GBDX

Vector Services Example Query Constructions

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

Aggregations with Bounding Box over Index#

For more information on Index Query API parameters and limits, and Aggregations API parameters and definitions, check the Vector Services Index Query 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 indices that fall under vector-gbdx-alpha-catalog-v2*.
    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-alpha-catalog-v2*?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.)

  1. 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, over the past two months. In this case, we know that we are working under the indices that fall under vector-sma-twitter*. There are two approaches to handling this query. 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/index/aggregation/vector-sma-twitter*?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/index/aggregation/vector-sma-twitter*?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 Index Bounding Box Aggregation Query Call.)

  1. 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. In this case, we know that we are working with the indices that fall under vector-workflow*. With this aggregation, 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/index/aggregation/vector-workflow*?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 Index Bounding Box Aggregation Query Call.)

Aggregations with Shape over Index#

For more information on Index Query API parameters and limits, and Aggregations API parameters and definitions, check the Vector Services Index Query 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-alpha-catalog-v2*
    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-alpha-catalog-v2*?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.)

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-workflow-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-workflow-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 (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 OSM Library and School vectors that are within a given region of Washington, DC. In this case, we know that we are working under the indices that fall under vector-osm*. The query setup can be generated in several ways:
  • item_type:(Library OR School)
  • item_type:Library OR item_type:School
  • +(item_type:Library item_type:School)
    In the API, the query would look similar to the following for a paging call:
https://vector.geobigdata.io/insight-vector/api/index/query/vector-osm*/paging?q=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 OSM 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 know that we are working under the indices that fall under vector-osm*, 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/index/query/vector-osm*/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 vectors within the last 30 days within a given region in Washington, DC. In this case, we know that we are working under the indices that fall under vector-gbdx-alpha-catalog-v2*. The query setup can be generated in a couple of ways:
  • item_date:[now-30d TO now]
  • +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/index/query/vector-gbdx-alpha-catalog-v2*/paging?q=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 OSM Local Road vectors within a given region in Washington, DC, sorted in ascending order by name. In this case, we know that we are working under the indices that fall under vector-osm*. The query setup can be generated in a couple of ways:
  • item_type:"Local Road"
  • +item_type:"Local Road"
    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/index/query/vector-osm*/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.)

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-worklow-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-workflow-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.)

  1. We want to see vectors of helicopter type that have been derived from catalog imagery within the last 3 weeks within Washington, DC. In this case, we know that we are working under the indices that fall under vector-workflow*. 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.)
    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-workflow*/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.)

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
            ],
            [
              -77.002589220168701,
              38.9654839197919
            ],
            [
              -77.002498000000003,
              38.965409999999999
            ],
            [
              -77.001649914165299,
              38.964754464201398
            ],
            [
              -76.999124025830696,
              38.962802055425001
            ],
            [
              -76.997492710321396,
              38.961541114997097
            ],
            [
              -76.997364490213485,
              38.961442006077498
            ],
            [
              -76.991892520544198,
              38.957212396454601
            ],
            [
              -76.989517122313089,
              38.955376310397199
            ],
            [
              -76.987925151061006,
              38.954145781477195
            ],
            [
              -76.985936949582396,
              38.952608982734901
            ],
            [
              -76.978434328903006,
              38.946809762611103
            ],
            [
              -76.977826303142493,
              38.946339783469597
            ],
            [
              -76.973887698849794,
              38.943295402812502
            ],
            [
              -76.973746208817289,
              38.943186036781796
            ],
            [
              -76.968158917514103,
              38.938867288234192
            ],
            [
              -76.966831323513205,
              38.937841112158601
            ],
            [
              -76.963435789814085,
              38.935216502945394
            ],
            [
              -76.963430253918403,
              38.9352122239235
            ],
            [
              -76.961374166883587,
              38.933622952412001
            ],
            [
              -76.958748660684705,
              38.931593543084901
            ],
            [
              -76.957718618064007,
              38.930797362103796
            ],
            [
              -76.957704907252193,
              38.930786764204797
            ],
            [
              -76.957531362067499,
              38.930652620847297
            ],
            [
              -76.956050960924685,
              38.929508331076498
            ],
            [
              -76.943957421306891,
              38.920160517650395
            ],
            [
              -76.941921067989597,
              38.918586499498396
            ],
            [
              -76.941920456206901,
              38.918586026615294
            ],
            [
              -76.935096000000001,
              38.913311
            ],
            [
              -76.931791668782893,
              38.910675480540895
            ],
            [
              -76.931565014869491,
              38.910494702416599
            ],
            [
              -76.931251468051201,
              38.910244618909005
            ],
            [
              -76.927557561849397,
              38.907298376224695
            ],
            [
              -76.923529268146595,
              38.904085427599497
            ],
            [
              -76.923451063194094,
              38.904023051687297
            ],
            [
              -76.9199019174646,
              38.901192269293205
            ],
            [
              -76.913413374932091,
              38.896017037458904
            ],
            [
              -76.912868951119108,
              38.895582807516796
            ],
            [
              -76.912298639399395,
              38.895127929498798
            ],
            [
              -76.910955802208107,
              38.894056888699396
            ],
            [
              -76.910850679128799,
              38.893973043012394
            ],
            [
              -76.909395000000004,
              38.892811999999999
            ],
            [
              -76.910794999999993,
              38.891711999999998
            ],
            [
              -76.912378388448388,
              38.890482545440101
            ],
            [
              -76.913262575757599,
              38.889795999999997
            ],
            [
              -76.913303391762398,
              38.889764307572698
            ],
            [
              -76.918535662710696,
              38.885701603071695
            ],
            [
              -76.918550521543892,
              38.885690065624694
            ],
            [
              -76.919294999999991,
              38.885111999999999
            ],
            [
              -76.920194999999993,
              38.884411999999998
            ],
            [
              -76.920825111103696,
              38.883918607691399
            ],
            [
              -76.921584694546695,
              38.8833238353944
            ],
            [
              -76.923972437651798,
              38.8814541745108
            ],
            [
              -76.924034950179788,
              38.881405225682002
            ],
            [
              -76.9298866240579,
              38.876823222814899
            ],
            [
              -76.933718878799695,
              38.873822474211899
            ],
            [
              -76.933804814136295,
              38.873755184754799
            ],
            [
              -76.934971325738687,
              38.872841777818898
            ],
            [
              -76.935944196395397,
              38.872079996449799
            ],
            [
              -76.93949014374229,
              38.869303433495595
            ],
            [
              -76.943641272331504,
              38.866052998919997
            ],
            [
              -76.946701967384385,
              38.8636564003058
            ],
            [
              -76.946746183955085,
              38.863621777656192
            ],
            [
              -76.949696000000003,
              38.861311999999998
            ],
            [
              -76.949860966442898,
              38.861196523489902
            ],
            [
              -76.953695999999994,
              38.858511999999997
            ],
            [
              -76.958140102774607,
              38.854946520854497
            ],
            [
              -76.958148964388798,
              38.854939411230305
            ],
            [
              -76.96194148624339,
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Updated 2 years ago

Vector Services Example Query Constructions


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

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