osdatahub is a python package from Ordnance Survey (OS) that makes it easier to interact with OS data via
the OS Data Hub APIs.
OS is the national mapping agency for Great Britain and produces a large variety of mapping
and geospatial products. Much of OS's data is available via the OS Data Hub, a platform
that hosts both free and premium data products. osdatahub provides a user-friendly way to interact with these data
products
in Python. To see what data is available, you can use
the OS Data Hub Explorer.
- Get access to Ordnance Survey data in as few as 2-3 lines of code
- Easily query geographic extents using bounding boxes, radii and ONS geographies
- Request as much data as you need with automatic API paging
- Supports the OS Features, Places, Names, Linked Identifiers, and Downloads APIs
- GitHub repo: https://github.com/OrdnanceSurvey/osdatahub
- Documentation: https://osdatahub.readthedocs.io/en/latest/
- PyPI: https://pypi.org/project/osdatahub/
- conda-forge: https://anaconda.org/conda-forge/osdatahub
- Data Hub Explorer: https://labs.os.uk/prototyping/data-hub-explorer/
- Free Software: Open Government License
osdatahub is available on PyPI. To install osdatahub, run this command in
your terminal:
pip install osdatahubThe library is also available to download via conda:
conda install -c conda-forge osdatahubYou'll also need to sign-up for an account on the OS Data Hub and get an API key. If you've setup you're account and need help getting a key, try the following steps:
- Navigate to the API Dashboard located on the top navigation bar
- Go to My Projects
- Click Create a new project, give your project a name, then click Create project
- Select Add an API to this project
- Choose the APIs you would like to use and click Done (Note: osdatahub supports the OS Features, Places, Names, Linked Identifiers, and Downloads APIs)
Ordnance Survey's newest API replaces the Features API with extra functionality, better error handling, and an OGC-compliant GeoJSON return type. Currently, the NGD supports topographic features, with Places being added soon.
We can use the NGD API by importing the NGD class (which helps us run queries):
from osdatahub import NGDThen we need to get our OS API key and store it as a variable (find out how to do this securely with environment variables):
key = "[YOUR KEY GOES HERE]"Next, we must decide which NGD Collection we are interested in. We can discover the available collection ids in 2 ways:
- Browse the OS Data Hub Technical Documentation
- Run the
get_collections()function:
NGD.get_collections()Then we can instantiate the NGD class, ready for us to query:
collection = "bld-fts-buildingline"
ngd = NGD(key, collection)
results = ngd.query(max_results=50)The query() function supports many different options and filters, such as various output CRS', CQL filters, and
start and end times for temporal features.
The data stored in the results variable will be in geojson format, limited to
50 features. To save the query results as a geojson file, you need to import
the geojson module and use the .dump()
function:
import geojson
geojson.dump(results, open("FILENAME.geojson", "w"))If you have the ID of a specific feature you would like to query, you can use the query_feature() function instead:
feature_id = "0000013e-5fed-447d-a627-dae6fb215138"
feature = ngd.query_feature(feature_id)Data can be queried within a geographical extent in just a few simple steps!
First, we need to import the FeaturesAPI class (which helps us runs queries) and the Extent class (which helps us to define a target region):
from osdatahub import FeaturesAPI, ExtentThen we need to get our OS API key and store it as a variable (find out how to do this securely with environment variables):
key = "[YOUR KEY GOES HERE]"Next, we define our geographic extent. For this example we're going use a bounding box, but it is also possible to specify radial extents, ONS geographies and custom polygons.
These bounding box coordinates are BNG coordinates in the order (West, South, East, North):
extent = Extent.from_bbox((600000, 310200, 600900, 310900), "EPSG:27700")And now we can run our query! We just have to assemble the parts and decide which OS Features product we want to explore. In this case, we're going to choose "zoomstack_local_buildings" — an open data set of Great Britain's local buildings:
product = "zoomstack_local_buildings"
features = FeaturesAPI(key, product, extent)
results = features.query(limit=50)The data stored in the results variable will be in geojson format, limited to 50 features. To save the query results as a geojson file, you need to import the geojson module and use the .dump() function:
import geojson
geojson.dump(results, open("FILENAME.geojson", "w"))Putting this all together, we get:
from osdatahub import FeaturesAPI, Extent
import geojson
key = "[YOUR KEY GOES HERE]"
extent = Extent.from_bbox((600000, 310200, 600900, 310900), "EPSG:27700")
product = "zoomstack_local_buildings"
features = FeaturesAPI(key, product, extent)
results = features.query(limit=50)
geojson.dump(results, open("FILENAME.geojson", "w"))To run a similar query using the OS Places API, we just need to make two changes. First, we no longer need to decide on a product — the Places API is always going to give us addresses! Secondly, the PlacesAPI class does not require an extent (because there are other, non-geographic, queries available). Therefore, our bounding box extent does not need to be passed in until the query is run.
The final result looks like this:
from osdatahub import PlacesAPI, Extent
import geojson
key = "[YOUR KEY GOES HERE]"
extent = Extent.from_bbox((600000, 310200, 600900, 310900), "EPSG:27700")
places = PlacesAPI(key) # No extent or product is given to PlacesAPI
results = places.query(extent, limit=50) # Extent is passed directly into the .query() function
geojson.dump(results, open("FILENAME.geojson", "w"))Note: The PlacesAPI requires a premium API key!
The OS Data Hub also contains the OS Names API, which is a geographic directory containing basic information about identifiable places. The API allows us to identify places by their address/place name and can find the nearest address to a given point.
The NamesAPI class is very similar to PlacesAPI as it needs only a (premium) API key. We can then query the object with a place name to get more information:
from osdatahub import NamesAPI
key = "[YOUR KEY GOES HERE]"
names = NamesAPI(key) # only a premium key is required to create a NamesAPI object
results = names.find("Buckingham Palace", limit=5)
geojson.dump(results, open("FILENAME.geojson", "w"))Note: The NamesAPI requires a premium API key!
The OS Linked Identifiers API allows you to access the valuable relationships between properties, streets and OS MasterMap identifiers for free. It's as easy as providing the identifier you are interested in and the API will return the related feature identifiers. This allows you to find what addresses exist on a given street, or the UPRN for a building on a map, or the USRN for a road and more.
You can access the Linked Identifiers API via the LinkedIdentifiersAPI class. In it's simplest form, queries can be made using just an API key and an identifier:
from osdatahub import LinkedIdentifiersAPI
key = "[YOUR KEY GOES HERE]"
linked_ids = LinkedIdentifiersAPI(key)
results = linked_ids.query(200001025758)If you'd like to download an entire dataset instead of querying the API on demand, the OS Data Hub has the Downloads API. This API allows you to search,m explore, and download both Open Data Products (e.g. OS Open Rivers, Boundary-Line, and a 1:250,000 scale colour raster of Great Britain) and Premium Data Packages using Python.
It is possible to download Open Data products without an API key, but the Premium Data Packages require you to have a premium API key and order the package you want to download on the OS Data Hub website.
The first step to download data is to discover which products are available. You can see the available datasets on the OS Data Hub website or using the following snippet of code:
from osdatahub import OpenDataDownload
OpenDataDownload.all_products()You can also see all Premium Data Packages available to download using your premium API key:
from osdatahub import DataPackageDownload
key = "[YOUR KEY GOES HERE]"
DataPackageDownload.all_products(key)Note: For Premium Data Packages, this query will only return datasets if you have previously ordered the dataset on the OS Data Hub Website.
Once you have found a package you'd like to download, you can get a list of the different products you can download:
greenspace = OpenDataDownload("OpenGreenspace")
greenspace.product_list()Once you know the dataset and specific product you'd like to download, you can download the dataset locally:
greenspace.download(file_name='opgrsp_essh_nj.zip')Example notebooks, demonstrating various osdatahub features can be found in
the Examples folder. Here is a list of the available content:
- Setting up an API Key
- Defining Extents
- Filtering Attributes
- Plotting Query Results - GeoPandas, Matplotlib, Contextily
- Interactive Plotting for Query Results
- Converting Query Results into Common Formats
- Post Processing Query Results
- Common CRS Pitfalls
To set proxies, use the set_proxies method from osdatahub. This should look something like:
import osdatahub
osdatahub.set_proxies({"http": "http://ip:port", "https": "https://ip:port"})and will apply to all the osdatahub api requests.
This package is still under active developement and we welcome contributions from the community via issues and pull requests.
To install osdatahub, along with the tools you need to develop and run tests, run the following in your environment:
pip install -e .[dev]For any kind of issues or suggestions please see the documentation, open a github issue or contact us via Email [email protected]