Rules for Loc-I datasets

This page describes rules for datasets, conformance to which enables datasets to be integrated in the Loc-I index.

Loc-I principles

Loc-I focuses on providing persistent identifiers for spatial objects, and linking between locations from different datasets.

Loc-I is concerned with descriptions of geospatial entities or locations, known as features, which are described within datasets that are maintained by distinct authorities and processes. The goal is to allow the spatial relationships with features from other datasets to be easily accessible - particularly spatial containment and overlap. While these relationships might in theory be computed on-the-fly, for various reasons this is not always practical or desirable. The Loc-I index computes and assembles the relationships in advance, through various procedures which are tuned to the different sources. Some relationships between objects are inherent in the original datasets, some are computed from their spatial geometry and extent, and some arise from other considerations, such as jurisdictional arrangements. The relationships then enable various Loc-I services, such as Excelerator and IderDown

Each significant relationship between features is realised as an explicit ‘link’. Metadata is associated with each link in order to indicate where it comes from. The set of links is managed and stored separately from either the source or target data.

To support Loc-I, access to a linked-data view of each Loc-I dataset through an RDF representation of the data is required.

Core properties

Required (mandatory)

Properties required for a spatial dataset to be compatible with Loc-I:

Useful but optional properties:

All other properties are application specific, thus not normally of direct interest in the context of Loc-I.

Implementation as linked data

The Loc-I core feature model uses elements from the following standard RDF vocabularies

prefix namespace description
dcterms: http://purl.org/dc/terms/ Dublin Core terms
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# RDF
rdfs: http://www.w3.org/2000/01/rdf-schema# RDF Schema
geo: http://www.opengis.net/ont/geosparql# OGC GeoSPARQL
geox: http://linked.data.gov.au/def/geox# GeoSPARQL Extensions
loci: http://linked.data.gov.au/def/loci# Loc-I ontology
data: http://linked.data.gov.au/def/datatype/ AGLDWG Datatypes

The Loc-I core feature model implements the mandatory and option properties as follows:

  1. each geospatial feature is encoded as a geo:Feature
  2. the persistent identifier appears as
    • the value of dcterms:identifier
    • the persistent URI that identifies the feature - this must follow the Loc-I URI convention
  3. the name of each feature is provided as the value of rdfs:label
  4. the feature-type or classification is encoded as
    • rdf:type if the classifier is an rdfs:Class or owl:Class, which must be a sub-class of geo:Feature
    • dcterms:type if the classifier is something else, such as a skos:Concept
  5. the geometry is provided as the value of geo:hasGeometry
  6. the area is recorded as the value of geox:hasArea or geox:hasAreaM2
  7. spatial relations are recorded using geo:sfWithin, geo:sfContains, geo:sfOverlaps
  8. other part-whole relations may be recorded using loci:isPartOf, loci:hasPart or sub-properties of these defined in applications
  9. membership of a registered dataset is recorded using loci:isMemberOf

Essential Loc-I feature

Core model for Loc-I features. Required elements in bold. Application-specific classes and properties in grey

RDF datasets may be serialized in many ways. The Terse RDF Triple Language (Turtle) is a lightweight format that is easily editable and quite readable. JSON-LD is a JSON-based format that might be more usable in many web contexts.


An RDF dataset may be tested for conformance to the Loc-I requirements in many ways. The Shapes Constraint Language (SHACL) provides a standard, executable method. SHACL playground is a convenient tool for basic testing. pySHACL is a

The constraints on geo:Feature required for Loc-I compatibility are defined in a NodeShape for loci:Feature in the Shapes graph designed for for Loc-I. This can be used to test data for basic conformance to Loc-I requirements.


The significant benefit of having a common core is that the queries underlying standard Loc-I services are common across datasets - i.e. the SPARQL queries have no dependence on RDF predicates from application-specific vocabularies, only from rdf:, dcterms:, rdfs:, geo:, geox: and loci:.

(detailed examples TBC)

Examples showing only core properties

  rdf:type geo:Feature , asgs:StatisticalAreaLevel2 ;
  dcterms:identifier "205031088"^^asgs-id:sa2Maincode2016 ;
  rdfs:label "French Island"^^asgs-id:sa2Name2016 ;
  geo:hasGeometry <http://gds.loci.cat/geometry/asgs16_sa2/205031088> ;
  geox:hasAreaM2 [
      data:value 170229100.0 ;
    ] ;
  geo:sfContains <http://linked.data.gov.au/dataset/asgs2016/statisticalarealevel1/20503108801> ;
  geo:sfWithin <http://linked.data.gov.au/dataset/asgs2016/stateorterritory/2> ;
  geo:sfWithin <http://linked.data.gov.au/dataset/asgs2016/statisticalarealevel3/20503> ;
  loci:isMemberOf <http://linked.data.gov.au/dataset/asgs2016/statisticalarealevel2/> ;

  rdf:type geo:Feature , asgs:MeshBlock ;
  dcterms:identifier "20663970000"^^asgs-id:mbCode2016 ;
  dcterms:type asgs-cat:primary-production ;
  geo:hasGeometry <http://gds.loci.cat/geometry/asgs16_mb/20663970000> ;
  geox:hasAreaM2 [
      data:value 58387600.000000007450580596923828125 ;
    ] ;
  geo:sfWithin <http://linked.data.gov.au/dataset/asgs2016/stateorterritory/2> ;
  geo:sfWithin <http://linked.data.gov.au/dataset/asgs2016/statisticalarealevel1/20503108801> ;
  loci:isMemberOf <http://linked.data.gov.au/dataset/asgs2016/meshblock/> ;

  rdf:type geo:Feature , gnaf:Address ;
  dcterms:identifier "GAVIC411436309"^^gnaf:gnaf-2016-05 ;
  rdfs:label "Address GAVIC411436309 of Rural type" ;
  dcterms:type <http://gnafld.net/def/gnaf/code/AddressTypes#Rural> ;
  geo:hasGeometry [
      a sf:Point ;
      gnaf:gnafType <http://gnafld.net/def/gnaf/code/GeocodeTypes#PropertyAccessPointSetback> ;
      dcterms:type <http://gnafld.net/def/gnaf/code/GeocodeTypes#PropertyAccessPointSetback> ;
      geo:asWKT "<http://www.opengis.net/def/crs/EPSG/0/4283> POINT(145.35714361 -38.34785008)"^^geo:wktLiteral ;
      rdfs:label "Property Access Point Setback" ;
    ] ;
  loci:isMemberOf <http://linked.data.gov.au/dataset/gnaf-2016-05/address/> ;

  rdf:type geo:Feature , geof:DrainageDivision ;
  dcterms:identifier "9400210"^^geof:geofabric-id ;
  geo:hasGeometry <http://gds.loci.cat/geometry/geofabric2_1_1_awradrainagedivision/9400210> ;
  geox:hasAreaM2 [
      data:value 134617156547.115 ;
      <http://www.w3.org/ns/qb4st/crs> <http://www.opengis.net/def/crs/EPSG/0/3577> ;
    ] ;
  geox:hasAreaM2 [
      data:value 135039327241.81703 ;
      <http://www.w3.org/ns/qb4st/crs> <http://www.opengis.net/def/crs/EPSG/0/4938> ;
    ] ;
  loci:isMemberOf <http://linked.data.gov.au/dataset/geofabric/drainagedivision/> ;

As mentioned above, links between features are realised and stored explicitly. See this explanation of the schema for link statements.

Application schemas

An Application Schema is a data model designed to support the functional requirements of a particular application. This will involve more specific classes and properties related to the domain of discourse. Design of a application-schema is the responsibility of the data provider, in consultation with their user community.

In order to ensure that an Application Schema complies with the conformance rules above, it is recommended to use Loc-I core as the basis, and add additional classes and properties as required to support the application. Some of the classes in an application schema will be Features, and these should be axiomatized as sub-classes of geo:Feature. Other classes and properties will extend the application schema outside the scope of Loc-I, and there are no specific constraints arising from Loc-I in these cases.

See Loc-I application schemas for a basic outline of the ontologies for the initial Loc-I conformant datasets.