The National Geographic Database
(NGD) Features API provides access to Ordnance Survey’s next generation
NGD data. The NGD Features API is a premium or public sector product
only. The NGD
Features API allows for programmatic access to NGD data in
GeoJSON format. For R developers,
osdatahub library wraps the functionality of the NGD
Features API to make it easy to get data from Ordnance Survey.
Not using
R? There are Python and JavaScript versions ofosdatahub.
osdatahub packageOnce the package is installed within R, import the
library.
In order to access the NGD, you must first have an OS Data Hub API key. A notebook explaining the process of registering an account and getting an API key is available to read here. When you’re choosing which APIs you want to add to your project, you must select “OS NGD API - Features” to use NGD data.
Once you have an API key, you can load your API key in by either
passing it in as a string to functions in osdatahub, or
setting it as an environment variable. While you can store your API key
as a string variable in your scripts, you want to protect your API key
and not share it with others.
# Option 1.
# Create an environment variable with your API key.
# This function is available in the osdatahub package.
set_os_key('YOUR API KEY HERE')Alternatively, you can add the environment variable manually in a
file called .Renviron, typically in your home
directory.
The file looks something like:
VAR1 = value1
VAR2 = value2Add a variable named: OS_API_KEY. Note that
.Renviron is processed on start-up, so restart
R to see changes. Environment variables are available in
every running R process, and can be read by any other program on your
computer. More secure options see the keyring
package.
The NGD contains multiple themes and collections:
theme is a family of collections, for
example, there is a theme called ‘Buildings’.collection is a group of features. The Buildings
theme contains two collections: ‘BuildingLine’ and ‘BuildingPart’.You can read more about the NGD Features API’s themes and collections in the Technical Specification.
To quickly retrieve a data frame of available data, you can query the API directly:
This will returna a data.frame object, which we can then iterate through to return metadata about each collection.
for(i in 1:nrow(ngd_collections)){
collection <- ngd_collections[i, ]
print(paste0(collection$title, '(', collection$id, ')'))
}
#> [1] "Building v1(bld-fts-building-1)"
#> [1] "Building Line v1(bld-fts-buildingline-1)"
#> [1] "Building Part v1(bld-fts-buildingpart-1)"
#> [1] "Named Area v1(gnm-fts-namedarea-1)"
#> [1] "Named Point v1(gnm-fts-namedpoint-1)"
#> [1] "Land v1(lnd-fts-land-1)"
#> [1] "Landform v1(lnd-fts-landform-1)"
#> [1] "Landform Line v1(lnd-fts-landformline-1)"
#> [1] "Landform Point v1(lnd-fts-landformpoint-1)"
#> [1] "Land Point v1(lnd-fts-landpoint-1)"
#> [1] "Site v1(lus-fts-site-1)"
#> [1] "Site Access Location v1(lus-fts-siteaccesslocation-1)"
#> [1] "Site Routing Point v1(lus-fts-siteroutingpoint-1)"
#> [1] "Compound Structure v1(str-fts-compoundstructure-1)"
#> [1] "Structure v1(str-fts-structure-1)"
#> [1] "Structure Line v1(str-fts-structureline-1)"
#> [1] "Structure Point v1(str-fts-structurepoint-1)"
#> [1] "Cartographic Rail Detail v1(trn-fts-cartographicraildetail-1)"
#> [1] "Rail v1(trn-fts-rail-1)"
#> [1] "Road Line v1(trn-fts-roadline-1)"
#> [1] "Road Track Or Path v1(trn-fts-roadtrackorpath-1)"
#> [1] "Connecting Link v1(trn-ntwk-connectinglink-1)"
#> [1] "Connecting Node v1(trn-ntwk-connectingnode-1)"
#> [1] "Ferry Link v1(trn-ntwk-ferrylink-1)"
#> [1] "Ferry Node v1(trn-ntwk-ferrynode-1)"
#> [1] "Ferry Terminal v1(trn-ntwk-ferryterminal-1)"
#> [1] "Path v1(trn-ntwk-path-1)"
#> [1] "Path Link v1(trn-ntwk-pathlink-1)"
#> [1] "Path Node v1(trn-ntwk-pathnode-1)"
#> [1] "Pavement Link v1(trn-ntwk-pavementlink-1)"
#> [1] "Railway Link v1(trn-ntwk-railwaylink-1)"
#> [1] "Railway Link Set v1(trn-ntwk-railwaylinkset-1)"
#> [1] "Railway Node v1(trn-ntwk-railwaynode-1)"
#> [1] "Road v1(trn-ntwk-road-1)"
#> [1] "Road Junction v1(trn-ntwk-roadjunction-1)"
#> [1] "Road Link v1(trn-ntwk-roadlink-1)"
#> [1] "Road Link v2(trn-ntwk-roadlink-2)"
#> [1] "Road Node v1(trn-ntwk-roadnode-1)"
#> [1] "Street v1(trn-ntwk-street-1)"
#> [1] "Average And Indicative Speed v1(trn-rami-averageandindicativespeed-1)"
#> [1] "Highway Dedication v1(trn-rami-highwaydedication-1)"
#> [1] "Maintenance Area v1(trn-rami-maintenancearea-1)"
#> [1] "Maintenance Line v1(trn-rami-maintenanceline-1)"
#> [1] "Maintenance Point v1(trn-rami-maintenancepoint-1)"
#> [1] "Reinstatement Area v1(trn-rami-reinstatementarea-1)"
#> [1] "Reinstatement Line v1(trn-rami-reinstatementline-1)"
#> [1] "Reinstatement Point v1(trn-rami-reinstatementpoint-1)"
#> [1] "Restriction v1(trn-rami-restriction-1)"
#> [1] "Routing Hazard v1(trn-rami-routinghazard-1)"
#> [1] "RoutingStructure v1(trn-rami-routingstructure-1)"
#> [1] "Special Designation Area v1(trn-rami-specialdesignationarea-1)"
#> [1] "Special Designation Line v1(trn-rami-specialdesignationline-1)"
#> [1] "Special Designation Point v1(trn-rami-specialdesignationpoint-1)"
#> [1] "Inter Tidal Line v1(wtr-fts-intertidalline-1)"
#> [1] "Tidal Boundary v1(wtr-fts-tidalboundary-1)"
#> [1] "Water v1(wtr-fts-water-1)"
#> [1] "Water Point v1(wtr-fts-waterpoint-1)"
#> [1] "Water Link v1(wtr-ntwk-waterlink-1)"
#> [1] "Water Link Set v1(wtr-ntwk-waterlinkset-1)"
#> [1] "Water Node v1(wtr-ntwk-waternode-1)"We’ll now walk through the process of acquiring data from the NGD
Building Part collection (bld-fts-buildingpart-1) and
importing this data into a an object of type sf.
To get the first 100 features in the collection, you simply need to query use the NGD query function:
collection <- 'bld-fts-buildingpart-1'
# make the query and contact the API
# reduce the number of features returned for the example
features <- query_ngd(collection = collection, max_results = 4)The API returns an OGC-compliant GeoJSON, which is easy to import into other libraries for analysis.
If you haven’t already, install and import the sf
library:
And now, import the NGD data.
Let’s take a look at a preview of the data using head().
As you’ll see, NGD data is rich in attribution:
head(gdf)
#> Simple feature collection with 4 features and 39 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -3.97032 ymin: 51.59236 xmax: 1.727559 ymax: 55.7426
#> Geodetic CRS: WGS 84
#> osid toid theme changetype isobscured description versiondate geometry_area height_source
#> 1 000000b1-0556-4231-a52a-9b5b8b82dfbf osgb1000041024621 Buildings Modified Attributes FALSE Building 2022-09-18 59.200650 Ordnance Survey
#> 2 00000183-3ae0-4f05-adb8-a5792d09f55f osgb5000005167584940 Buildings New FALSE Building 2022-08-26 9.499142 Ordnance Survey
#> 3 000001d6-8217-4f7a-a70e-3757eb76c9e4 osgb1000000360448 Buildings New FALSE Building 2022-08-26 15.258750 Ordnance Survey
#> 4 00000208-a659-49f9-a587-ab26cb2b2248 osgb1000017249389 Buildings New FALSE Building 2022-08-26 12.806250 Ordnance Survey
#> physicallevel oslandusetiera oslandusetierb geometry_source oslandcovertiera oslandcovertierb oslanduse_source height_updatedate
#> 1 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 2 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 3 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 4 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> description_source oslandcover_source associatedstructure geometry_updatedate height_evidencedate capturespecification oslanduse_updatedate
#> 1 Ordnance Survey Ordnance Survey <NA> 2006-08-30 2022-06-20 Urban 2006-08-30
#> 2 Ordnance Survey Ordnance Survey <NA> 2015-10-29 2022-04-16 Rural 2015-10-29
#> 3 Ordnance Survey Ordnance Survey <NA> 1993-04-01 2022-07-10 Urban 1993-04-01
#> 4 Ordnance Survey Ordnance Survey <NA> 1993-05-01 2022-02-27 Urban 1993-05-01
#> absoluteheightmaximum absoluteheightminimum geometry_evidencedate heightconfidencelevel relativeheightmaximum absoluteheightroofbase description_updatedate
#> 1 115.16 105.00 2006-08-30 Not Assessed 10.16 110.85 2006-08-30
#> 2 7.29 5.39 2015-03-11 Not Assessed 1.90 6.21 2015-10-29
#> 3 38.63 34.70 1993-04-01 Not Assessed 3.93 37.13 1993-04-01
#> 4 72.80 69.99 1993-05-01 Not Assessed 2.81 71.95 1993-05-01
#> oslandcover_updatedate oslanduse_evidencedate relativeheightroofbase versionavailabletodate firstdigitalcapturedate description_evidencedate
#> 1 2006-08-30 2006-08-30 5.85 <NA> 1991-09-18 2006-08-30
#> 2 2015-10-29 2015-03-11 0.82 <NA> 2015-11-12 2015-03-11
#> 3 1993-04-01 1993-04-01 2.43 <NA> 1993-04-01 1993-04-01
#> 4 1993-05-01 1993-05-01 1.96 <NA> 1993-05-01 1993-05-01
#> oslandcover_evidencedate versionavailablefromdate geometry
#> 1 2006-08-30 2022-09-19 01:00:00 POLYGON ((-3.970308 55.7425...
#> 2 2015-03-11 2022-08-27 01:00:00 POLYGON ((1.727559 52.66142...
#> 3 1993-04-01 2022-08-27 01:00:00 POLYGON ((0.1941514 51.5924...
#> 4 1993-05-01 2022-08-27 01:00:00 POLYGON ((-1.454721 52.3961...Alternatively, it’s possible to return an sf object
directly from the NGD query function:
# retrieve features and automatically convert to spatial data frame
features <- query_ngd(collection = collection,
max_results = 4,
returnType = 'sf')
head(features)
#> Simple feature collection with 4 features and 39 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -3.97032 ymin: 51.59236 xmax: 1.727559 ymax: 55.7426
#> Geodetic CRS: WGS 84
#> osid toid theme changetype isobscured description versiondate geometry_area height_source
#> 1 000000b1-0556-4231-a52a-9b5b8b82dfbf osgb1000041024621 Buildings Modified Attributes FALSE Building 2022-09-18 59.200650 Ordnance Survey
#> 2 00000183-3ae0-4f05-adb8-a5792d09f55f osgb5000005167584940 Buildings New FALSE Building 2022-08-26 9.499142 Ordnance Survey
#> 3 000001d6-8217-4f7a-a70e-3757eb76c9e4 osgb1000000360448 Buildings New FALSE Building 2022-08-26 15.258750 Ordnance Survey
#> 4 00000208-a659-49f9-a587-ab26cb2b2248 osgb1000017249389 Buildings New FALSE Building 2022-08-26 12.806250 Ordnance Survey
#> physicallevel oslandusetiera oslandusetierb geometry_source oslandcovertiera oslandcovertierb oslanduse_source height_updatedate
#> 1 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 2 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 3 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 4 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> description_source oslandcover_source associatedstructure geometry_updatedate height_evidencedate capturespecification oslanduse_updatedate
#> 1 Ordnance Survey Ordnance Survey <NA> 2006-08-30 2022-06-20 Urban 2006-08-30
#> 2 Ordnance Survey Ordnance Survey <NA> 2015-10-29 2022-04-16 Rural 2015-10-29
#> 3 Ordnance Survey Ordnance Survey <NA> 1993-04-01 2022-07-10 Urban 1993-04-01
#> 4 Ordnance Survey Ordnance Survey <NA> 1993-05-01 2022-02-27 Urban 1993-05-01
#> absoluteheightmaximum absoluteheightminimum geometry_evidencedate heightconfidencelevel relativeheightmaximum absoluteheightroofbase description_updatedate
#> 1 115.16 105.00 2006-08-30 Not Assessed 10.16 110.85 2006-08-30
#> 2 7.29 5.39 2015-03-11 Not Assessed 1.90 6.21 2015-10-29
#> 3 38.63 34.70 1993-04-01 Not Assessed 3.93 37.13 1993-04-01
#> 4 72.80 69.99 1993-05-01 Not Assessed 2.81 71.95 1993-05-01
#> oslandcover_updatedate oslanduse_evidencedate relativeheightroofbase versionavailabletodate firstdigitalcapturedate description_evidencedate
#> 1 2006-08-30 2006-08-30 5.85 <NA> 1991-09-18 2006-08-30
#> 2 2015-10-29 2015-03-11 0.82 <NA> 2015-11-12 2015-03-11
#> 3 1993-04-01 1993-04-01 2.43 <NA> 1993-04-01 1993-04-01
#> 4 1993-05-01 1993-05-01 1.96 <NA> 1993-05-01 1993-05-01
#> oslandcover_evidencedate versionavailablefromdate geometry
#> 1 2006-08-30 2022-09-19T00:00:00Z POLYGON ((-3.970308 55.7425...
#> 2 2015-03-11 2022-08-27T00:00:00Z POLYGON ((1.727559 52.66142...
#> 3 1993-04-01 2022-08-27T00:00:00Z POLYGON ((0.1941514 51.5924...
#> 4 1993-05-01 2022-08-27T00:00:00Z POLYGON ((-1.454721 52.3961...Filters can help you limit the scope of your query using spatial, temporal and contextual parameters. The NGD Features API uses Common Query Language (CQL) to allow you to filter data using the attribution set of each collection.
extentYou can specify any polygon to query by using the functions in
osdatahub to create extents. You can learn more about using
extents here.
state_datetime and
end_datetimeIf you want to only get features that have a temporal property, you can specify date ranges to query within. If youw ant to get features for a single time, simply provide the same argument for both parameters.
cql_filterThe NGD API supports a generic filter grammar called the Common Query Language (CQL) to further filter your query using human readable commands. You can find out more about the operations that the API supports in the Queryables section of the Technical Specification. The CQL filter allows you to specify specific properties for features as well as spatial filters.
In addition, the osdatahub package permits you to
specify the maximum number of features to return (by default, 100
features):
max_resultsAllows you to specify the maximum number of features you’d like to receive. Default is 100.
offsetSkips past the specified number of features in the collection. Default is 0.
In our case, we want to get features that are only within a certain bounding box in Manchester. We can specify an extent based on the geometry, and then pass this into the query:
extent = extent_from_bbox(c(-2.244973, 53.476620, -2.237799, 53.480525),
crs = 'CRS84')
# Specify the 'extent' as the first parameter to define the type of query.
features = query_ngd(x = extent,
collection = collection,
max_results = 4,
returnType = 'sf')
# run this cell to see the contents of 'features'
features
#> Simple feature collection with 4 features and 39 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -2.241707 ymin: 53.47689 xmax: -2.23773 ymax: 53.48008
#> Geodetic CRS: WGS 84
#> osid toid theme changetype isobscured description versiondate geometry_area height_source
#> 1 02837688-4553-4d32-87aa-10733dad9274 osgb1000024157902 Buildings New FALSE Building 2022-08-26 21.57975 Ordnance Survey
#> 2 02c6f2c7-0e25-4224-b4a7-c62c7d476e48 osgb1000024157913 Buildings Modified Attributes FALSE Building 2023-06-03 441.13125 Ordnance Survey
#> 3 0364fa8b-024a-4078-ae13-4a3e21c5e744 osgb1000024155721 Buildings New FALSE Building 2022-08-26 173.96250 Ordnance Survey
#> 4 04b6b1d7-2a73-4173-b953-20c3ef2e6ce7 osgb5000005290155401 Buildings Modified Attributes FALSE Building 2022-09-26 45.44059 <NA>
#> physicallevel oslandusetiera oslandusetierb geometry_source oslandcovertiera oslandcovertierb oslanduse_source height_updatedate
#> 1 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 2 Surface Level Commercial Activity: Retail [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 3 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24
#> 4 Surface Level Residential Accommodation ["Private Residence"] Ordnance Survey Constructed ["Building"] Ordnance Survey <NA>
#> description_source oslandcover_source associatedstructure geometry_updatedate height_evidencedate capturespecification oslanduse_updatedate
#> 1 Ordnance Survey Ordnance Survey <NA> 2016-10-26 2021-04-19 Urban 1993-05-01
#> 2 Ordnance Survey Ordnance Survey <NA> 2010-04-13 2021-04-19 Urban 2010-04-13
#> 3 Ordnance Survey Ordnance Survey <NA> 1993-05-01 2021-04-19 Urban 1993-05-01
#> 4 Ordnance Survey Ordnance Survey <NA> 2021-11-30 <NA> Urban 2021-11-30
#> absoluteheightmaximum absoluteheightminimum geometry_evidencedate heightconfidencelevel relativeheightmaximum absoluteheightroofbase description_updatedate
#> 1 68.98 43.34 2016-10-04 Not Assessed 25.64 66.54 1993-05-01
#> 2 67.69 44.22 2010-04-13 Not Assessed 23.47 60.87 2010-04-13
#> 3 57.17 42.16 1993-05-01 Not Assessed 15.01 52.83 1993-05-01
#> 4 NA NA 2021-11-30 <NA> NA NA 2021-11-30
#> oslandcover_updatedate oslanduse_evidencedate relativeheightroofbase versionavailabletodate firstdigitalcapturedate description_evidencedate
#> 1 1993-05-01 1993-05-01 23.20 <NA> 1993-05-01 1993-05-01
#> 2 2010-04-13 2010-04-13 16.65 <NA> 1993-05-01 2010-04-13
#> 3 1993-05-01 1993-05-01 10.67 <NA> 1993-05-01 1993-05-01
#> 4 2021-11-30 2021-11-30 NA <NA> 2021-11-30 2021-11-30
#> oslandcover_evidencedate versionavailablefromdate geometry
#> 1 1993-05-01 2022-08-27T00:00:00Z POLYGON ((-2.240982 53.4790...
#> 2 2010-04-13 2023-06-04T00:00:00Z POLYGON ((-2.241351 53.4798...
#> 3 1993-05-01 2022-08-27T00:00:00Z POLYGON ((-2.240074 53.4781...
#> 4 2021-11-30 2022-09-27T00:00:00Z POLYGON ((-2.23773 53.47692...By default, osdatahub will return a maximum 100 features
to a query - starting at zero. This behaviour can be altered by
specifying max_results and offset parameters.
We’ll be building on the previous example, specifying the same
extent as before…
# returns a maximum of 4 results (features 1 to 4)
features = query_ngd(extent,
collection = collection,
max_results = 4)
# returns a maximum of 4 results (features 101 to 104)
features = query_ngd(extent,
collection = collection,
offset = 100,
max_results = 4,
returnType = 'sf')
# run this cell to see the contents of 'features'
features
#> Simple feature collection with 4 features and 39 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -2.2437 ymin: 53.47765 xmax: -2.238997 ymax: 53.47982
#> Geodetic CRS: WGS 84
#> osid toid theme changetype isobscured description versiondate geometry_area height_source physicallevel
#> 1 4e1a5c52-aa3f-4c99-a216-f3ad0b5aff83 osgb1000024155766 Buildings New FALSE Building 2022-08-26 64.48625 Ordnance Survey Surface Level
#> 2 50b0ab14-2a1a-41ae-8425-6ddafbddb575 osgb1000024155767 Buildings New FALSE Building 2022-08-26 457.61400 Ordnance Survey Surface Level
#> 3 52335576-4e7b-47f8-ab79-a644172032f0 osgb1000024155700 Buildings New FALSE Building 2022-08-26 166.62625 Ordnance Survey Surface Level
#> 4 537d6ba9-5902-4e6b-b193-ad604a1570b9 osgb1000002517038005 Buildings New FALSE Building 2022-08-26 1018.93900 Ordnance Survey Surface Level
#> oslandusetiera oslandusetierb geometry_source oslandcovertiera oslandcovertierb oslanduse_source height_updatedate description_source
#> 1 Commercial Activity: Retail [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24 Ordnance Survey
#> 2 Commercial Activity: Retail [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24 Ordnance Survey
#> 3 Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24 Ordnance Survey
#> 4 Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey 2022-08-24 Ordnance Survey
#> oslandcover_source associatedstructure geometry_updatedate height_evidencedate capturespecification oslanduse_updatedate absoluteheightmaximum
#> 1 Ordnance Survey <NA> 1993-05-01 2021-04-19 Urban 1993-05-01 63.80
#> 2 Ordnance Survey <NA> 2002-10-08 2021-04-19 Urban 2002-10-08 69.45
#> 3 Ordnance Survey <NA> 2010-04-28 2021-04-19 Urban 2010-04-28 63.78
#> 4 Ordnance Survey <NA> 2007-03-22 2021-04-19 Urban 2007-03-22 70.57
#> absoluteheightminimum geometry_evidencedate heightconfidencelevel relativeheightmaximum absoluteheightroofbase description_updatedate oslandcover_updatedate
#> 1 41.84 1993-05-01 Not Assessed 21.96 62.62 1993-05-01 1993-05-01
#> 2 41.20 2002-10-08 Not Assessed 28.25 61.31 2002-10-08 2002-10-08
#> 3 42.59 2010-04-28 Not Assessed 21.19 61.16 2010-04-28 2010-04-28
#> 4 41.34 2007-03-22 Not Assessed 29.23 59.01 2007-03-22 2007-03-22
#> oslanduse_evidencedate relativeheightroofbase versionavailabletodate firstdigitalcapturedate description_evidencedate oslandcover_evidencedate
#> 1 1993-05-01 20.78 <NA> 1993-05-01 1993-05-01 1993-05-01
#> 2 2002-10-08 20.11 <NA> 1993-05-01 2002-10-08 2002-10-08
#> 3 2010-04-28 18.57 <NA> 1993-05-01 2010-04-28 2010-04-28
#> 4 2007-03-22 17.67 <NA> 2007-03-22 2007-03-22 2007-03-22
#> versionavailablefromdate geometry
#> 1 2022-08-27T00:00:00Z POLYGON ((-2.241327 53.4780...
#> 2 2022-08-27T00:00:00Z POLYGON ((-2.24123 53.47778...
#> 3 2022-08-27T00:00:00Z POLYGON ((-2.238997 53.4783...
#> 4 2022-08-27T00:00:00Z POLYGON ((-2.243258 53.4794...Common Query Language (CQL) filters are a handy way in which you can
tailor the results of your query to match specific needs. More
information on CQL filters can be found here. We
can pass a filter in the API using the filter
parameter.
# Building off the same extent covering Manchester, filtering by buildings
# larger than or equal to 20m^2 and with a maximum height greater than 60m.
# At the time of writing, this returns 7 features (buildings)
features = query_ngd(extent,
collection = collection,
cql_filter='geometry_area>=200 AND relativeheightmaximum>60',
returnType = 'sf')
# run this cell to see the contents of 'features'
features
#> Simple feature collection with 7 features and 39 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -2.243508 ymin: 53.47677 xmax: -2.237916 ymax: 53.48044
#> Geodetic CRS: WGS 84
#> osid toid theme changetype isobscured description versiondate geometry_area height_source
#> 1 40fa2462-4c01-48d4-bcf1-5179efb56027 osgb5000005134737612 Buildings New FALSE Building 2022-08-26 2122.6936 Ordnance Survey
#> 2 41ab80d3-28ca-460f-9afe-04671aa2807a osgb1000002517080496 Buildings New FALSE Building 2022-08-26 687.8187 Ordnance Survey
#> 3 8a98fdc7-bc44-4ac0-8c20-aac5c7ddcf30 osgb1000002517080494 Buildings New FALSE Building 2022-08-26 206.9899 Ordnance Survey
#> 4 8ce5ac53-d4ee-492d-9b6d-56b069252cbe osgb1000024155687 Buildings Modified Attributes FALSE Building 2023-06-22 748.7997 Ordnance Survey
#> 5 a1007205-9f06-4a36-bcba-45de245d9faf osgb5000005134737702 Buildings New FALSE Building 2022-08-26 380.3845 Ordnance Survey
#> 6 af43ce9a-18d2-47d2-9744-fdc89f7c241c osgb5000005267089134 Buildings New FALSE Building 2022-08-26 1590.4881 Ordnance Survey
#> 7 cfe4934e-8360-40bb-81eb-5f49d240c2b8 osgb5000005275364197 Buildings Modified Attributes FALSE Building 2023-06-22 1143.7811 Ordnance Survey
#> physicallevel oslandusetiera oslandusetierb geometry_source oslandcovertiera oslandcovertierb oslanduse_source
#> 1 Surface Level Commercial Activity: Industrial Or Manufacturing [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 2 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 3 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 4 Surface Level Government Services [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 5 Surface Level Unknown Or Unused Artificial [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 6 Surface Level Commercial Activity: Industrial Or Manufacturing [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> 7 Surface Level Temporary Or Holiday Accommodation [] Ordnance Survey Constructed ["Building"] Ordnance Survey
#> height_updatedate description_source oslandcover_source associatedstructure geometry_updatedate height_evidencedate capturespecification oslanduse_updatedate
#> 1 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2017-09-26 2021-04-19 Urban 2014-09-26
#> 2 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2009-06-25 2021-04-19 Urban 2009-06-25
#> 3 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2009-06-25 2021-04-19 Urban 2009-06-25
#> 4 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2014-11-19 2021-04-19 Urban 2010-03-18
#> 5 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2014-10-13 2021-04-19 Urban 2014-10-13
#> 6 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2020-07-16 2021-04-19 Urban 2020-07-16
#> 7 2022-08-24 Ordnance Survey Ordnance Survey <NA> 2021-01-13 2021-04-19 Urban 2021-01-13
#> absoluteheightmaximum absoluteheightminimum geometry_evidencedate heightconfidencelevel relativeheightmaximum absoluteheightroofbase description_updatedate
#> 1 102.52 38.65 2017-09-26 Not Assessed 63.87 94.76 2014-09-26
#> 2 105.48 43.20 2009-06-25 Not Assessed 62.28 100.70 2009-06-25
#> 3 104.38 43.17 2009-06-25 Not Assessed 61.21 88.38 2009-06-25
#> 4 116.73 43.13 2013-11-04 Not Assessed 73.60 109.57 2010-03-18
#> 5 99.96 39.25 2014-10-13 Not Assessed 60.71 94.68 2014-10-13
#> 6 102.53 38.69 2020-07-16 Not Assessed 63.84 95.19 2020-07-16
#> 7 112.00 40.06 2021-01-13 Not Assessed 71.94 107.00 2021-01-13
#> oslandcover_updatedate oslanduse_evidencedate relativeheightroofbase versionavailabletodate firstdigitalcapturedate description_evidencedate
#> 1 2014-09-26 2013-11-04 56.11 <NA> 2014-10-21 2013-11-04
#> 2 2009-06-25 2009-06-25 57.50 <NA> 2009-06-25 2009-06-25
#> 3 2009-06-25 2009-06-25 45.21 <NA> 2009-06-25 2009-06-25
#> 4 2010-03-18 2010-03-18 66.44 <NA> 1993-05-01 2010-03-18
#> 5 2014-10-13 2014-10-13 55.43 <NA> 2014-10-21 2014-10-13
#> 6 2020-07-16 2020-07-16 56.50 <NA> 2020-07-16 2020-07-16
#> 7 2021-01-13 2021-01-13 66.94 <NA> 2021-01-13 2021-01-13
#> oslandcover_evidencedate versionavailablefromdate geometry
#> 1 2013-11-04 2022-08-27T00:00:00Z POLYGON ((-2.242897 53.4772...
#> 2 2009-06-25 2022-08-27T00:00:00Z POLYGON ((-2.242983 53.4804...
#> 3 2009-06-25 2022-08-27T00:00:00Z POLYGON ((-2.242827 53.4801...
#> 4 2010-03-18 2023-06-23T00:00:00Z POLYGON ((-2.238278 53.4780...
#> 5 2014-10-13 2022-08-27T00:00:00Z POLYGON ((-2.242894 53.4777...
#> 6 2020-07-16 2022-08-27T00:00:00Z POLYGON ((-2.2422 53.47719,...
#> 7 2021-01-13 2023-06-23T00:00:00Z POLYGON ((-2.241926 53.4779...We hope this short introduction to the osdatahub
package’s new NGD capabilities has been useful! You can find further
resources to assist you on your OS API development journal at: https://github.com/OrdnanceSurvey/os-api-resources