GBDX

Vector Services Example Query Constructions

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

Aggregations with Bounding Box

For more information on Aggregations api parameters and definitions, check the 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.)
  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.)
  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.)

Aggregations with Shape

For more information on Aggregations api parameters and definitions, check the 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.
    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 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 included in the Body.

Both cases return the same results. (See Aggregation Query Call.)

Aggregations with Bounding Box over Index

For more information on Index Query API parameters and limits, check the Vector Services Index Query Reference Overview page.
For more information on Aggregations api parameters and definitions, check the Vector Services Aggregations Reference Overview page.

  1. We want to see the aggregation of all of the catalog imagery available within a given region of Washington, DC, with maximum Off Nadir Angle noted, more than three weeks old.
    In this case, we know that we are working under the indicies that fall under vector-gbdx-*
    The basic query setup for this aggregation involves a base aggregation query along the lines of
    • aggs=geohash:4;max:attributes.offNadirAngle_dbl
      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.
      The query part of the call is simply going to be the date and time specifications.
    • query=item_date:<now-3w
      The API call for this case would look similar to the following:
      https://vector.geobigdata.io/insight-vector/api/index/aggregation/vector-gbdx-*?aggs=geohash:4;max:attributes.offNadirAngle_dbl&query=item_date:<now-3w&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&count=1000
      
      (See Index Bounding Box Aggregation Query Call.)

Aggregations with Shape over Index

For more information on Index Query API parameters and limits, check the Vector Services Index Query Reference Overview page.
For more information on Aggregations api parameters and definitions, check the Vector Services Aggregations Reference Overview page.

  1. We want to see the aggregation of all of the types of catalog imagery available within Washington, DC, with minimum cloud cover noted, over the past three weeks.
    In this case, we know that we are working under the indicies that fall under vector-gbdx-*
    The primary difference between the bounding box index and shape index 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.
    The basic query setup for shape aggregations is the same as that of bounding box aggregations. And again, the user is looking at a base aggregation query along the lines of
    • aggs=terms:item_type;min:attributes.cloudCover_int
      Note: When used, 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.
    • query=item_date:[now-3w TO now]
      The API call would look similar to the following:
      https://vector.geobigdata.io/insight-vector/api/index/aggregation/vector-gbdx-*?aggs=terms:item_type;min:attributes.cloudCover_int&query=item_date:[now-3w TO now]&count=1000
      
      With the json provided included in the Body.
      (See Index Shape Aggregation Query Call.)

ESRI Vector Services with Bounding Box

For more information on ESRI API parameters and limits, check the 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.)
      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.)

ESRI Vector Services with Shape

For more information on Shape API parameters and limits, check the Vector Services Shape Reference Overview page.

See 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 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.)

General Vector Services with Bounding Box

For more information on General API parameters and limits, check the 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.)
      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.)
  1. 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.)
      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.)
      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.)
  1. 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.)
      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.)
      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.)
  1. We want to see Local Road vectors within a given region in Washington, DC, sorted in ascending order by name. The query setup can be generated in a couple of ways:
    • item_type:"Local Road"
    • +item_type:"Local Road"
      Going through the user interface, copy/pasting either of the above setups will return the same results. Sort cannot be added through the user interface at this time; sort is only available via the API.
      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=item_type:"Local Road"&left=-77.794631&right=-76.429580&upper=39.370370&lower=38.371776&ttl=5m&count=500&sort=attributes.name:asc
      
      (See 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.)

General Vector Services with Shape

For more information on Shape API parameters and limits, check the 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.)
      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 included in the Body.
      (See 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.)

Index Query with Bounding Box

For more information on Index Query API parameters and limits, check the 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.)
      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.)

Index Query with Shape

For more information on Index Query API parameters and limits, check the 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 included in the Body.
      (See 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.)

Shape JSON

Note: All example shape queries will use Washington, DC, as the aoi, depicted below:

{
    "type": "Polygon",
    "coordinates": [
          [
            [
              -77.119759000000002,
              38.934342999999998
            ],
            [
              -77.112531800026687,
              38.940058356319398
            ],
            [
              -77.112478301097397,
              38.940100663913604
            ],
            [
              -77.104500000000002,
              38.94641
            ],
            [
              -77.101277750604197,
              38.948529900918302
            ],
            [
              -77.100700000000003,
              38.948909999999998
            ],
            [
              -77.100526969660208,
              38.949052938106803
            ],
            [
              -77.098089871993395,
              38.951066192701099
            ],
            [
              -77.093866059016392,
              38.954555429508197
            ],
            [
              -77.091500000000011,
              38.956510000000002
            ],
            [
              -77.091399387458097,
              38.956587350573898
            ],
            [
              -77.091355285244887,
              38.956621256202702
            ],
            [
              -77.085812268288393,
              38.9608827084474
            ],
            [
              -77.085780121691698,
              38.9609074226397
            ],
            [
              -77.083839171090702,
              38.9623996187416
            ],
            [
              -77.082706082725707,
              38.9632707331536
            ],
            [
              -77.0822402534113,
              38.963628861117598
            ],
            [
              -77.0804181742635,
              38.965029669257902
            ],
            [
              -77.077441665345091,
              38.967317999008898
            ],
            [
              -77.077436698207094,
              38.967321817727395
            ],
            [
              -77.071640984008695,
              38.971777542736795
            ],
            [
              -77.069563580871105,
              38.973374643076397
            ],
            [
              -77.068951003066402,
              38.973845590771795
            ],
            [
              -77.066690519618689,
              38.9755834426199
            ],
            [
              -77.062564652888398,
              38.9787553946774
            ],
            [
              -77.054299,
              38.985109999999999
            ],
            [
              -77.052937499620796,
              38.986133684495599
            ],
            [
              -77.052220061432294,
              38.986673111705002
            ],
            [
              -77.051662504310286,
              38.987092327586197
            ],
            [
              -77.046541251074387,
              38.990942893929002
            ],
            [
              -77.042388998663696,
              38.994064888222802
            ],
            [
              -77.040998999999999,
              38.995109999999997
            ],
            [
              -77.036769251411599,
              38.992050181872202
            ],
            [
              -77.036461350189299,
              38.991827444817801
            ],
            [
              -77.036299,
              38.991709999999998
            ],
            [
              -77.033707359689004,
              38.989731935321302
            ],
            [
              -77.031234921812697,
              38.987844851680599
            ],
            [
              -77.026696218486805,
              38.984380694753405
            ],
            [
              -77.026634552895203,
              38.984333628604602
            ],
            [
              -77.026590704025892,
              38.984300161036096
            ],
            [
              -77.020536715098586,
              38.979679465173596
            ],
            [
              -77.018706497583295,
              38.978282555036799
            ],
            [
              -77.018654553432199,
              38.978242908759398
            ],
            [
              -77.017323130969686,
              38.977226703025003
            ],
            [
              -77.015597999999997,
              38.975909999999999
            ],
            [
              -77.013797999999994,
              38.974409999999999
            ],
            [
              -77.011056734099299,
              38.972266828477601
            ],
            [
              -77.008589274640102,
              38.9703377238095
            ],
            [
              -77.008297999999996,
              38.970109999999998
            ],
            [
              -77.007774574197001,
              38.969685844607895
            ],
            [
              -77.002601759533803,
              38.965494081001602
            ],
            [
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      }

Vector Services Example Query Constructions

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