{"id":34983,"date":"2017-11-20T21:54:02","date_gmt":"2017-11-20T21:54:02","guid":{"rendered":"https:\/\/oxforditrc.wpengine.com\/?post_type=itrcpublications&#038;p=34983"},"modified":"2020-07-14T12:54:31","modified_gmt":"2020-07-14T11:54:31","slug":"ukcensusapi-python-and-r-interfaces-to-the-nomisweb-uk-census-data-api","status":"publish","type":"itrcpublications","link":"https:\/\/www.itrc.org.uk\/itrcpublications\/ukcensusapi-python-and-r-interfaces-to-the-nomisweb-uk-census-data-api\/","title":{"rendered":"UKCensusAPI: python and R interfaces to the nomisweb UK census data API"},"content":{"rendered":"<p>Nomisweb (\u201cNomis \u2013 Official Labour Market Statistics\u201d 2017) provide an extremely useful API for querying and downloading UK census data. However, in practice data queries must be built manually and the query URL copied and pasted into user code. This makes modification of queries laborious and this is especially so when (re)defining the geographical coverage and resolution of a query.<\/p>\n<p>This package (Smith 2017) provides both python and R interfaces around the nomisweb API that address these shortcomings. It contains functionality to: \u2013 query tables directly for their metadata \u2013 autogenerate customised python and R query code for reuse \u2013 automate and cache data and metadata downloads \u2013 easily modify the geographical coverage and resolution of existing queries \u2013 add descriptive information to downloaded tables (from metadata).<\/p>\n<p>This is particularly useful in applications such as microsimulation, where there are requirements to run the model for different geographical areas and\/or different geographical resolutions with minimal user\/developer intervention.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nomisweb (\u201cNomis \u2013 Official Labour Market Statistics\u201d 2017) provide an extremely useful API for querying and downloading UK census data. However, in practice data queries must be built manually and the query URL copied and pasted into user code. This makes modification of queries laborious and this is especially so when (re)defining the geographical coverage [&hellip;]<\/p>\n","protected":false},"template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"areas":[44,86],"class_list":["post-34983","itrcpublications","type-itrcpublications","status-publish","hentry","areas-population","areas-population-publications"],"_links":{"self":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/itrcpublications\/34983","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/itrcpublications"}],"about":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/types\/itrcpublications"}],"version-history":[{"count":1,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/itrcpublications\/34983\/revisions"}],"predecessor-version":[{"id":34984,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/itrcpublications\/34983\/revisions\/34984"}],"wp:attachment":[{"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/media?parent=34983"}],"wp:term":[{"taxonomy":"areas","embeddable":true,"href":"https:\/\/www.itrc.org.uk\/wp-json\/wp\/v2\/areas?post=34983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}