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22	22
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.
	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.
26	26
27	27	= How do I query the KG Spatial Search? =
28	28
29		-Go to
	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:
30	30
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.
32	32
	32	+==== Get started: Query by a "bounding box" (hyperrectangle) ====
33	33
	34	+{{code}}
	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}}
34	34
	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}}
	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}}
	66	+{
	67	+ "filters": "
	68	+ union(
	69	+ union(
	70	+ inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"}),
	71	+ inside(hyperrectangle{[120.0,146.0,227.0], [150.0, 180.0, 300.0], \"AMB-CCF_v3-RAS\"})
	72	+ ),
	73	+ inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"})
	74	+ )"
	75	+}
	76	+{{/code}}
	77	+
	78	+
	79	+
	80	+
35	35
36	36	)))
37	37