{"__v":3,"_id":"54ddf6893a91410d001b1683","category":{"__v":7,"_id":"54fdad6e660db63700c23b82","pages":["55fbef82af72eb0d0007df93","55fbf623e013770d008fcba8","5617bd3a26e3db230054fba4","5617c4a6f8c9632100ac74f5","561ed6fd0a04340d00d8a0b0","562156adf4e0352100cbf268","562a5c846049f20d0032520b"],"project":"54d0fd1d095c470d00d1646d","version":"54d0fd1e095c470d00d16470","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2015-03-09T14:25:50.133Z","from_sync":false,"order":2,"slug":"api-reference","title":"API Reference"},"project":"54d0fd1d095c470d00d1646d","user":"54db525474dfff3700f161e9","version":{"__v":20,"_id":"54d0fd1e095c470d00d16470","project":"54d0fd1d095c470d00d1646d","createdAt":"2015-02-03T16:53:50.090Z","releaseDate":"2015-02-03T16:53:50.090Z","categories":["54d0fd1e095c470d00d16471","54d8b5e68934140d00496544","54db6361c6a4d40d0034b8f7","54db638208ea010d00ab1fdd","54db639008ea010d00ab1fde","54db6547c6a4d40d0034b8fd","54db83482092743700ea6ee1","54db84afc6a4d40d0034b93c","54db8805c6a4d40d0034b93f","54db8de9c6a4d40d0034b961","54db931ac6a4d40d0034b96d","54e49219e4365521006fd9ee","54e74fcc652d7c1900cbe737","54e74ffd3c1e111700d05762","54e77e0a523b1b2f00e6f313","54e784affdabe62500fcddcf","54e784fa523b1b2f00e6f319","54e785de8ae8911900cd42c5","54fdad6e660db63700c23b82","54fdff31f7b1202100a25e06"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2015-02-13T13:05:13.057Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"auth":"required","params":[],"url":""},"isReference":false,"order":41,"body":"GeoAggregation consists to group spatial data by an relational attribute. For example, suppose we have a collection with about 700,000 companies in Florida, and we which to determine what companies kind are saturated and what companies kind has deficiencies in Florida. To do this analysis an possible first step is to segment the companies by their categories. \n\nThe **goGeo** GeoAggregation feature provide a way to group your data by some relevant collection field. For example: group Florida companies by category. The spatial restriction is mandatory to allow filtering spatially. Therefore, you must do the request using the **geom** or **points** parameters.\n\nThe GeoAggregation operation returns a JSON containing the with a list of *buckets*. Each one contains a set of documents ordered by the *doc_count*. The field *doc_count* have the number of times that the document shows up in the result.\n\nYou can download the data used in the examples bellow through the [link](https://www.dropbox.com/s/k7k73mufd1vo3bc/geo_agg_doc.csv?dl=1).\n\n---\n[block:html]\n{\n  \"html\": \"<div class='div-middle'> \\n  <a href='#'>\\n    Top page &spades; </div>\\n  </a>\\n</div>\\n\\n\\n<div class='div-forward'> \\n  <a href='/v1.0/docs/geoaggdatabase_namecollection_name'>\\n    Next &raquo; </div>\\n  </a>\\n</div>\\n\\n<style>\\n  .div-middle {\\n  \\ttext-align: center;\\n\\t\\tmargin-top: -15px;\\n  }\\n  \\n  .div-forward {\\n  \\tfloat: right;\\n    padding-right: 15px;\\n\\t\\tmargin-top: -20px;\\n  }\\n</style>\"\n}\n[/block]","excerpt":"","slug":"geo-aggregation","type":"basic","title":"GeoAggregation"}
GeoAggregation consists to group spatial data by an relational attribute. For example, suppose we have a collection with about 700,000 companies in Florida, and we which to determine what companies kind are saturated and what companies kind has deficiencies in Florida. To do this analysis an possible first step is to segment the companies by their categories. The **goGeo** GeoAggregation feature provide a way to group your data by some relevant collection field. For example: group Florida companies by category. The spatial restriction is mandatory to allow filtering spatially. Therefore, you must do the request using the **geom** or **points** parameters. The GeoAggregation operation returns a JSON containing the with a list of *buckets*. Each one contains a set of documents ordered by the *doc_count*. The field *doc_count* have the number of times that the document shows up in the result. You can download the data used in the examples bellow through the [link](https://www.dropbox.com/s/k7k73mufd1vo3bc/geo_agg_doc.csv?dl=1). --- [block:html] { "html": "<div class='div-middle'> \n <a href='#'>\n Top page &spades; </div>\n </a>\n</div>\n\n\n<div class='div-forward'> \n <a href='/v1.0/docs/geoaggdatabase_namecollection_name'>\n Next &raquo; </div>\n </a>\n</div>\n\n<style>\n .div-middle {\n \ttext-align: center;\n\t\tmargin-top: -15px;\n }\n \n .div-forward {\n \tfloat: right;\n padding-right: 15px;\n\t\tmargin-top: -20px;\n }\n</style>" } [/block]