A PDF map that shows the counties and unitary authorities in the United Kingdom as at 1 April 2020. (File Size - 472 KB)

The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 15 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 Radar and Sentinel-2 Optical Satellite images during the period late January 2020 – September 2020. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. The data has been split into the Ordnance Survey Ceremonial Counties and each county is given a three letter code. Please refer to the CROME specification document to see which county each CODE label represents.

A PDF map that shows the regions and their constituent counties and unitary authorities in the United Kingdom as at 1 April 2020. (File Size - 557 KB)

A PDF map that shows the counties and unitary authorities in the United Kingdom as at 1 April 2020. (File Size - 472 KB)

This file contains the digital vector boundaries for Counties, in England, as at December 2020.The boundaries available are: (BFC) Full resolution - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_(December_2020)_EN_BFE/MapServerREST URL of ArcGIS for INSPIRE Feature Download Service – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Counties_December_2020_EN_BFE/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_December_2020_EN_BFE_2022/FeatureServer

This file contains the digital vector boundaries for Counties and Unitary Authorities, in the United Kingdom, as at December 2020.The boundaries available are: (BGC) Generalised (20m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of Map Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_and_Unitary_Authorities_(December_2020)_UK_BGC/MapServerREST URL of WFS Service – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Counties_and_Unitary_Authorities_December_2020_UK_BGC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_and_Unitary_Authorities_December_2020_UK_BGC_2022/FeatureServer

This file contains the digital vector boundaries for Counties, in England, as at December 2020.The boundaries available are: (BGC) Generalised (20m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_(Dec_2020)_EN_BGC/MapServerREST URL of ArcGIS for INSPIRE Feature Download Service – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Counties_December_2020_EN_BGC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_Dec_2020_EN_BGC_2022/FeatureServer

In 2024, over nine million people lived in Greater London, making it the most populated ceremonial county in England. The West Midlands Metropolitan County, which contains the large city of Birmingham, was the second-largest county at just over 3.03 million, closely followed by Greater Manchester at three million, and then West Yorkshire with a population of 2.4 million. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with just over 1.9 million people. A patchwork of regions England is just one of the four countries that compose the United Kingdom of Great Britain and Northern Ireland, with England, Scotland and Wales making up Great Britain. England is therefore not to be confused with Great Britain or the United Kingdom as a whole. Within England, the next subdivisions are the nine regions of England, containing various smaller units such as unitary authorities, metropolitan counties and non-metropolitan districts. The counties in this statistic, however, are based on the ceremonial counties of England as defined by the Lieutenancies Act of 1997. Regions of Scotland, Wales, and Northern Ireland Like England, the other countries of the United Kingdom have their own regional subdivisions, although with some different terminology. Scotland’s subdivisions are council areas, while Wales has unitary authorities, and Northern Ireland has local government districts. As of 2024, the most-populated Scottish council area was Glasgow City, with over 650,000 inhabitants. In Wales, Cardiff had the largest population among its unitary authorities, and in Northern Ireland, Belfast was the local government area with the most people living there.

🇬🇧 영국

This file contains the digital vector boundaries for Counties and Unitary Authorities, in the United Kingdom, as at December 2020.The boundaries available are: (BFC) Full resolution - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_and_Unitary_Authorities_(December_2020)_UK_BFC/MapServerREST URL of ArcGIS for INSPIRE Feature Download Service – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Counties_and_Unitary_Authorities_December_2020_UK_BFC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Counties_and_Unitary_Authorities_December_2020_UK_BFC_2022/FeatureServer

Two vector (.shp) files are provided. The first, (macro assemblages.shp) shows the modelled (random forest) macrofaunal assemblage type based on a clustering of abundance data from the OneBenthic database (see https://sway.office.com/HM5VkWvBoZ86atYP?ref=Link). The second file, (macro assemblages confidence.shp) shows associated confidence in the modelled output, with darker shades (high values) indicating higher confidence and lighter shades (lower values) indicating lower confidence. Both layers can be viewed in the OneBenthic Layers tool (https://rconnect.cefas.co.uk/onebenthic_layers/), together with further details of the methodology used to produce them.

The modelled layer for macrofaunal assemblage is based on a random forest modelling of point sample data from the OneBenthic (OB, https://rconnect.cefas.co.uk/onebenthic dashboard/) dataset, largely following the methodology in Cooper et al. (2019), but with an expanded dataset covering the Greater North Sea and including data from the EurOBIS (https://www.eurobis.org/) data repository. Of the 44,407 samples within OB, we selected a subset of 31,845 for which data were considered comparable (i.e. sample acquired using a 0.1 m2 grab or core, processed using a 1 mm sieve and not taken from a known impacted site). Colonial taxa were included and given a value of one. To take account of potential differences in taxonomic resolution between surveys, macrofaunal data were aggregated to family level using the taxonomic hierarchy provided by the World Register of Marine Species (https://www.marinespecies.org/). This reduced the number of taxa from 3,659 to 750. To address spatial autocorrelation in the data, and in keeping with the previous approach, samples closer than 50 m were removed from the dataset, reducing the overall number to 18,348. A fourth-root transformation was then applied to the data to down weight the influence of highly abundant taxa. Data were then subjected to clustering using k-means. A species distribution modelling approach, based on random forest, was then used to model cluster group (i.e. macrofaunal assemblage or biotope) identity across the study area (Greater North Sea). Cross-validation via repeated sub-sampling was done to evaluate the robustness of the model estimate and predictions to data sub-setting and to extract additional information from the model outputs to produce maps of confidence in the predicted distribution, following the approach described in Mitchell et al. (2018). The cross-validation was done on 10 split sample data sets with 75% used to train and 25% to test models, randomly sampled within the levels of the response variable to maintain the class balance. The final model output was plotted as the cluster class with the majority vote of all 10 model runs. An associated confidence map was produced by multiplying map layers for 1) the frequency of the most common class and ii) the average probability of the most common class. Model outputs are used in the OneBenthic Layers Tool (https://rconnect.cefas.co.uk/onebenthic_layers/).

Cooper, K.M.; Bolam, S.G.; Downie, A.-L.; Barry, J. 2019. Biological-based habitat classification approaches promote cost-efficient monitoring: An example using seabed assemblages. J. Appl. Ecol. 56:1085–1098. https://doi.org/10.1111/1365-2664.13381

Mitchell, P.J., Downie, A.-L., Diesing, M. How good is my map? 2018. A tool for semi-automated thematic mapping and spatially explicit confidence assessment. Env. Model. Softw. 108, 111–122. https://doi.org/10.1016/j.envsoft.2018.07.014

🇺🇸 미국 English Loudoun County, Office of Mapping and Geographic Information (OMAGI) has a seamless countywide base map geodatabase that can be used as a reference when mapping all other data. Base Map data layers include planimetric (buildings, roads, miscellaneous cultural features), environmental (hydrology, forest cover), and topographic (elevation contours and spot heights) features. Each base map feature has an update date field associated with it which shows the year when that particular feature was last updated.The countywide remapping project, conducted in two phases, was undertaken to produce the current data set. Phase I, using 2002 Virginia Base Mapping Program digital scanned imagery and Phase II, using 2004 scanned aerial photography, comprises the initial re-map effort. The initial project was completed in 2005 and now undergoes annual updates. The first annual update, Phase III, was​ derived from 2005 imagery and completed in fall 2006. The second annual update, Phase IV, was derived from 2007 imagery and completed in spring 2007. Phase V of the base map updates was completed in late 2008. Most recent updates were derived from 2024 imagery and completed in Summer 2025. Disclaimer Loudoun County is not liable for any use of or reliance upon this map or data, or any information contained herein. While reasonable efforts have been made to obtain accurate data, the County makes no warranty, expressed or implied, as to its accuracy, completeness, or fitness for use of any purpose.

This dataset provides digital spatial information on the location of mineral resources in England at a scale of 1:50 000. The term ‘mineral resources’ has a definition under international standards that includes both an economic and geological dimension. These data are based primarily on mapped geology with limited assessment of economics. Therefore, the term ‘mineral resources’ is used here in a broad sense. The dataset allows users to visualise the extent and distribution of mineral resources and to relate them to other forms of land-use (such as urban areas or designated environmentally sensitive areas) or to other factors (such as transport infrastructure and conservation information). The dataset is derived from a set of commissioned projects to prepare a series of mineral resource maps based on counties or amalgamations of counties. Maps for England were commissioned by the central government department with responsibility for mineral planning at the time (Department of the Environment (DoE), Department of the Environment, Transport and the Regions (DETR), Department for Transport, Local Government and the Regions (DTLR), Office of the Deputy Prime Minister (ODPM), and the Department for Communities and Local Government (DCLG) between 1994 and 2006. Each map produced (with an accompanying report describing the mineral resources depicted on the map) is available to download, as a PDF file from the BGS-hosted website: www.MineralsUK.com. During 2011-2012 revisions were made to areas of the resource linework. These changes were made as a result of new research and release of a new version of DiGMap (v5). This work was on an ad hoc basis but affects all resource layers. In 2020 minor revisions to geometry and attributes were made in in response to minor corrections that were required. The paper maps were not re-released with these data updates. The BGS Mineral Resource data does not determine mineral reserves and therefore does not denote potential areas of extraction. Only onshore, mainland mineral resources are included in the dataset. This dataset has been produced by the collation and interpretation of mineral resource data principally held by the British Geological Survey. The mineral resource data presented are based on the best available information, but are not comprehensive and their quality is variable. The dataset should only be used to show a broad distribution of those mineral resources which may be of current or potential economic interest.

This file contains the names and codes for County Electoral Divisions in England as at 7th May 2020. (File Size - 184 KB) Field Names – CED20CD, CED20NM, FIDField Types – Text, Text, NumberField Lengths – 9, 55FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portalREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/County_Electoral_Division_May_2020_Names_and_Codes_for_England_2022/FeatureServer

This spatial dataset is an output of the Natural England County & City Natural Capital Atlas project (July 2020). It shows variation in ecosystem service flow for habitats across England, based on indicators identified by NE in the 2018 Natural Capital Indicators project. The dataset comprises a hexagonal grid which summarises indicator values across the country (each unit = 5km²).Natural Capital is an important aspect of current environmental policy and management. This dataset, in combination with the other project outputs, will support understanding of Natural Capital in England and serve as a valuable engagement tool to communicate concepts of the Natural Capital approach to a wide variety of stakeholders.

This layer shows language or language groups spoken at home by English ability. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of individuals age 5+ who are bilingual in English and another language (speak English very well and speak another language at home). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): C16001 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.