66_Geo_bounds_agg.asciidoc

geo_bounds Aggregation

In our previous example, we filtered our results by using a bounding box that covered the greater New York area. However, our results were all located in downtown Manhattan. When displaying a map for our user, it makes sense to zoom into the area of the map that contains the data; there is no point in showing lots of empty space.

The geo_bounds aggregation does exactly this: it calculates the smallest bounding box that is needed to encapsulate all of the geo-points:

GET /attractions/restaurant/_search?search_type=count
{
  "query": {
    "filtered": {
      "filter": {
        "geo_bounding_box": {
          "location": {
            "top_left": {
              "lat":  40,8,
              "lon": -74.1
            },
            "bottom_right": {
              "lat":  40.4,
              "lon": -73.9
            }
          }
        }
      }
    }
  },
  "aggs": {
    "new_york": {
      "geohash_grid": {
        "field":     "location",
        "precision": 5
      }
    },
    "map_zoom": { (1)
      "geo_bounds": {
        "field":     "location"
      }
    }
  }
}

1. The geo_bounds aggregation will calculate the smallest bounding box required to encapsulate all of the documents matching our query.

The response now includes a bounding box that we can use to zoom our map:

...
"aggregations": {
  "map_zoom": {
     "bounds": {
        "top_left": {
           "lat":  40.722,
           "lon": -74.011
        },
        "bottom_right": {
           "lat":  40.715,
           "lon": -73.983
        }
     }
  },
...

In fact, we could even use the geo_bounds aggregation inside each geohash cell, in case the geo-points inside a cell are clustered in just a part of the cell:

GET /attractions/restaurant/_search?search_type=count
{
  "query": {
    "filtered": {
      "filter": {
        "geo_bounding_box": {
          "location": {
            "top_left": {
              "lat":  40,8,
              "lon": -74.1
            },
            "bottom_right": {
              "lat":  40.4,
              "lon": -73.9
            }
          }
        }
      }
    }
  },
  "aggs": {
    "new_york": {
      "geohash_grid": {
        "field":     "location",
        "precision": 5
      },
      "aggs": {
        "cell": { (1)
          "geo_bounds": {
            "field": "location"
          }
        }
      }
    }
  }
}

1. The cell_bounds subaggregation is calculated for every geohash cell.

Now the points in each cell have a bounding box:

...
"aggregations": {
  "new_york": {
     "buckets": [
        {
           "key": "dr5rs",
           "doc_count": 2,
           "cell": {
              "bounds": {
                 "top_left": {
                    "lat":  40.722,
                    "lon": -73.989
                 },
                 "bottom_right": {
                    "lat":  40.719,
                    "lon": -73.983
                 }
              }
           }
        },
...