In 2023, London had a gross domestic product of over 569 billion British pounds, by far the most of any region of the United Kingdom. The region of South East England which surrounds London had the second-highest GDP in this year, at over 360 billion pounds. North West England, which includes the major cities of Manchester and Liverpool, had the third-largest GDP among UK regions, at almost 250 billion pounds. Levelling Up the UK London’s economic dominance of the UK can clearly be seen when compared to the other regions of the country. In terms of GDP per capita, the gap between London and the rest of the country is striking, standing at over 63,600 pounds per person in the UK capital, compared with just over 37,100 pounds in the rest of the country. To address the economic imbalance, successive UK governments have tried to implement "levelling-up policies", which aim to boost investment and productivity in neglected areas of the country. The success of these programs going forward may depend on their scale, as it will likely take high levels of investment to reverse economic neglect regions have faced in the recent past. Overall UK GDP The gross domestic product for the whole of the United Kingdom amounted to 2.56 trillion British pounds in 2024. During this year, GDP grew by 0.9 percent, following a growth rate of 0.4 percent in 2023. Due to the overall population of the UK growing faster than the economy, however, GDP per capita in the UK fell in both 2023 and 2024. Nevertheless, the UK remains one of the world’s biggest economies, with just five countries (the United States, China, Japan, Germany, and India) having larger economies. It is it likely that several other countries will overtake the UK economy in the coming years, with Indonesia, Brazil, Russia, and Mexico all expected to have larger economies than Britain by 2050.

Annual estimates of balanced UK regional gross domestic product (GDP). Current price estimates and chained volume measures for local authority districts, London boroughs, unitary authorities and Scottish Council areas.

The instrument maps and data tables aim to give an insight into the mapping process and datasets used to reconcile the historical data for the households and NPISH financial categories AF.6, AF.7 and AF.8 assets and liabilities.

The Business Structure Database is managed by the Secure Data Service (SDS) and can only be accessed through secure conditions. The ‘domestic use’ input-output matrix, contains domestic trade flows describing intermediate demand between Standard-Industrial-Classification (SIC) coded sectors. This was obtained from the ONS.

GRIT (‘Geospatial Restructuring of Industrial Trade’) is an ESRC-funded project in the School of Geography at the University of Leeds. An energy revolution must take place if the worst effects of climate change are to be avoided. Even without the impact this may have (eg through carbon pricing), fuel costs have a very uncertain future. GRIT has two aims:

create a fine-grained picture of the current spatial structure of the UK economy

consider how changing fuel prices could alter that structure over the long term. GRIT examines the web of connections between businesses in the UK to identify sectors and locations facing the greatest changes.

GRIT will work with a unique dataset: the Business Structure Database contains information for nearly every UK business, including location and sector classification. This will be linked to sectoral trade flow data. These two sources offer an opportunity to map the current spatial distribution of economic activity in the UK and to think about how that distribution may change in the future. GRIT combines this data-driven approach with a plan to engage with organisations directly affected. GRIT will work closely with a small number of organisations and engage others through the project website.

UK air pollution removal A GeoPackage (see https://www.geopackage.org/) that contains the spatial data used in this article:https://www.ons.gov.uk/economy/environmentalaccounts/articles/ukairpollutionremovalhowmuchpollutiondoesvegetationremoveinyourarea/2018-07-30The methodology used to develop estimates for the valuation of air pollution in ecosystem accounts can be found here:https://www.ons.gov.uk/economy/environmentalaccounts/articles/developingestimatesforthevaluationofairpollutioninecosystemaccounts/2017-07-25Download file size: 110 MB

Data sources: England & Wales - Office for National Statistics (ONS)Scotland - National Records of Scotland (NRS)Northern Ireland - Northern Ireland Statistics and Research Agency (NISRA)Coverage: United Kingdom The boundaries used have been generalised using a point remove algorithm for web display using the following thresholds:Euro Regions - 250 metres Local Authorities - 150 metres Middle Super Output Area (MSOA) - 100 metres Lower Super Output Area (LSOA) - 75 metres Output Area (OA) - 50 metres The boundaries have been set to display at the following scale thresholds: Euro Regions - > 1:4,000,000 Local Authorities - 1:300,000 – 1:4,000,000 Middle Super Output Area (MSOA) - 1:100,000 – 1:300,000 Lower Super Output Area (LSOA) - 1:40,000 – 1:100,000 Output Area (OA) - < 1:40,000Ever wondered how Census information can be used in analysis? Take a look at our supermarket and census story map.

Hazards data in Sichuan (Dechang, Anning River catchment), China. Data include rainfall, earthquake, river catchment, boundary, geological map, soil map, land-cover map, road-map, DEM.

A new, robust seismic hazard model and seismic hazard maps for the UK offshore Exclusive Economic Zone using the latest available data and recent advances in seismic hazard methodology and a Monte Carlo-based approach for probabilistic seismic hazard analysis. These are intended to update the current maps for UK waters published in 2002. We developed a comprehensive catalogue of earthquake activity across the region by combining existing earthquake catalogues and data from regional and local monitoring agencies. We modelled earthquake occurrence across the region using a seismic source characterisation (SSC) model that consists of a series of zones, where seismicity is considered to be homogeneous, based on tectonics, geology and seismicity of the study area. We use four different seismic source zone models within the SSC to capture the epistemic uncertainty in different rupture scenarios. A logic tree approach was used to account for the epistemic uncertainty in earthquake activity rates, maximum magnitude, earthquake depth distribution, and faulting style. Ground motions are estimated for different rupture scenarios using a ground motion characterisation (GMC) model that consists of five multiple ground motion prediction equations considered to be applicable to the region. The GMPEs are included in a logic tree where the weights are informed by the fit between observed and modelled ground motions. The GMC model also includes the host-to-target adjustments and a single-station sigma model. Hazard is calculated at 4585 individual points spaced at 0.125° in latitude and 0.25° in longitude for peak ground acceleration (PGA) and spectral acceleration at 0.2 s (SA0.2 s) and 1.0 s (SA1.0 s) for 5% damping and rock conditions and the return periods of 95, 475, 1100, 2475, and 5000 years. This is the first time that maps of the seismic hazard at short (0.2 s) and long periods (1.0 s) have been produced for UK waters. Hazard curves, uniform hazard spectra, and disaggregation analysis have been calculated for three offshore carbon capture and storage sites (Endurance, Acorn, and HyNet North West). New offshore hazard maps for the UK waters will support UK decarbonisation by providing owners and operators of offshore structures with robust estimates of seismic hazard for new and existing sites. The results will also inform regulatory decisions to ensure safe operating practices in the industry and help identify areas of higher hazard where further site-specific studies might be needed. Finally, it will provide a robust baseline for tectonic seismic activity in the North Sea that can be used to help discriminate any seismicity induced by operations, such as CCS, in the event it occurs. The technical report by Mosca et al. (2024), which provides a detailed description of the methods and results, together with the project’s products, can be found in (http://www.earthquakes.bgs.ac.uk/hazard/UKhazard.html).

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

This project systematically processed high-resolution and manuscript historical maps to unlock a dormant body of information about the historical development of cities and regions during periods of structural economic transformation.

The work was organised across six interlinked work packages, combining empirical and theoretical analysis in the UK, France, and Canada. Outputs included peer-reviewed publications and robust algorithms for extracting spatial data from historical sources, contributing valuable tools and insights to the fields of urban economics and economic history.

This data package contains three segmentation codes designed to extract features and segment historical maps.

Little is known about the patterns of city development during the structural transformation of economies. This project will systematically process high-resolution and manuscript historical maps to make a dormant body of information about our cities' and regions' past accessible.

The proposed research will advance our understanding of long-run urban growth through the development of three innovative methodologies, which will overcome practical limitations of historical data sources: 1) A technique to extract land use patterns from historical colour maps applied to France (1750-1950); 2) A recognition algorithm to detect, tag and geo-locate points of interest in historical high-quality maps of the 70 largest urban centre in England and Wales; 3) An algorithm to geo-locate address information from Micro-censuses and trade registers.

We have identified four main research questions that will be developed in the following separate research projects. In Project 1, the main question is: what are the long-term empirical patterns of urban development, most notably the persistence of the spatial organisation of economic activity and the role of building infrastructure in shaping such persistence? In Project 2, the main question is: How do environmental disamenities and their unequal distribution within cities affect the spatial organisation of consumption amenities and production? In Project 3, the main question is: Do cities grow towards their bad parts, their neighbourhoods with the lowest environmental amenities? In Project 4, the main question is: How does vertical growth and advances in building technologies affect the spatial organisation of cities?

To address these research questions, we will organise our workflow in six inter-connected work packages (WP):

WP1--Classification of land use in France (1750-2015): The objective of WP1 will be to recover land use information at a fine scale from digitised maps using state-of-the-art machine learning techniques;

WP2--Digitisation of micro-features embedded in Ordnance Survey (OS) city maps of England and Wales (1870-1960);

WP3--Geo-localization of residents and production units in England and Wales (1851-1911);

WP4--Dynamic model of city growth with persistent building stock: WP4 builds a general equilibrium model of spatial economic activity that embeds the durability of housing and infrastructure and exploits the three hundred years of population settlement data produced in WP1;

WP5--Pollution and the long-run development of cities: WP5 builds on WP2,3 and proposes to study the joint dynamics of residential sorting and the location of production within cities to understand how a major environmental disamenity-industrial pollution-affects the spatial organisation of cities in the longer-run;

WP6--Horizontal and vertical urban growth in Montreal and Toronto: WP6 will bridge between the previous working packages WP1, WP2, WP4 and WP5, and study--empirically and theoretically--horizontal and vertical urban growth.

The project will be jointly led by three teams. The French team will be composed of Gobillon (PI), Combes (CoI) and Duranton (TM) who have contributed to the development of major theoretical approaches in urban economics. The Canadian team will be led by Heblich (PI), who is a lead researcher in urban economics/economic history, and Fortin (Co-I), a lead in GIS analysis. The UK team will be led by Zylberberg (PI), who is an economist specialist in data extraction form historical sources and remote sensing. Shaw-Taylor and Schürer, advisory board, will help design the analysis of the population micro-censuses between 1851 and 1911 (WP3). The collaboration partner, Redding (TM), involved in the design of WP3 and the implementation of WP6, is one of the World lead researchers in urban economics.

Outputs will include articles in top economic journals, and detailed algorithms to extract relevant spatial information from manuscript maps.

Understanding the size and spatial distribution of material stocks is crucial for sustainable resource management and climate change mitigation. This study presents high-resolution maps of buildings and mobility infrastructure stocks for the United Kingdom (UK) and the Republic of Ireland (IRL) at 10 m, combining satellite-based Earth observations, OpenStreetMaps, and material intensities research. Stocks in the UK and IRL amount to 19.8 Gigatons or 279 tons/cap, predominantly aggregate, concrete and bricks, as well as various metals and timber. Building stocks per capita are surprisingly similar across medium to high population density, with only the lowest population densities having substantially larger per capita stocks. Infrastructure stocks per capita decrease with higher population density. Interestingly, for a given building stock within an area, infrastructure stocks are substantially larger in IRL than in the UK. These maps can provide useful insights for sustainable urban planning and advancing a circular economy.

This dataset features a detailed map of material stocks in the United Kingdom and the Republic of Ireland on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent
This dataset covers the whole British Isles. Due to processing reasons, the dataset is internally structured into the Island of Ireland, and the Island of Great Britain.

Temporal extent
The map is representative for ca. 2018.

Data format
The data are organized by nations. Within each nation, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

• t at 10m x 10m
• kt at 100m x 100m
• Mt at 1km x 1km
• Gt at 10km x 10km

For each nation, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per nation is tabulated for each island in mass_grand_total_t_10m2.tif.csv. County code and the ID in this table can be related via zones_name_pop.csv.

Material layers
Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials).

Further information
For further information, please see the publication.
Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication

D. Wiedenhofer, F. Schug, H. Gauch, M. Lanau, M. Drewniok, A. Baumgart, D. Virág, H. Watt, A. Cabrera Serrenho, D. Densley Tingley, H. Haberl, D. Frantz (2024): Mapping material stocks of buildings and mobility infrastructure in the United Kingdom and the Republic of Ireland. Resources, Conservation and Recycling 206, 107630. https://doi.org/10.1016/j.resconrec.2024.107630

Funding
This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Acknowledgments
We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

Using our global network in over 100 countries, the British Council creates international opportunities for people across the UK. Find out more about some of the cultural relations opportunities we provided last year for UK organisations by clicking on the map. In 2010-11 the British Council worked with over 9200 UK organisations on 183 initiatives, involving a total of 167 different countries. We work in Education and Society, the Arts and English Language. We help equip young people in the UK for life in a global society and work in a global economy. We help position the UK as an enabler for countries, communities and individuals to access quality education, life changing experiences and greater opportunities.

What does the data show?

The data shows the S80/S20 income quintile ratio from the UK Climate Resilience Programme UK-SSPs project. The data is available for each ONS NUTS3 shape simplified to a 10m resolution.

The S80/S20 ratio is a measure of the inequality of income distribution. The ratio is the total income received by the 20% of the population with the highest income (the top quintile) against the total income received by the 20% of the population with the lowest income (the bottom quintile).

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Inequality

Metric

S80/S20 income quintile ratio

Unit

Ratio [unitless]

Spatial Resolution

NUTS 3

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

OECD 2011

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

The data describes future land use projections at 1 km^2 resolution developed by CRAFTY-GB. For each of six Shared Socioeconomic Pathways (SSP-RCP) scenarios, gridded land use maps for Great Britain are provided, each as a stacked raster file with seven bands representing land use at each decadal timestep, from 2020 to 2080. CRAFTY-GB is a new agent-based model of the British land system operating at a 1 km^2 resolution and based on a broad range of available land system data . The model is based on linked UK-RCP climate scenarios and UK-SSPs socio-economic pathway (SSP) scenarios, based on global SSPs developed by the Intergovernmental Panel on Climate Change (IPCC). It extrapolates the impact of these on the British Land system at decadal timesteps from 2020-2080. Full details about this dataset can be found at https://doi.org/10.5285/f9ab3051-4f85-415f-b691-371ff8e951f2

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

City Development Plan Policy CDP3 - Economic Development - These are areas identified as Economic Development Areas. It includes hospital and university campuses (Consulted in Summer 2014 and amended at Reporters recommendation Autumn 2016).

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.

The BGS Seabed Sediments 250k dataset is vector data which reflects the distribution of seabed substrate types of the UK and some of its adjacent waters (the UK Exclusive Economic Zone, EEZ) at 1:250,000 scale. This comprehensive dataset provides a digital compilation of the paper maps published by BGS at the same scale, as well as additional re-interpretations from regional geological studies. The seabed is commonly covered by sediments that form a veneer or thicker superficial layer of unconsolidated material above the bedrock. These sediments are classified based on their grain size, which reflects the environment in which they were deposited. This information is important to a range of stakeholders, including marine habitat mappers, marine spatial planners and offshore industries (in particular, the dredging and aggregate industries). This dataset was primarily based on seabed grab samples of the top 0.1 m, combined with cores, dredge samples and sidescan sonar acquired during mapping surveys since the early 1970s. Variations in data density are reflected in the detail of the mapping. The sediment divisions on the map are primarily based on particle size analysis (PSA) of both surface sediment samples and the uppermost sediments taken from shallow cores. Sediments are classified according to the modified Folk triangle classification (Folk, 1954, Journal of Geology, Vol. 62, pp 344–359). The modified Folk diagram and classification used by BGS differs from that created by Folk (1954) in that the boundary between 'no gravel' and 'slightly gravelly' is changed from trace (0.05%) to 1% weight of particles coarser than -1Ø (2mm), shown below. The boundaries between sediment classifications or types are delineated using sample station particle size analyses and descriptions, seafloor topography derived from shallow geophysical and, where available, multibeam bathymetry, backscatter and side-scan sonar profiles. This dataset was produced for use at 1:250 000 scale. These data should not be relied on for local or site-specific geology.