geohashing - Elasticsearch geohash_grid outlier handling -


remove outliers

how remove outliers in elasticsearch geo_hash query. i've mucked around precision , size, not correct answer scenarios. can combine query percentile aggregation? if so, can example?

here current query (for "95490" zipcode) you're seeing:

{   "aggs": {     "map_clusters": {       "aggs": {         "center_lat": {           "avg": {             "script": "doc['geo'].lat"           }         },          "center_lon": {           "avg": {             "script": "doc['geo'].lon"           }         }       },        "geohash_grid": {         "field": "geo",          "precision": 3       }     }   },    "from": 0,    "query": {     "bool": {       "must": [         {           "multi_match": {             "fields": [               "agents.listingagents.contact.officephone",                "agents.listingagents.mls.id",                "agents.listingagents.name.full",                "agents.sellingagents.contact.officephone",                "agents.sellingagents.mls.id",                "agents.sellingagents.name.full",                "offices.listingoffices.officelongname",                "offices.sellingoffices.officelongname",                "construction.exterior",                "construction.style",                "utilities.headingandcooling",                "description.miscellaneous",                "description.marketingremarks",                "primary.hoa.commonareas",                "primary.hoa.exclusiveareas",                "primary.interior.floors",                "primary.interior.kitchen",                "primary.interior.diningroom",                "primary.interior.otherrooms",                "primary.interior.fireplace",                "primary.interior.laundryappliances",                "primary.interior.specialfeatures",                "primary.interior.views",                "primary.utilities.headingandcooling",                "primary.utilities.energyconservation",                "primary.address.zipcode",                "primary.address.streetname",                "primary.address.crossstreet",                "parking.parking",                "parking.parkingfeatures",                "primary.propinfo"             ],              "operator": "and",              "query": "95490",              "type": "most_fields"           }         },          {           "geo_bounding_box": {             "geo": {               "bottom_left": {                 "lat": 38.2273928792016,                  "lon": -124.27734374999999               },                "top_right": {                 "lat": 39.58452390500424,                  "lon": -119.9652099609375               }             }           }         }       ]     }   },    "size": 20,    "sort": {     "_created": {       "order": "desc"     }   } }

fyi, outlier in image "95490" zipcode, it's geocoded incorrectly data provider.


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