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- Dependencies and versions
Geo-grid processor
editGeo-grid processor
editConverts geo-grid definitions of grid tiles or cells to regular bounding boxes or polygons which describe their shape.
This is useful if there is a need to interact with the tile shapes as spatially indexable fields.
For example the geotile
field value "4/8/3"
could be indexed as a string field, but that would not enable
any spatial operations on it.
Instead, convert it to the value
"POLYGON ((0.0 40.979898069620134, 22.5 40.979898069620134, 22.5 55.77657301866769, 0.0 55.77657301866769, 0.0 40.979898069620134))"
,
which can be indexed as a geo_shape
field.
Table 21. geo_grid processor options
Name | Required | Default | Description |
---|---|---|---|
|
yes |
- |
The field to interpret as a geo-tile. The field format is determined by the |
|
yes |
- |
Three tile formats are understood: |
|
no |
|
The field to assign the polygon shape to, by default |
|
no |
- |
If specified and a parent tile exists, save that tile address to this field. |
|
no |
- |
If specified and children tiles exist, save those tile addresses to this field as an array of strings. |
|
no |
- |
If specified and intersecting non-child tiles exist, save their addresses to this field as an array of strings. |
|
no |
- |
If specified, save the tile precision (zoom) as an integer to this field. |
|
no |
- |
If |
|
no |
"GeoJSON" |
Which format to save the generated polygon in. Either |
|
no |
- |
Description of the processor. Useful for describing the purpose of the processor or its configuration. |
|
no |
- |
Conditionally execute the processor. See Conditionally run a processor. |
|
no |
|
Ignore failures for the processor. See Handling pipeline failures. |
|
no |
- |
Handle failures for the processor. See Handling pipeline failures. |
|
no |
- |
Identifier for the processor. Useful for debugging and metrics. |
To demonstrate the usage of this ingest processor, consider an index called geocells
with a mapping for a field geocell
of type geo_shape
.
In order to populate that index using geotile
and geohex
fields, define
two ingest processors:
response = client.indices.create( index: 'geocells', body: { mappings: { properties: { geocell: { type: 'geo_shape' } } } } ) puts response response = client.ingest.put_pipeline( id: 'geotile2shape', body: { description: 'translate rectangular z/x/y geotile to bounding box', processors: [ { geo_grid: { field: 'geocell', tile_type: 'geotile' } } ] } ) puts response response = client.ingest.put_pipeline( id: 'geohex2shape', body: { description: 'translate H3 cell to polygon', processors: [ { geo_grid: { field: 'geocell', tile_type: 'geohex', target_format: 'wkt' } } ] } ) puts response
PUT geocells { "mappings": { "properties": { "geocell": { "type": "geo_shape" } } } } PUT _ingest/pipeline/geotile2shape { "description": "translate rectangular z/x/y geotile to bounding box", "processors": [ { "geo_grid": { "field": "geocell", "tile_type": "geotile" } } ] } PUT _ingest/pipeline/geohex2shape { "description": "translate H3 cell to polygon", "processors": [ { "geo_grid": { "field": "geocell", "tile_type": "geohex", "target_format": "wkt" } } ] }
These two pipelines can be used to index documents into the geocells
index.
The geocell
field will be the string version of either a rectangular tile with format z/x/y
or an H3 cell address,
depending on which ingest processor we use when indexing the document.
The resulting geometry will be represented and indexed as a geo_shape
field in either
GeoJSON or the Well-Known Text format.
Example: Rectangular geotile with envelope in GeoJSON
editIn this example a geocell
field with a value defined in z/x/y
format is indexed as a
GeoJSON Envelope since the ingest-processor above was defined with default target_format
.
response = client.index( index: 'geocells', id: 1, pipeline: 'geotile2shape', body: { geocell: '4/8/5' } ) puts response response = client.get( index: 'geocells', id: 1 ) puts response
PUT geocells/_doc/1?pipeline=geotile2shape { "geocell": "4/8/5" } GET geocells/_doc/1
The response shows how the ingest-processor has replaced the geocell
field with an indexable geo_shape
:
{ "_index": "geocells", "_id": "1", "_version": 1, "_seq_no": 0, "_primary_term": 1, "found": true, "_source": { "geocell": { "type": "Envelope", "coordinates": [ [ 0.0, 55.77657301866769 ], [ 22.5, 40.979898069620134 ] ] } } }
Example: Hexagonal geohex with polygon in WKT format
editIn this example a geocell
field with an H3 string address is indexed as a
WKT Polygon, since this ingest processor explicitly
defined the target_format
.
response = client.index( index: 'geocells', id: 1, pipeline: 'geohex2shape', body: { geocell: '811fbffffffffff' } ) puts response response = client.get( index: 'geocells', id: 1 ) puts response
PUT geocells/_doc/1?pipeline=geohex2shape { "geocell": "811fbffffffffff" } GET geocells/_doc/1
The response shows how the ingest-processor has replaced the geocell
field with an indexable geo_shape
:
{ "_index": "geocells", "_id": "1", "_version": 1, "_seq_no": 0, "_primary_term": 1, "found": true, "_source": { "geocell": "POLYGON ((1.1885095294564962 49.470279179513454, 2.0265689212828875 45.18424864858389, 7.509948452934623 43.786609335802495, 12.6773177459836 46.40695743262768, 12.345747342333198 50.55427505169064, 6.259687012061477 51.964770150370896, 3.6300085578113794 50.610463307239115, 1.1885095294564962 49.470279179513454))" } }
Example: Enriched tile details
editAs described in geo_grid processor options, there are many other fields that can be set, which will enrich the information available. For example, with H3 tiles there are 7 child tiles, but only the first is fully contained by the parent. The remaining six are only partially overlapping the parent, and there exist a further six non-child tiles that overlap the parent. This can be investigated by adding parent and child additional fields to the ingest-processor:
response = client.ingest.put_pipeline( id: 'geohex2shape', body: { description: 'translate H3 cell to polygon with enriched fields', processors: [ { geo_grid: { description: "Ingest H3 cells like '811fbffffffffff' and create polygons", field: 'geocell', tile_type: 'geohex', target_format: 'wkt', target_field: 'shape', parent_field: 'parent', children_field: 'children', non_children_field: 'nonChildren', precision_field: 'precision' } } ] } ) puts response
PUT _ingest/pipeline/geohex2shape { "description": "translate H3 cell to polygon with enriched fields", "processors": [ { "geo_grid": { "description": "Ingest H3 cells like '811fbffffffffff' and create polygons", "field": "geocell", "tile_type": "geohex", "target_format": "wkt", "target_field": "shape", "parent_field": "parent", "children_field": "children", "non_children_field": "nonChildren", "precision_field": "precision" } } ] }
Index the document to see a different result:
response = client.index( index: 'geocells', id: 1, pipeline: 'geohex2shape', body: { geocell: '811fbffffffffff' } ) puts response response = client.get( index: 'geocells', id: 1 ) puts response
PUT geocells/_doc/1?pipeline=geohex2shape { "geocell": "811fbffffffffff" } GET geocells/_doc/1
The response from this index request:
{ "_index": "geocells", "_id": "1", "_version": 1, "_seq_no": 0, "_primary_term": 1, "found": true, "_source": { "parent": "801ffffffffffff", "geocell": "811fbffffffffff", "precision": 1, "shape": "POLYGON ((1.1885095294564962 49.470279179513454, 2.0265689212828875 45.18424864858389, 7.509948452934623 43.786609335802495, 12.6773177459836 46.40695743262768, 12.345747342333198 50.55427505169064, 6.259687012061477 51.964770150370896, 3.6300085578113794 50.610463307239115, 1.1885095294564962 49.470279179513454))", "children": [ "821f87fffffffff", "821f8ffffffffff", "821f97fffffffff", "821f9ffffffffff", "821fa7fffffffff", "821faffffffffff", "821fb7fffffffff" ], "nonChildren": [ "821ea7fffffffff", "82186ffffffffff", "82396ffffffffff", "821f17fffffffff", "821e37fffffffff", "82194ffffffffff" ] } }
This additional information will then enable, for example, creating a visualization of the H3 cell, its children and its intersecting non-children cells.
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