148 lines
4.3 KiB
Plaintext
148 lines
4.3 KiB
Plaintext
[[search-aggregations-metrics-geocentroid-aggregation]]
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=== Geo Centroid Aggregation
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A metric aggregation that computes the weighted https://en.wikipedia.org/wiki/Centroid[centroid] from all coordinate values for a <<geo-point>> field.
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Example:
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[source,js]
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--------------------------------------------------
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PUT /museums
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{
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"mappings": {
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"properties": {
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"location": {
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"type": "geo_point"
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}
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}
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}
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}
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POST /museums/_bulk?refresh
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{"index":{"_id":1}}
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{"location": "52.374081,4.912350", "city": "Amsterdam", "name": "NEMO Science Museum"}
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{"index":{"_id":2}}
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{"location": "52.369219,4.901618", "city": "Amsterdam", "name": "Museum Het Rembrandthuis"}
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{"index":{"_id":3}}
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{"location": "52.371667,4.914722", "city": "Amsterdam", "name": "Nederlands Scheepvaartmuseum"}
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{"index":{"_id":4}}
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{"location": "51.222900,4.405200", "city": "Antwerp", "name": "Letterenhuis"}
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{"index":{"_id":5}}
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{"location": "48.861111,2.336389", "city": "Paris", "name": "Musée du Louvre"}
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{"index":{"_id":6}}
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{"location": "48.860000,2.327000", "city": "Paris", "name": "Musée d'Orsay"}
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POST /museums/_search?size=0
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{
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"aggs" : {
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"centroid" : {
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"geo_centroid" : {
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"field" : "location" <1>
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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<1> The `geo_centroid` aggregation specifies the field to use for computing the centroid. (NOTE: field must be a <<geo-point>> type)
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The above aggregation demonstrates how one would compute the centroid of the location field for all documents with a crime type of burglary
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The response for the above aggregation:
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[source,js]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"centroid": {
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"location": {
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"lat": 51.009829603135586,
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"lon": 3.9662130642682314
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},
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"count": 6
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
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The `geo_centroid` aggregation is more interesting when combined as a sub-aggregation to other bucket aggregations.
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Example:
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[source,js]
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--------------------------------------------------
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POST /museums/_search?size=0
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{
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"aggs" : {
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"cities" : {
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"terms" : { "field" : "city.keyword" },
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"aggs" : {
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"centroid" : {
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"geo_centroid" : { "field" : "location" }
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[continued]
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The above example uses `geo_centroid` as a sub-aggregation to a
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<<search-aggregations-bucket-terms-aggregation, terms>> bucket aggregation
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for finding the central location for museums in each city.
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The response for the above aggregation:
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[source,js]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"cities": {
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"sum_other_doc_count": 0,
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"doc_count_error_upper_bound": 0,
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"buckets": [
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{
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"key": "Amsterdam",
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"doc_count": 3,
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"centroid": {
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"location": {
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"lat": 52.371655642054975,
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"lon": 4.9095632415264845
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},
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"count": 3
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}
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},
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{
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"key": "Paris",
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"doc_count": 2,
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"centroid": {
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"location": {
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"lat": 48.86055548675358,
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"lon": 2.331694420427084
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},
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"count": 2
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}
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},
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{
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"key": "Antwerp",
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"doc_count": 1,
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"centroid": {
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"location": {
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"lat": 51.22289997059852,
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"lon": 4.40519998781383
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},
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"count": 1
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}
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}
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]
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
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