In November 2024, Google.com was the leading website in the United Kingdom with more than 4.16 billion monthly visits. The search engine was also popular in its UK top-level domain, with Google.co.uk reaching 267 million views and placing tenth in the ranking. YouTube and Facebook were the most visited social media platforms, ranking as the second and fifth most visited websites in the country.

The total number of unique visitors to the authority's website in the period, excluding visitors from the authority's own staff.

In 2019, seven percent of total enterprises in the United Kingdom (UK) sold products via websites or apps to other European countries. This is a one percent decrease from 2017.

United Kingdom UK: Terrestrial Protected Areas: % of Total Land Area data was reported at 28.683 % in 2017. This records an increase from the previous number of 28.240 % for 2016. United Kingdom UK: Terrestrial Protected Areas: % of Total Land Area data is updated yearly, averaging 28.462 % from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 28.683 % in 2017 and a record low of 28.240 % in 2016. United Kingdom UK: Terrestrial Protected Areas: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded.; ; World Database on Protected Areas (WDPA) where the compilation and management is carried out by United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with governments, non-governmental organizations, academia and industry. The data is available online through the Protected Planet website (https://www.protectedplanet.net/).; Weighted average; Restricted use: Please contact the Protected Planet for third-party use of these data.

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

United Kingdom UK: Surface Area data was reported at 243,610.000 sq km in 2017. This stayed constant from the previous number of 243,610.000 sq km for 2016. United Kingdom UK: Surface Area data is updated yearly, averaging 243,610.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 243,610.000 sq km in 2017 and a record low of 243,610.000 sq km in 2017. United Kingdom UK: Surface Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

A breakdown of the Food Hygiene Ratings website traffic data showing total number of visits, unique visitors and page views.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.2963 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.3221 and 0.1183 (in million kms), corressponding to 24.8447% and 9.1242% respectively of the total road length in the dataset region. 0.856 million km or 66.031% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0104 million km of information (corressponding to 1.2101% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Abstract copyright UK Data Service and data collection copyright owner.

This project had as its aim the production of a reliable time series of government expenditure by function in the United Kingdom. To generate the new data, a consistent functional coding scheme was applied to the basic raw 'atomic' data from the General Expenditure Statistics (GES) team at HM Treasury. The procedures used are described fully in the documentation for this dataset.

Further information about the research is available on the Degrees of Democracy project web site.
Main Topics:

The dataset includes new data for each expenditure function covered from 1980-2000. The rows in the data file designate functions and the columns years. All spending figures are in current Pounds Sterling.

For Culture, Media and Sport (CS) and Social Security (SS) two sets of numbers are provided: