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23	23	= What data is registered in the spatial search? =
24	24
25		-Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries. Please also note that currently only datasets can be registered for which the spatial location information is regarded to be non-sensitive from a GDPR perspective.
	25	+Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries.
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27	27	= How do I query the KG Spatial Search? =
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29		-Simply use the API endpoint at [[https:~~/~~/spatial.kg.ebrains.eu/api/>>https://spatial.kg.ebrains.eu/api/]] by running queries according to the following examples:
	29	+Go to
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	31	+Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries.
31	31
32		-==== Get started: Query by a "bounding box" (hyperrectangle) ====
33	33
34		-{{code language="bash" layout="LINENUMBERS"}}
35		-curl -X 'POST' \
36		- 'https://spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects' \
37		- -H 'accept: application/json' \
38		- -H 'Content-Type: application/json' \
39		- -d '{
40		- "filters": "inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"})"
41		-}'
42		-{{/code}}
43	43
44		-As you can see, you're sending a POST request to the endpoint at **https:~/~/spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects  **with a payload defining a geometry of interest. In this case, we're looking for all objects that are **inside **a **hyperrectangle **defined by its lowest and highest point in the coordinate system of the coordinate space **AMB-CCF_v3-RAS**. This query will return you an array of ids of the objects located within the geometry which you then can use to conveniently query the KG either through the [[Instance API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/2%20Advanced/getInstancesByIds]] or the [[Query API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/1%20Basic/runDynamicQuery]] to access detailed meta information.
45		-
46		-
47		-==== Query by hypersphere ====
48		-
49		-Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres:
50		-
51		-{{code language="javascript" layout="LINENUMBERS"}}
52		-{
53		- "filters": "inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"})"
54		-}
55		-{{/code}}
56		-
57		-It is defined by the center of the sphere with its coordinates, the radius in coordinate units and the coordinate space of the given coordinates (in this case again AMB-CCF_v3-RAS)
58		-
59		-
60		-
61		-==== Advanced use: Union geometries for complex queries ====
62		-
63		-To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions:
64		-
65		-{{code language="javascript" layout="LINENUMBERS"}}
66		-{
67		- "filters": "union(union(inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"}), inside(hyperrectangle{[120.0,146.0,227.0], [150.0, 180.0, 300.0], \"AMB-CCF_v3-RAS\"})),inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"}))"
68		-}
69		-{{/code}}
70		-
71		-
72		-
73		-
74	74
75	75	)))
76	76