This file contains the digital vector boundaries for Counties and Unitary Authorities in the United Kingdom, as at December 2019. The boundaries available are: (BUC) Ultra Generalised (500m) - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights. Download File SizesUltra Generalised (500m) - clipped to the coastline (200 KB)Units for the following fields:St_length = metresSt_area = metres2REST URL of ArcGIS for INSPIRE View Service https://ons-inspire.esriuk.com/arcgis/rest/services/Administrative_Boundaries/Counties_and_Unitary_Authorities_December_2019_Boundaries_UK_BUC2/MapServer/exts/InspireView REST URL of ArcGIS for INSPIRE Feature Download Service https://ons-inspire.esriuk.com/arcgis/rest/services/Administrative_Boundaries/Counties_and_Unitary_Authorities_December_2019_Boundaries_UK_BUC2/MapServer/exts/InspireFeatureDownload REST URL of Feature Access Service https://ons-inspire.esriuk.com/arcgis/rest/services/Administrative_Boundaries/Counties_and_Unitary_Authorities_December_2019_Boundaries_UK_BUC2/FeatureServer

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 20 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 2019 – September 2019. 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.

The 2019 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

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

Point feature class that comprises the photogrammetrically compiled Spot Heights (SH) or digital elevation model (DEM) for Jefferson County, Kentucky in Spring of 2019. These points serve as the DEM for for generating 2-foot terrain surface contours as part of the LOJIC planimetric and topographic database. All elevations are in U.S. Feet and cast on the North American Vertical Datum (NAVD) of 1988. View detailed metadata.

This is a lookup file between local authority districts and counties, metropolitan counties and Greater London in England as at 1st April 2019. (File Size - 48KB)