{"_id":"574585e243d4d41700a19dfb","__v":8,"category":{"_id":"56e1c96b2506700e00de6e83","version":"55faeacad0e22017005b8268","__v":38,"pages":["56e1ca17cd6a8d0e00d12163","56e1cb0fe416450e00b9e485","56e1cba3cd6a8d0e00d1216c","56e1cc1ae63f910e00e5986b","56e1d0b0cd6a8d0e00d1217f","56e1d15ce416450e00b9e4a3","56e1d23292bf640e00b55663","56e1d296bc46be0e002af287","56e1d305cd6a8d0e00d1218c","56e1d373e416450e00b9e4ab","56e1d38a92bf640e00b55669","56e1d44dbc46be0e002af28e","56e2cb174cd67e220032d655","56e2cf9c1067950e006a16c8","56e2d5ff3987d729008695a0","56e2ddea8ffe6020004c53b2","56e2df768ffe6020004c53bd","56e2e1201067950e006a16f1","56e2e3888ffe6020004c53c5","56e2e53f1067950e006a1702","56e2e9773987d729008695cf","56e2ec731bffb72b00d0562e","56e2ed25b9c88f2900864aab","56e2f0314cd67e220032d6b9","56e2f3c43987d729008695ed","56e2fc3fb9c88f2900864ad7","56e2fe0d3987d7290086960b","56e30180f1ad030e00e72fa7","56e302c228f99e2000a5ffa6","56e3040c6e602e0e00700af6","56e304c5d46bc30e007bb958","56e30650d46bc30e007bb965","56e307456e602e0e00700b08","56e3086251857d0e008e778d","56e3096251857d0e008e7796","56e30a16d46bc30e007bb973","56e30aafd46bc30e007bb97c","56e30aeb51857d0e008e779f"],"project":"55faeacad0e22017005b8265","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-03-10T19:22:19.658Z","from_sync":false,"order":15,"slug":"read-query-vector-services-guide","title":"Read & Query Vector Services Guide"},"project":"55faeacad0e22017005b8265","user":"56267741db1eda0d001c3dbb","version":{"_id":"55faeacad0e22017005b8268","project":"55faeacad0e22017005b8265","__v":33,"createdAt":"2015-09-17T16:31:06.800Z","releaseDate":"2015-09-17T16:31:06.800Z","categories":["55faeacbd0e22017005b8269","55faf550764f50210095078e","55faf5b5626c341700fd9e96","55faf8a7825d5f19001fa386","560052f91503430d007cc88f","560054f73aa0520d00da0b1a","56005aaf6932a00d00ba7c62","56005c273aa0520d00da0b3f","5601ae7681a9670d006d164d","5601ae926811d00d00ceb487","5601aeb064866b1900f4768d","5601aee850ee460d0002224c","5601afa02499c119000faf19","5601afd381a9670d006d1652","561d4c78281aec0d00eb27b6","561d588d8ca8b90d00210219","563a5f934cc3621900ac278c","5665c5763889610d0008a29e","566710a36819320d000c2e93","56ddf6df8a5ae10e008e3926","56e1c96b2506700e00de6e83","56e1ccc4e416450e00b9e48c","56e1ccdfe63f910e00e59870","56e1cd10bc46be0e002af26a","56e1cd21e416450e00b9e48e","56e3139a51857d0e008e77be","573b4f62ef164e2900a2b881","57c9d1335fd8ca0e006308ed","57e2bd9d1e7b7220000d7fa5","57f2b992ac30911900c7c2b6","58adb5c275df0f1b001ed59b","58c81b5c6dc7140f003c3c46","595412446ed4d9001b3e7b37"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"v1","version_clean":"1.0.0","version":"1"},"parentDoc":null,"updates":[],"next":{"pages":[],"description":""},"createdAt":"2016-05-25T11:00:50.091Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":2,"body":"| Table of Contents |\n| --- |\n| [Aggregations with Bounding Box](#section-aggregations-with-bounding-box) |\n| [Aggregations with Shape](#section-aggregations-with-shape) |\n| [ESRI Vector Services with Bounding Box](#section-esri-vector-services-with-bounding-box) |\n| [ESRI Vector Services with Shape](#section-esri-vector-services-with-shape) |\n| [Facets Search with Bounding Box](#section-facets-search-with-bounding-box) |\n| [General Vector Services with Bounding Box](#section-general-vector-services-with-bounding-box) |\n| [General Vector Services with Shape](#section-general-vector-services-with-shape) |\n| [Index Query with Bounding Box](#section-index-query-with-bounding-box) |\n| [Index Query with Shape](#section-index-query-with-shape) |\n\n#Aggregations with Bounding Box#\nFor more information on Aggregations api parameters and definitions, check the [Vector Services Aggregations Reference Overview](doc:vector-services-aggregations-reference-overview) page.\n\n1. We want to see the aggregation of all of the sources with building vectors available within a given region of Washington, DC, including all variations, from January 1, 2016 to February 1, 2016.\nThere are two approaches to handling this query. In both cases, however, the user is still looking at a base aggregation query along the lines of\n - `aggs=geohash:4;terms:ingest_source`\n**Note**: The `geohash` value used should be chosen based on the size of the aoi/the granular detail desired. Too fine of a `geohash` over too large of an area, and the time required to process the request will drastically increase. Experiment with `geohash` value vs aoi size to find the correct balance for your needs.\nOne approach involves applying the date and time specifications in the query directly. The `query` itself may be composed in several different fashions, each one leading to the same result.\n - `query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]`\n - `query=+item_type.analyzed:building +item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]`\nThe API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=1000\n```\nThe next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following:\n -  `query=item_type.analyzed:building`\nAnd the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=2016-01-01T00:00:00.000Z&endDate=2016-02-01T00:00:00.000Z&count=1000\n```\nBoth cases return the same results. (See [Aggregation Query Call](doc:aggs-query).)\n\n2. We want to see the aggregation of the number of distinct screen names, as well as the top five screen names themselves, of the top twitter posters within a given region of Washington, DC.\nAs with Example 1, in this example there are two approaches to handling this query. Again, in both cases the user is still looking at a base aggregation query along the lines of\n - aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw)\nOne approach involves applying the date and time specifications in the `query` directly. The `query` itself may be composed in several different fashions, each one leading to the same result.\n - query=item_type:tweet AND item_date:[now-2M TO now]\n - query=+item_type:tweet +item_date:[now-2M TO now]\nThe API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw)&query=item_type:tweet AND item_date:[now-2M TO now]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=5\n```\nThe next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following:\n - `query=item_type:tweet`\nAnd the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw)&query=item_type:tweet&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=now-2M&endDate=now&count=5\n```\n(See [Aggregation Query Call](doc:aggs-query).)\n\n3. We want to see the aggregation of the sum of cars found in a given region of Washington, DC, extracted by a GBDX workflow from a specific image. With this, we are interested in a numeric value instead of  a specific name, and so the aggregation query would be more along the lines of\n - `aggs=geohash:4;sum: attributes.count_int`\nSince we are looking for the extracted items from a specific workflow, we also know the workflow run_id, which we specify in the `query` itself:\n - `ingest_attributes.run_id_raw:764fd5fb-97f1-4628-9ee7-1169a7c7cd5d`\nThe API call would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;sum: attributes.count_int&query=ingest_attributes.run_id_raw:764fd5fb-97f1-4628-9ee7-1169a7c7cd5d&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=100\n```\n(See [Aggregation Query Call](doc:aggs-query).)\n\n\n#Aggregations with Shape#\nFor more information on Aggregations api parameters and definitions, check the [Vector Services Aggregations Reference Overview](doc:vector-services-aggregations-reference-overview) page.\n\n1. We want to see the aggregation of all of the sources with building vectors available within Washington, DC, including all variations, from January 1, 2016 to February 1, 2016.\nThe primary difference between the bounding box and shape aggregation queries is that shape queries require the aoi to be identified in the body. So, for a query over all of Washington, DC, the Body of the API call will be the [json provided](#section-shape-json).\nThe basic query setup for shape aggregations is the same as that of bounding box aggregations, with two approaches to handling this query. And again, in both cases, the user is looking at a base aggregation query along the lines of\n - aggs=geohash:6;terms:ingest_source\n**Note**: The `geohash` value used should be chosen based on the size of the aoi/the granular detail desired. Too fine of a `geohash` over too large of an area, and the time required to process the request will drastically increase. Experiment with `geohash` value vs aoi size to find the correct balance for your needs.\nOne approach involves applying the date and time specifications in the `query` directly. The `query` itself may be composed in several different fashions, each one leading to the same result.\n - `query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]`\n - `query=+item_type.analyzed:building +item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]`\nThe API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]&count=1000\n```\nWith the [json provided](#section-shape-json) included in the Body.\n\nThe next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following:\n -  `query=item_type.analyzed:building`\nAnd the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building&startDate=2016-01-01T00:00:00.000Z&endDate=2016-02-01T00:00:00.000Z&count=1000\n```\nWith the [json provided](#section-shape-json) included in the Body.\n\nBoth cases return the same results. (See [Aggregation Query Call](doc:aggs-shape-query).)\n\n\n#ESRI Vector Services with Bounding Box#\nFor more information on ESRI API parameters and limits, check the [Vector Services ESRI Reference Overview](doc:vector-services-esri-reference-overview) page.\n\n1. We want to see *OSM Library and School* vectors that are within a given region of Washington, DC. The query setup can be generated in several ways:\n - `ingest_source:OSM AND item_type:(Library OR School)`\n - `ingest_source:OSM AND (item_type:Library OR item_type:School)`\n - `+ingest_source:OSM +(item_type:Library item_type:School)`\nGoing through the user interface, copy/pasting any of the above setups will return the same results.\nIn the API, the query would look similar to the following for a paging call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/esri/query/paging?q=ingest_source:OSM AND item_type:(Library OR School)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500\n```\n(See [Get Paging ID](doc:esri-basic-get-paging-id).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call):\n```\nhttps://vector.geobigdata.io/insight-vector/api/esri/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:esri-retrieve-page-of-vector-items).)\n\n\n#ESRI Vector Services with Shape#\nFor more information on Shape API parameters and limits, check the [Vector Services Shape Reference Overview](doc:vector-services-shape-reference-overview) page.\n\nSee [General Vector Services with Shape](#section-general-vector-services-with-shape) - Shape queries are handled the same way between general calls and ESRI.\n\n\n#Facets Search with Bounding Box#\nFor more information on Facet API parameters and limits, check the [Vector Services Facets Reference Overview](doc:vector-services-facets-reference-overview) page.\n\n1. We want to see what types of vectors and how many of each type is available within a given region of Washington, DC over the last two days, limited to the top 10 types. The query setup involves separating out the date and time specifications into the `startDate` and `endDate` parameters. To discover vector types, we know we are searching through the `item_type`. The API call for this case would look similar to the following:\n```\nhttps://vector.geobigdata.io/insight-vector/api/facets?fields=item_type&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=now-2d&endDate=now&count=10\n```\n\nNote: This call **will not** give you individual vectors. This call helps to guide you toward the vector items you will eventually want to see.\n\n(See [Facet Search](doc:facets-search).)\n\n\n#General Vector Services with Bounding Box#\nFor more information on General API parameters and limits, check the [Vector Services General Reference Overview](doc:vector-services-general-reference-overview) page.\n\n1. We want to see *OSM Library and School* vectors that are within a given region in Washington, DC. Since we are looking specifically for OSM vector items, we know the `ingest_source` to query on, and, since we are specifically looking for libraries and schools, we also know the `item_type`. The query setup can be generated in several ways:\n - `ingest_source:OSM AND item_type:(Library OR School)`\n - `ingest_source:OSM AND (item_type:Library OR item_type:School)`\n - `+ingest_source:OSM +(item_type:Library item_type:School)`\nGoing through the user interface, copy/pasting any of the above setups will return the same results.\nIn the API, the query would look similar to the following for a paging call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=ingest_source:OSM AND item_type:(Library OR School)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500\n```\n(See [Get Paging ID](doc:vs-query-get-paging-id).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call):\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n\n2. We want to see all of the *rail and road* vectors available within a given region in Washington, DC, including all variations, that were generated *in the last month*. In this case, we are not limited by a specific source, and we are looking to include potential capitalization differences as well as other vectors that *include* the word rail or the word road. The query setup can be generated in several ways:\n - `item_date:[now-1M TO now] AND item_type.analyzed:(rail OR road)`\n - `item_date:[now-1M TO now] AND (item_type.analyzed:rail OR item_type.analyzed:road)`\n - `+item_date:[now-1M TO now] +(item_type.analyzed:rail item_type.analyzed:road)`\nNote that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).)\nGoing through the user interface, copy/pasting any of the above setups will return the same results.\nIn the API, the query would look similar to the following for a paging call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=item_date:[now-1M TO now] AND (item_type.analyzed:rail OR item_type.analyzed:road)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500\n```\n(See [Get Paging ID](doc:vs-query-get-paging-id).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call):\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n\n3. We want to see *DG Catalog v2* vectors *within the last 30 days* within a given region in Washington, DC. The query setup can be generated in a couple of ways:\n - `ingest_source:GBDX_INGEST_ALPHA AND item_date:[now-30d TO now]`\n - `+ingest_source:GBDX_INGEST_ALPHA +item_date:[now-30d TO now]`\nNote that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).)\nGoing through the user interface, copy/pasting either of the above setups will return the same results.\nIn the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call (this is a GET call):\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=ingest_source:GBDX_INGEST_ALPHA AND item_date:[now-30d TO now]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500\n```\n(See [Get Paging ID](doc:vs-query-get-paging-id).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call):\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n\n#General Vector Services with Shape#\nFor more information on Shape API parameters and limits, check the [Vector Services Shape Reference Overview](doc:vector-services-shape-reference-overview) page.\n\n1. We want to see vectors of *helicopter* type *within the last 3 weeks* within Washington, DC. The query setup can be generated in a couple of ways:\n - `item_type:helicopter AND item_date:[now-3w TO now]`\n - `+item_type:helicopter +item_date:[now-3w TO now]`\nNote that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).)\nGoing through the user interface, copy/pasting either of the above setups will return the same results.\nIn the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/shape/paging?q=item_type:helicopter AND item_date:[now-3w TO now]&ttl=5m&count=500\n```\nWith the [json provided](#section-shape-json) included in the Body.\n(See [Get Paging ID](doc:shape-query-get-paging-id).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items:\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n#Index Query with Bounding Box#\nFor more information on Index Query API parameters and limits, check the [Vector Services Index Query Reference Overview](doc:vector-services-index-query-reference-overview) page.\n\n1. We know that the index we want to query on is *vector-oiltanks-20170109* and that want to see oil tank vectors of that were derived from the catalog acquisition *1030010055AD4300* within a given region of Iceland. The query setup can be generated in a couple of ways:\n - `attributes.cat_id_raw:1030010055AD4300`\n - `+attributes.cat_id_raw:1030010055AD4300`\nIn the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/index/query/vector-oiltanks-20170109/paging?q=attributes.cat_id_raw:1030010055AD4300&left=-22.362671&right=-21.544189&upper=64.194423&lower=64.036149&ttl=5m&count=500\n```\n(See [Get Paging ID](doc:index-query-get-paging-id-for-bounding-box).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items:\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n\n#Index Query with Shape#\nFor more information on Index Query API parameters and limits, check the [Vector Services Index Query Reference Overview](doc:vector-services-index-query-reference-overview) page.\n\n1. We know that the index we want to query on is *vector-oiltanks-20170109* and that want to see oil tank vectors of that were derived from the catalog acquisition *1030010003A3D500* within Washington, DC. The query setup can be generated in a couple of ways:\n - `attributes.cat_id_raw:1030010003A3D500`\n - `+attributes.cat_id_raw:1030010003A3D500`\nIn the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call:\n```\nhttps://vector.geobigdata.io/insight-vector/api/index/query/vector-oiltanks-20170109/paging?q=attributes.cat_id_raw:1030010003A3D500&ttl=5m&count=500\n```\nWith the [json provided](#section-shape-json) included in the Body.\n(See [Get Paging ID](doc:index-query-get-paging-id-for-shape).)\nUsing the paging ID from the previous call, you would then submit a call to retrieve pages of vector items:\n```\nhttps://vector.geobigdata.io/insight-vector/api/vectors/paging\n```\nWhere the paging ID is placed within the body of the call.\n(See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).)\n\n\n###Shape JSON###\nNote: All example shape queries will use Washington, DC, as the aoi, depicted below:\n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"{\\n    \\\"type\\\": \\\"Polygon\\\",\\n    \\\"coordinates\\\": [\\n          [\\n            [\\n              -77.119759000000002,\\n              38.934342999999998\\n            ],\\n            [\\n              -77.112531800026687,\\n              38.940058356319398\\n            ],\\n            [\\n              -77.112478301097397,\\n              38.940100663913604\\n            ],\\n            [\\n              -77.104500000000002,\\n              38.94641\\n            ],\\n            [\\n              -77.101277750604197,\\n              38.948529900918302\\n            ],\\n            [\\n              -77.100700000000003,\\n              38.948909999999998\\n            ],\\n            [\\n              -77.100526969660208,\\n              38.949052938106803\\n            ],\\n            [\\n              -77.098089871993395,\\n              38.951066192701099\\n            ],\\n            [\\n              -77.093866059016392,\\n              38.954555429508197\\n            ],\\n            [\\n              -77.091500000000011,\\n              38.956510000000002\\n            ],\\n            [\\n              -77.091399387458097,\\n              38.956587350573898\\n            ],\\n            [\\n              -77.091355285244887,\\n              38.956621256202702\\n            ],\\n            [\\n              -77.085812268288393,\\n              38.9608827084474\\n            ],\\n            [\\n              -77.085780121691698,\\n              38.9609074226397\\n            ],\\n            [\\n              -77.083839171090702,\\n              38.9623996187416\\n            ],\\n            [\\n              -77.082706082725707,\\n              38.9632707331536\\n            ],\\n            [\\n              -77.0822402534113,\\n              38.963628861117598\\n            ],\\n            [\\n              -77.0804181742635,\\n              38.965029669257902\\n            ],\\n            [\\n              -77.077441665345091,\\n              38.967317999008898\\n            ],\\n            [\\n              -77.077436698207094,\\n              38.967321817727395\\n            ],\\n            [\\n              -77.071640984008695,\\n              38.971777542736795\\n            ],\\n            [\\n              -77.069563580871105,\\n              38.973374643076397\\n            ],\\n            [\\n              -77.068951003066402,\\n              38.973845590771795\\n            ],\\n            [\\n              -77.066690519618689,\\n              38.9755834426199\\n            ],\\n            [\\n              -77.062564652888398,\\n              38.9787553946774\\n            ],\\n            [\\n              -77.054299,\\n              38.985109999999999\\n            ],\\n            [\\n              -77.052937499620796,\\n              38.986133684495599\\n            ],\\n            [\\n              -77.052220061432294,\\n              38.986673111705002\\n            ],\\n            [\\n              -77.051662504310286,\\n              38.987092327586197\\n            ],\\n            [\\n              -77.046541251074387,\\n              38.990942893929002\\n            ],\\n            [\\n              -77.042388998663696,\\n              38.994064888222802\\n            ],\\n            [\\n              -77.040998999999999,\\n              38.995109999999997\\n            ],\\n            [\\n              -77.036769251411599,\\n              38.992050181872202\\n            ],\\n            [\\n              -77.036461350189299,\\n              38.991827444817801\\n            ],\\n            [\\n              -77.036299,\\n              38.991709999999998\\n            ],\\n            [\\n              -77.033707359689004,\\n              38.989731935321302\\n            ],\\n            [\\n              -77.031234921812697,\\n              38.987844851680599\\n            ],\\n            [\\n              -77.026696218486805,\\n              38.984380694753405\\n            ],\\n            [\\n              -77.026634552895203,\\n              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          -77.008297999999996,\\n              38.970109999999998\\n            ],\\n            [\\n              -77.007774574197001,\\n              38.969685844607895\\n            ],\\n            [\\n              -77.002601759533803,\\n              38.965494081001602\\n            ],\\n            [\\n              -77.002589220168701,\\n              38.9654839197919\\n            ],\\n            [\\n              -77.002498000000003,\\n              38.965409999999999\\n            ],\\n            [\\n              -77.001649914165299,\\n              38.964754464201398\\n            ],\\n            [\\n              -76.999124025830696,\\n              38.962802055425001\\n            ],\\n            [\\n              -76.997492710321396,\\n              38.961541114997097\\n            ],\\n            [\\n              -76.997364490213485,\\n              38.961442006077498\\n            ],\\n            [\\n              -76.991892520544198,\\n              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            -76.963435789814085,\\n              38.935216502945394\\n            ],\\n            [\\n              -76.963430253918403,\\n              38.9352122239235\\n            ],\\n            [\\n              -76.961374166883587,\\n              38.933622952412001\\n            ],\\n            [\\n              -76.958748660684705,\\n              38.931593543084901\\n            ],\\n            [\\n              -76.957718618064007,\\n              38.930797362103796\\n            ],\\n            [\\n              -76.957704907252193,\\n              38.930786764204797\\n            ],\\n            [\\n              -76.957531362067499,\\n              38.930652620847297\\n            ],\\n            [\\n              -76.956050960924685,\\n              38.929508331076498\\n            ],\\n            [\\n              -76.943957421306891,\\n              38.920160517650395\\n            ],\\n            [\\n              -76.941921067989597,\\n              38.918586499498396\\n            ],\\n            [\\n              -76.941920456206901,\\n              38.918586026615294\\n            ],\\n            [\\n              -76.935096000000001,\\n              38.913311\\n            ],\\n            [\\n              -76.931791668782893,\\n              38.910675480540895\\n            ],\\n            [\\n              -76.931565014869491,\\n              38.910494702416599\\n            ],\\n            [\\n              -76.931251468051201,\\n              38.910244618909005\\n            ],\\n            [\\n              -76.927557561849397,\\n              38.907298376224695\\n            ],\\n            [\\n              -76.923529268146595,\\n              38.904085427599497\\n            ],\\n            [\\n              -76.923451063194094,\\n              38.904023051687297\\n            ],\\n            [\\n              -76.9199019174646,\\n              38.901192269293205\\n            ],\\n            [\\n              -76.913413374932091,\\n              38.896017037458904\\n            ],\\n            [\\n              -76.912868951119108,\\n              38.895582807516796\\n            ],\\n            [\\n              -76.912298639399395,\\n              38.895127929498798\\n            ],\\n            [\\n              -76.910955802208107,\\n              38.894056888699396\\n            ],\\n            [\\n              -76.910850679128799,\\n              38.893973043012394\\n            ],\\n            [\\n              -76.909395000000004,\\n              38.892811999999999\\n            ],\\n            [\\n              -76.910794999999993,\\n              38.891711999999998\\n            ],\\n            [\\n              -76.912378388448388,\\n              38.890482545440101\\n            ],\\n            [\\n              -76.913262575757599,\\n              38.889795999999997\\n            ],\\n            [\\n              -76.913303391762398,\\n              38.889764307572698\\n            ],\\n            [\\n              -76.918535662710696,\\n              38.885701603071695\\n            ],\\n            [\\n              -76.918550521543892,\\n              38.885690065624694\\n            ],\\n            [\\n              -76.919294999999991,\\n              38.885111999999999\\n            ],\\n            [\\n              -76.920194999999993,\\n              38.884411999999998\\n            ],\\n            [\\n              -76.920825111103696,\\n              38.883918607691399\\n            ],\\n            [\\n              -76.921584694546695,\\n              38.8833238353944\\n            ],\\n            [\\n              -76.923972437651798,\\n              38.8814541745108\\n            ],\\n            [\\n              -76.924034950179788,\\n              38.881405225682002\\n            ],\\n            [\\n              -76.9298866240579,\\n              38.876823222814899\\n            ],\\n            [\\n              -76.933718878799695,\\n              38.873822474211899\\n            ],\\n            [\\n              -76.933804814136295,\\n              38.873755184754799\\n            ],\\n            [\\n              -76.934971325738687,\\n              38.872841777818898\\n            ],\\n            [\\n              -76.935944196395397,\\n              38.872079996449799\\n            ],\\n            [\\n              -76.93949014374229,\\n              38.869303433495595\\n            ],\\n            [\\n              -76.943641272331504,\\n              38.866052998919997\\n            ],\\n            [\\n              -76.946701967384385,\\n              38.8636564003058\\n            ],\\n            [\\n              -76.946746183955085,\\n              38.863621777656192\\n            ],\\n            [\\n              -76.949696000000003,\\n              38.861311999999998\\n            ],\\n            [\\n              -76.949860966442898,\\n              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        -76.972326515878294,\\n              38.843564839863603\\n            ],\\n            [\\n              -76.972843853797698,\\n              38.843149782504099\\n            ],\\n            [\\n              -76.979496999999995,\\n              38.837812\\n            ],\\n            [\\n              -76.982490980881394,\\n              38.835634786175696\\n            ],\\n            [\\n              -76.984969314303896,\\n              38.833832549620901\\n            ],\\n            [\\n              -76.987330649544404,\\n              38.8321153937896\\n            ],\\n            [\\n              -76.989040541131402,\\n              38.830871965809003\\n            ],\\n            [\\n              -76.991343739779794,\\n              38.829197086731398\\n            ],\\n            [\\n              -76.992696999999993,\\n              38.828212999999998\\n            ],\\n            [\\n              -76.992802935577501,\\n              38.828121576145399\\n            ],\\n            [\\n              -76.995947329318099,\\n              38.825407921273396\\n            ],\\n            [\\n              -76.998458490566009,\\n              38.823240754716998\\n            ],\\n            [\\n              -76.999130853194188,\\n              38.822660496558399\\n            ],\\n            [\\n              -76.999996999999993,\\n              38.821912999999995\\n            ],\\n            [\\n              -77.001396999999997,\\n              38.821512999999996\\n            ],\\n            [\\n              -77.001481076298305,\\n              38.821446228991007\\n            ],\\n            [\\n              -77.001491820919298,\\n              38.821437695918007\\n            ],\\n            [\\n              -77.006821877075296,\\n              38.8172047166507\\n            ],\\n            [\\n              -77.024423651667504,\\n              38.803225887021597\\n            ],\\n            [\\n              -77.037579715256499,\\n              38.792777714848\\n            ],\\n            [\\n              -77.039006000000001,\\n              38.791644999999995\\n            ],\\n            [\\n              -77.03899278543409,\\n              38.792766769814001\\n            ],\\n            [\\n              -77.038969617554301,\\n              38.794733465389996\\n            ],\\n            [\\n              -77.038929172245602,\\n              38.798166822709696\\n            ],\\n            [\\n              -77.038904082038201,\\n              38.800296702537096\\n            ],\\n            [\\n              -77.038898000000003,\\n              38.800812999999998\\n            ],\\n            [\\n              -77.038800581262493,\\n              38.801657927494098\\n            ],\\n            [\\n              -77.038195752156,\\n              38.806903702118099\\n            ],\\n            [\\n              -77.037999808574014,\\n              38.808603150538801\\n            ],\\n            [\\n              -77.037446963170709,\\n              38.8133980626165\\n            ],\\n            [\\n              -77.037356000000003,\\n              38.814186999999997\\n            ],\\n            [\\n              -77.038097999999991,\\n              38.815612999999999\\n            ],\\n            [\\n              -77.038788344509499,\\n              38.819616998155098\\n            ],\\n            [\\n              -77.039097999999996,\\n              38.821413\\n            ],\\n            [\\n              -77.038097999999991,\\n              38.828612\\n            ],\\n            [\\n              -77.039198999999996,\\n              38.832211999999998\\n            ],\\n            [\\n              -77.041198999999992,\\n              38.833711999999998\\n            ],\\n            [\\n              -77.042598999999996,\\n              38.833812000000002\\n            ],\\n            [\\n              -77.043498999999997,\\n              38.833211999999996\\n            ],\\n            [\\n              -77.044899000000001,\\n              38.834711999999996\\n            ],\\n            [\\n              -77.04499899999999,\\n              38.838512000000001\\n            ],\\n            [\\n              -77.044487611898006,\\n              38.839598699716696\\n            ],\\n            [\\n              -77.044198999999992,\\n              38.840212000000001\\n            ],\\n            [\\n              -77.041698999999994,\\n              38.840212000000001\\n            ],\\n            [\\n              -77.038549000000003,\\n              38.839261999999998\\n            ],\\n            [\\n              -77.03454099999999,\\n              38.840472999999996\\n            ],\\n            [\\n              -77.031697999999992,\\n              38.850511999999995\\n            ],\\n            [\\n              -77.031831738380092,\\n              38.850896512341599\\n            ],\\n            [\\n              -77.034003999999996,\\n              38.857141999999996\\n            ],\\n            [\\n              -77.039299,\\n              38.864311999999998\\n            ],\\n            [\\n              -77.038899000000001,\\n              38.865811999999998\\n            ],\\n            [\\n              -77.039098999999993,\\n              38.868111999999996\\n            ],\\n            [\\n              -77.040599,\\n              38.871212\\n            ],\\n            [\\n              -77.041589912408796,\\n              38.872239612868299\\n            ],\\n            [\\n              -77.04329899999999,\\n              38.874012\\n            ],\\n            [\\n              -77.043454092682893,\\n              38.874100624390202\\n            ],\\n            [\\n              -77.045399000000003,\\n              38.875211999999998\\n            ],\\n            [\\n              -77.046599000000001,\\n              38.874911999999995\\n            ],\\n     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-77.079297390379892,\\n              38.9016231616772\\n            ],\\n            [\\n              -77.0822,\\n              38.901910999999998\\n            ],\\n            [\\n              -77.082839211732292,\\n              38.902094773373101\\n            ],\\n            [\\n              -77.09020000000001,\\n              38.904210999999997\\n            ],\\n            [\\n              -77.093699999999998,\\n              38.905910999999996\\n            ],\\n            [\\n              -77.10039698865171,\\n              38.9105542454652\\n            ],\\n            [\\n              -77.101015593108386,\\n              38.910983144555203\\n            ],\\n            [\\n              -77.101200000000006,\\n              38.911110999999998\\n            ],\\n            [\\n              -77.103400000000008,\\n              38.912911000000001\\n            ],\\n            [\\n              -77.105002725079999,\\n              38.916337515688397\\n            ],\\n            [\\n              -77.106300000000005,\\n              38.919111000000001\\n            ],\\n            [\\n              -77.107360506400894,\\n              38.920022139302198\\n            ],\\n            [\\n              -77.113399999999999,\\n              38.925210999999997\\n            ],\\n            [\\n              -77.116600000000005,\\n              38.928910999999999\\n            ],\\n            [\\n              -77.116816261811707,\\n              38.929493243339202\\n            ],\\n            [\\n              -77.117900000000006,\\n              38.932410999999995\\n            ],\\n            [\\n              -77.119759000000002,\\n              38.934342999999998\\n            ]\\n          ]\\n        ]\\n      }\",\n      \"language\": \"json\"\n    }\n  ]\n}\n[/block]","excerpt":"Query construction for both the UI and the API call that would result","slug":"vector-services-example-query-constructions","type":"basic","title":"Vector Services Example Query Constructions"}

Vector Services Example Query Constructions

Query construction for both the UI and the API call that would result

| Table of Contents | | --- | | [Aggregations with Bounding Box](#section-aggregations-with-bounding-box) | | [Aggregations with Shape](#section-aggregations-with-shape) | | [ESRI Vector Services with Bounding Box](#section-esri-vector-services-with-bounding-box) | | [ESRI Vector Services with Shape](#section-esri-vector-services-with-shape) | | [Facets Search with Bounding Box](#section-facets-search-with-bounding-box) | | [General Vector Services with Bounding Box](#section-general-vector-services-with-bounding-box) | | [General Vector Services with Shape](#section-general-vector-services-with-shape) | | [Index Query with Bounding Box](#section-index-query-with-bounding-box) | | [Index Query with Shape](#section-index-query-with-shape) | #Aggregations with Bounding Box# For more information on Aggregations api parameters and definitions, check the [Vector Services Aggregations Reference Overview](doc:vector-services-aggregations-reference-overview) page. 1. We want to see the aggregation of all of the sources with building vectors available within a given region of Washington, DC, including all variations, from January 1, 2016 to February 1, 2016. There are two approaches to handling this query. In both cases, however, the user is still looking at a base aggregation query along the lines of - `aggs=geohash:4;terms:ingest_source` **Note**: The `geohash` value used should be chosen based on the size of the aoi/the granular detail desired. Too fine of a `geohash` over too large of an area, and the time required to process the request will drastically increase. Experiment with `geohash` value vs aoi size to find the correct balance for your needs. One approach involves applying the date and time specifications in the query directly. The `query` itself may be composed in several different fashions, each one leading to the same result. - `query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]` - `query=+item_type.analyzed:building +item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]` The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=1000 ``` The next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following: - `query=item_type.analyzed:building` And the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=2016-01-01T00:00:00.000Z&endDate=2016-02-01T00:00:00.000Z&count=1000 ``` Both cases return the same results. (See [Aggregation Query Call](doc:aggs-query).) 2. We want to see the aggregation of the number of distinct screen names, as well as the top five screen names themselves, of the top twitter posters within a given region of Washington, DC. As with Example 1, in this example there are two approaches to handling this query. Again, in both cases the user is still looking at a base aggregation query along the lines of - aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw) One approach involves applying the date and time specifications in the `query` directly. The `query` itself may be composed in several different fashions, each one leading to the same result. - query=item_type:tweet AND item_date:[now-2M TO now] - query=+item_type:tweet +item_date:[now-2M TO now] The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw)&query=item_type:tweet AND item_date:[now-2M TO now]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=5 ``` The next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following: - `query=item_type:tweet` And the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;(cardinality:attributes.actor_displayName_raw,terms:attributes.actor_displayName_raw)&query=item_type:tweet&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=now-2M&endDate=now&count=5 ``` (See [Aggregation Query Call](doc:aggs-query).) 3. We want to see the aggregation of the sum of cars found in a given region of Washington, DC, extracted by a GBDX workflow from a specific image. With this, we are interested in a numeric value instead of a specific name, and so the aggregation query would be more along the lines of - `aggs=geohash:4;sum: attributes.count_int` Since we are looking for the extracted items from a specific workflow, we also know the workflow run_id, which we specify in the `query` itself: - `ingest_attributes.run_id_raw:764fd5fb-97f1-4628-9ee7-1169a7c7cd5d` The API call would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;sum: attributes.count_int&query=ingest_attributes.run_id_raw:764fd5fb-97f1-4628-9ee7-1169a7c7cd5d&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=100 ``` (See [Aggregation Query Call](doc:aggs-query).) #Aggregations with Shape# For more information on Aggregations api parameters and definitions, check the [Vector Services Aggregations Reference Overview](doc:vector-services-aggregations-reference-overview) page. 1. We want to see the aggregation of all of the sources with building vectors available within Washington, DC, including all variations, from January 1, 2016 to February 1, 2016. The primary difference between the bounding box and shape aggregation queries is that shape queries require the aoi to be identified in the body. So, for a query over all of Washington, DC, the Body of the API call will be the [json provided](#section-shape-json). The basic query setup for shape aggregations is the same as that of bounding box aggregations, with two approaches to handling this query. And again, in both cases, the user is looking at a base aggregation query along the lines of - aggs=geohash:6;terms:ingest_source **Note**: The `geohash` value used should be chosen based on the size of the aoi/the granular detail desired. Too fine of a `geohash` over too large of an area, and the time required to process the request will drastically increase. Experiment with `geohash` value vs aoi size to find the correct balance for your needs. One approach involves applying the date and time specifications in the `query` directly. The `query` itself may be composed in several different fashions, each one leading to the same result. - `query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]` - `query=+item_type.analyzed:building +item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]` The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building AND item_date:[2016-01-01T00:00:00.000Z TO 2016-02-01T00:00:00.000Z]&count=1000 ``` With the [json provided](#section-shape-json) included in the Body. The next approach involves separating out the date and time specifications into the `startDate` and `endDate` parameters. In this case, the `query` is truncated to the following: - `query=item_type.analyzed:building` And the date portion of the `query` is moved into the two separate parameters. The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/aggregation?aggs=geohash:4;terms:ingest_source&query=item_type.analyzed:building&startDate=2016-01-01T00:00:00.000Z&endDate=2016-02-01T00:00:00.000Z&count=1000 ``` With the [json provided](#section-shape-json) included in the Body. Both cases return the same results. (See [Aggregation Query Call](doc:aggs-shape-query).) #ESRI Vector Services with Bounding Box# For more information on ESRI API parameters and limits, check the [Vector Services ESRI Reference Overview](doc:vector-services-esri-reference-overview) page. 1. We want to see *OSM Library and School* vectors that are within a given region of Washington, DC. The query setup can be generated in several ways: - `ingest_source:OSM AND item_type:(Library OR School)` - `ingest_source:OSM AND (item_type:Library OR item_type:School)` - `+ingest_source:OSM +(item_type:Library item_type:School)` Going through the user interface, copy/pasting any of the above setups will return the same results. In the API, the query would look similar to the following for a paging call: ``` https://vector.geobigdata.io/insight-vector/api/esri/query/paging?q=ingest_source:OSM AND item_type:(Library OR School)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500 ``` (See [Get Paging ID](doc:esri-basic-get-paging-id).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call): ``` https://vector.geobigdata.io/insight-vector/api/esri/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:esri-retrieve-page-of-vector-items).) #ESRI Vector Services with Shape# For more information on Shape API parameters and limits, check the [Vector Services Shape Reference Overview](doc:vector-services-shape-reference-overview) page. See [General Vector Services with Shape](#section-general-vector-services-with-shape) - Shape queries are handled the same way between general calls and ESRI. #Facets Search with Bounding Box# For more information on Facet API parameters and limits, check the [Vector Services Facets Reference Overview](doc:vector-services-facets-reference-overview) page. 1. We want to see what types of vectors and how many of each type is available within a given region of Washington, DC over the last two days, limited to the top 10 types. The query setup involves separating out the date and time specifications into the `startDate` and `endDate` parameters. To discover vector types, we know we are searching through the `item_type`. The API call for this case would look similar to the following: ``` https://vector.geobigdata.io/insight-vector/api/facets?fields=item_type&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&startDate=now-2d&endDate=now&count=10 ``` Note: This call **will not** give you individual vectors. This call helps to guide you toward the vector items you will eventually want to see. (See [Facet Search](doc:facets-search).) #General Vector Services with Bounding Box# For more information on General API parameters and limits, check the [Vector Services General Reference Overview](doc:vector-services-general-reference-overview) page. 1. We want to see *OSM Library and School* vectors that are within a given region in Washington, DC. Since we are looking specifically for OSM vector items, we know the `ingest_source` to query on, and, since we are specifically looking for libraries and schools, we also know the `item_type`. The query setup can be generated in several ways: - `ingest_source:OSM AND item_type:(Library OR School)` - `ingest_source:OSM AND (item_type:Library OR item_type:School)` - `+ingest_source:OSM +(item_type:Library item_type:School)` Going through the user interface, copy/pasting any of the above setups will return the same results. In the API, the query would look similar to the following for a paging call: ``` https://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=ingest_source:OSM AND item_type:(Library OR School)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500 ``` (See [Get Paging ID](doc:vs-query-get-paging-id).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call): ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) 2. We want to see all of the *rail and road* vectors available within a given region in Washington, DC, including all variations, that were generated *in the last month*. In this case, we are not limited by a specific source, and we are looking to include potential capitalization differences as well as other vectors that *include* the word rail or the word road. The query setup can be generated in several ways: - `item_date:[now-1M TO now] AND item_type.analyzed:(rail OR road)` - `item_date:[now-1M TO now] AND (item_type.analyzed:rail OR item_type.analyzed:road)` - `+item_date:[now-1M TO now] +(item_type.analyzed:rail item_type.analyzed:road)` Note that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).) Going through the user interface, copy/pasting any of the above setups will return the same results. In the API, the query would look similar to the following for a paging call: ``` https://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=item_date:[now-1M TO now] AND (item_type.analyzed:rail OR item_type.analyzed:road)&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500 ``` (See [Get Paging ID](doc:vs-query-get-paging-id).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call): ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) 3. We want to see *DG Catalog v2* vectors *within the last 30 days* within a given region in Washington, DC. The query setup can be generated in a couple of ways: - `ingest_source:GBDX_INGEST_ALPHA AND item_date:[now-30d TO now]` - `+ingest_source:GBDX_INGEST_ALPHA +item_date:[now-30d TO now]` Note that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).) Going through the user interface, copy/pasting either of the above setups will return the same results. In the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call (this is a GET call): ``` https://vector.geobigdata.io/insight-vector/api/vectors/query/paging?q=ingest_source:GBDX_INGEST_ALPHA AND item_date:[now-30d TO now]&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500 ``` (See [Get Paging ID](doc:vs-query-get-paging-id).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items (this is a POST call): ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) #General Vector Services with Shape# For more information on Shape API parameters and limits, check the [Vector Services Shape Reference Overview](doc:vector-services-shape-reference-overview) page. 1. We want to see vectors of *helicopter* type *within the last 3 weeks* within Washington, DC. The query setup can be generated in a couple of ways: - `item_type:helicopter AND item_date:[now-3w TO now]` - `+item_type:helicopter +item_date:[now-3w TO now]` Note that time may be expressed in two ways: using the now format, or using ISO-8601 format. (See [Query Syntax](doc:vector-query-syntax-query-fields-and-type-suffixes).) Going through the user interface, copy/pasting either of the above setups will return the same results. In the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call: ``` https://vector.geobigdata.io/insight-vector/api/shape/paging?q=item_type:helicopter AND item_date:[now-3w TO now]&ttl=5m&count=500 ``` With the [json provided](#section-shape-json) included in the Body. (See [Get Paging ID](doc:shape-query-get-paging-id).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items: ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) #Index Query with Bounding Box# For more information on Index Query API parameters and limits, check the [Vector Services Index Query Reference Overview](doc:vector-services-index-query-reference-overview) page. 1. We know that the index we want to query on is *vector-oiltanks-20170109* and that want to see oil tank vectors of that were derived from the catalog acquisition *1030010055AD4300* within a given region of Iceland. The query setup can be generated in a couple of ways: - `attributes.cat_id_raw:1030010055AD4300` - `+attributes.cat_id_raw:1030010055AD4300` In the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call: ``` https://vector.geobigdata.io/insight-vector/api/index/query/vector-oiltanks-20170109/paging?q=attributes.cat_id_raw:1030010055AD4300&left=-22.362671&right=-21.544189&upper=64.194423&lower=64.036149&ttl=5m&count=500 ``` (See [Get Paging ID](doc:index-query-get-paging-id-for-bounding-box).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items: ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) #Index Query with Shape# For more information on Index Query API parameters and limits, check the [Vector Services Index Query Reference Overview](doc:vector-services-index-query-reference-overview) page. 1. We know that the index we want to query on is *vector-oiltanks-20170109* and that want to see oil tank vectors of that were derived from the catalog acquisition *1030010003A3D500* within Washington, DC. The query setup can be generated in a couple of ways: - `attributes.cat_id_raw:1030010003A3D500` - `+attributes.cat_id_raw:1030010003A3D500` In the API, first we must make a call to get the paging ID for retrieving our results. The fully constructed query would look similar to the following for a paging ID call: ``` https://vector.geobigdata.io/insight-vector/api/index/query/vector-oiltanks-20170109/paging?q=attributes.cat_id_raw:1030010003A3D500&ttl=5m&count=500 ``` With the [json provided](#section-shape-json) included in the Body. (See [Get Paging ID](doc:index-query-get-paging-id-for-shape).) Using the paging ID from the previous call, you would then submit a call to retrieve pages of vector items: ``` https://vector.geobigdata.io/insight-vector/api/vectors/paging ``` Where the paging ID is placed within the body of the call. (See [Retrieve Page of Vector Items](doc:vs-retrieve-page-of-vector-items).) ###Shape JSON### Note: All example shape queries will use Washington, DC, as the aoi, depicted below: [block:code] { "codes": [ { "code": "{\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.119759000000002,\n 38.934342999999998\n ],\n [\n -77.112531800026687,\n 38.940058356319398\n ],\n [\n -77.112478301097397,\n 38.940100663913604\n ],\n [\n -77.104500000000002,\n 38.94641\n ],\n [\n -77.101277750604197,\n 38.948529900918302\n ],\n [\n -77.100700000000003,\n 38.948909999999998\n ],\n [\n -77.100526969660208,\n 38.949052938106803\n ],\n [\n -77.098089871993395,\n 38.951066192701099\n ],\n [\n -77.093866059016392,\n 38.954555429508197\n ],\n [\n -77.091500000000011,\n 38.956510000000002\n ],\n [\n -77.091399387458097,\n 38.956587350573898\n ],\n [\n -77.091355285244887,\n 38.956621256202702\n ],\n [\n -77.085812268288393,\n 38.9608827084474\n ],\n [\n -77.085780121691698,\n 38.9609074226397\n ],\n 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