0/ defines degree, radian and metre.

Enhanced Polygons

Stardog’s geospatial support covers the use of basic WKT formatted shapes; specifically points and rectangles. However, WKT can encode more complex spatial structures, most notably, polygons.

To enable support for these more complex shapes, set spatial.use.jts=true in your stardog.properties file. When you restart Stardog, it will pick up JTS and you’ll be able to use more complex WKT formatted shapes. If your endpoint is in Stardog Cloud, you must set spatial.use.jts at the database level.

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