DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Full metadata can be found at: data.gov.uk

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.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2015 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2015 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

The population of the United Kingdom in 2023 was estimated to be approximately 68.3 million in 2023, with almost 9.48 million people living in South East England. London had the next highest population, at over 8.9 million people, followed by the North West England at 7.6 million. With the UK's population generally concentrated in England, most English regions have larger populations than the constituent countries of Scotland, Wales, and Northern Ireland, which had populations of 5.5 million, 3.16 million, and 1.92 million respectively. English counties and cities The United Kingdom is a patchwork of various regional units, within England the largest of these are the regions shown here, which show how London, along with the rest of South East England had around 18 million people living there in this year. The next significant regional units in England are the 47 metropolitan and ceremonial counties. After London, the metropolitan counties of the West Midlands, Greater Manchester, and West Yorkshire were the biggest of these counties, due to covering the large urban areas of Birmingham, Manchester, and Leeds respectively. Regional divisions in Scotland, Wales and Northern Ireland The smaller countries that comprise the United Kingdom each have different local subdivisions. Within Scotland these are called council areas whereas in Wales the main regional units are called unitary authorities. Scotland's largest Council Area by population is that of Glasgow City at over 622,000, while in Wales, it was the Cardiff Unitary Authority at around 372,000. Northern Ireland, on the other hand, has eleven local government districts, the largest of which is Belfast with a population of around 348,000.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

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.
Request Free Sample

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 app

Surface features are produced as a result of internal deformation of active landslides, and are continuously created and destroyed by the movement. The detailed mapping of the evolution of bare ground patches and vegetation cover pattern over time provides useful insights on the mass movements and the most vulnerable areas. Monitoring the evolution of the latter, in turn, could help to describe the benefits of NBS past their implementation by observing a reduction of vulnerable areas and the increase of the extent of stable vegetation as a result of the interventions. The preliminary analysis of satellite data on OAL-UK was devoted to the analysis of a time series of very high resolution multispectral satellite data (<1 m) in the period prior to the implementation of NBS. The goal was to explore the potential of remote sensing to describe the pattern and extent of vegetation cover and plant cover regeneration, as well as to observe the self-organisation of landslide scars - i.e. re-distribution of bare ground patches over time.

The dataset includes Land Cover maps obtained by Worldview 2 satellite images acquired between 2011 and 2016 in different seasons, notably on 29 Jun 2011, 22 April 2014, 08 June 2014 and 22 Feb 2016.

After the pre-processing phase, Principal Component Analysis (PCA) was applied on the pansharpened multispectral bands to reduce the dimensionality of each dataset (pansharpened multispectral bands), while retaining as much as possible its information content. (Richardson, 2009).

A machine learning unsupervised classification (K-means) was applied to the first three PC found by PCA. K-means is an unsupervised classification algorithm that groups objects into k groups based on their characteristics. The mean spectral reflectance values of the samples assigned to each cluster was analyzed to identify typical spectral profiles of land features as well as eventual similarities between the classes and successively used to assign labels to the unsupervised classes. In cases of spectral similarity between two or three clusters, thy were merged in a unique class.

The results of k-means classification were analyzed to define typical spectral profiles of land cover features found in OAL-UK. First, the mean and standard deviation of the spectral bands of each cluster and image were calculated and compared with spectral libraries of land features. This led to identify 11 land cover classes with typical and recurrent spectral profiles within the OAL:

1. Bare soil

2. urban materials and sea foam (uniform spectra)

3. water saturated soil

4. water logged soil/vegetation

5. vegetation (grass)

6. dense vegetation (shrubs)

7. vegetation with exposed soil

8. soil with sparse vegetation

9. mixed wet soil/vegetation

10. Water

11. Turbid Water

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

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.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office generally uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer, and have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. For an example of an urban heat island map during an average summer, see this dataset. For an example of an urban heat island map during a warm summer, see this dataset. As well as outdoor temperature, an individual’s heat exposure may also depend on the type of building they are inside, if indoors. Indoor temperature exposure may depend on a number of characteristics, such as the building geometry, construction materials, window sizes, and the ability to add extra ventilation. It is also known that people have different vulnerabilities to heat, with some more prone to negative health issues when exposed to high temperatures. This Triple Jeopardy dataset combines: Urban Heat Island information for London, based on the 55 days between May 26th -July 19th 2006, where the last four days were considered a heatwave An estimate of the indoor temperatures for individual dwellings in London across this time period Population age, as a proxy for heat vulnerability, and distribution From this, local levels of heat-related mortality were estimated using a mortality model derived from epidemiological data. The dataset comprises four layers: Ind_Temp_A – indoor Temperature Anomaly is the difference in degrees Celsius between the estimated indoor temperatures for dwellings and the average indoor temperature estimate for the whole of London, averaged by ward. Positive numbers show dwellings with a greater tendency to overheat in comparison with the London average HeatMortpM – total estimated mortality due to heat (outdoor and indoor) per million population over the entire 55 day period, inclusive of age effects HeatMorUHI – estimated mortality per million population due to increased outdoor temperature exposure caused by the UHI over the 55 day period (excluding the effect of overheating housing), inclusive of age effects HeatMorInd - estimated mortality per million population due to increased temperature exposure caused by heat-vulnerable dwellings (excluding the effect of the UHI) over the 55 day period, inclusive of age effects More information is on this website and in the Triple Jeopardy leaflet. The maps are also available as one combined PDF. More information is on this website and in the Triple Jeopardy leaflet.

Xverum’s Location Data is a highly structured dataset of 230M+ verified locations, covering businesses, landmarks, and points of interest (POI) across 5000 industry categories. With accurate geographic coordinates, business metadata, and mapping attributes, our dataset is optimized for GIS applications, real estate analysis, market research, and urban planning.

With continuous discovery of new locations and regular updates, Xverum ensures that your location intelligence solutions have the most current data on business openings, closures, and POI movements. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into mapping, navigation, and geographic analysis platforms.

🔥 Key Features:

Comprehensive Location Coverage: ✅ 230M+ locations worldwide, spanning 5000 business categories. ✅ Includes retail stores, corporate offices, landmarks, service providers & more.

Geographic & Mapping Data: ✅ Latitude & longitude coordinates for precise location tracking. ✅ Country, state, city, and postal code classifications. ✅ Business status tracking – Open, temporarily closed, permanently closed.

Continuous Discovery & Regular Updates: ✅ New locations added frequently to ensure fresh data. ✅ Updated business metadata, reflecting new openings, closures & status changes.

Detailed Business & Address Metadata: ✅ Company name, category, & subcategories for industry segmentation. ✅ Business contact details, including phone number & website (if available). ✅ Operating hours for businesses with scheduling data.

Optimized for Mapping & Location Intelligence: ✅ Supports GIS, real estate analysis & smart city planning. ✅ Enhances navigation & mapping solutions with structured geographic data. ✅ Helps businesses optimize site selection & expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered via S3 Bucket or cloud storage for full dataset access. ✅ Available in a structured format (.json) for easy integration.

🏆 Primary Use Cases:

Location Intelligence & Mapping: 🔹 Power GIS platforms & digital maps with structured geographic data. 🔹 Integrate accurate location insights into real estate, logistics & market analysis.

Retail Expansion & Business Planning: 🔹 Identify high-traffic locations & competitors for strategic site selection. 🔹 Analyze brand distribution & presence across different industries & regions.

Market Research & Competitive Analysis: 🔹 Track openings, closures & business density to assess industry trends. 🔹 Benchmark competitors based on location data & geographic presence.

Smart City & Infrastructure Planning: 🔹 Optimize city development projects with accurate POI & business location data. 🔹 Support public & commercial zoning strategies with real-world business insights.

💡 Why Choose Xverum’s Location Data? - 230M+ Verified Locations – One of the largest & most structured location datasets available. - Global Coverage – Spanning 249+ countries, with diverse business & industry data. - Regular Updates – Continuous discovery & refresh cycles ensure data accuracy. - Comprehensive Geographic & Business Metadata – Coordinates, addresses, industry categories & more. - Bulk Dataset Delivery (NO API) – Seamless access via S3 Bucket or cloud storage. - 100% Compliant – Ethically sourced & legally compliant.

Access Xverum’s 230M+ Location Data for mapping, geographic analysis & business intelligence. Request a free sample or contact us to customize your dataset today!

In 2023, almost 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 2.98 million inhabitants, followed by Greater Manchester and then West Yorkshire with populations of 2.95 million and 2.4 million, respectively. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with around 1.89 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 2022, the most-populated Scottish council area was Glasgow City, with over 622,000 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.

The Geological Survey of Northern Ireland (GSNI) and the British Geological Survey (BGS) completed a regional geochemical survey of Northern Ireland's soils, sediments and waters between 2004 and 2006.

Soil sampling of all of Northern Ireland was completed under the Tellus survey between 2004 and 2006. Soil samples were collected on a systematic basis from rural areas in most of the region, excluding only the major urban centres of Belfast and Bangor. Soils were also collected at a higher sampling density from the urban areas of Belfast, Bangor, Carrickfergus, Carryduff, Castlereagh, Greenisland, Holywood, Lisburn, Newtownabbey and Londonderry, although these urban results are not reported here.

In rural areas, samples were collected from alternate 1 km Irish national- grid squares. Site selection within each square was random, subject to the avoidance wherever possible of roads, tracks, railways, human habitation and other disturbed ground. At each site two composite samples of five auger flights were collected, each composite sample comprising approximately 750 g of unsieved material. Samples were collected using a hand auger with a 20 by 5 cm flight from a standard depth interval of 5–20 cm for designated ‘A’ samples, referred to subsequently as ‘surface soils’, and at 35–50 cm for designated ‘S’ samples (nominally the B horizon), referred to subsequently as ‘deep soils’. Some 6,862 regional soil sites were sampled (see supplementary map - soil locations) and analysed, resulting in an average regional sampling density of 1 site per 2 km2. Observations of soil colour, depth, clast lithology and abundance were recorded at site. The samples were classified into five textural groups (sand, sand-silt, silt, silt-clay and clay).

The methods used for urban soils were similar except that (1) the sample density was higher, at four sites per square kilometre; (2) the sample sites corresponded closely to a predefined grid and did not avoid areas of human influence. In addition, extra samples requiring special treatment were taken for the determination of selected organic constituents (Smyth, 2009: especially Appendix 1).

At each soil sample site, information on the location, site and catchment geology, contamination, land use, and other features required for data interpretation were entered onto field cards. The sample location was also plotted on a field copy of the 1:50 000 Ordnance Survey of Northern Ireland (OSNI) map.

Observations from field cards were entered into a digital Access2000™ database after undergoing a field quality control process (Lister et al, 2005). This involved checking that the correct codes had been recorded on field cards and that GPS coordinates recorded on the card matched those in the GPS unit for each site. Thus both a traditional paper archive of observations was maintained as well as the construction of a computerised database.

Soils were initially air-dried at the field-base prior to transport to the sample store where they were dried in a temperature controlled oven at 30°C for 2–3 days. At the end of each field campaign samples were checked against field sheets prior to packing for transport to the BGS laboratory for sample preparation. On arrival at the laboratory samples were checked against shipping lists prior to assigning laboratory batch numbers in the BGS UKAS Quality Assurance System. The A and S soils were prepared in the same manner in a trace-level sample preparation laboratory.

Samples were disaggregated prior to sieving to a <2 mm fraction using nylon mesh. Replicate samples were prepared by riffle splitting each of the duplicate samples. Soil pH and LOI was determined for every A surface soil sample. A representative 30 g (± 2 g) sub-sample was obtained by cone and quartering. This sub-sample was then milled in an agate ball mill at 300 rpm for 30 minutes.

Different analytical procedures were employed for the surface and deep soils. Pressed pellet production and XRF analysis were completed by laboratory on surface soils only. Sub-samples of milled soil were weighed and placed into tamper-evident plastic sample tubes. The XRF pressed pellet was prepared by adding an aliquot (3 g ±0.05 g) of two blended synthetic waxes comprising 90 % EMU 120 FD wax and 10 % Ceridust (both waxes are styrene based co-polymers) to 12 g (± 0.05 g) of milled material. This mixture was milled for 4 minutes at 300 rpm. On completion of the binder milling the prepared powders were placed into tamper evident plastic sample tubes for temporary storage prior to pellet preparation. Pellets (40 mm) were pressed using a calibrated Herzog semi-automatic pellet press at 25 kN.

Prior to analysis, concealed certified reference materials and secondary reference materials were inserted into the sample batches. XRF analysis of the A samples was undertaken at the BGS; ICP analysis of A and S samples at SGS Laboratories, Toronto; and fire-assay of S samples at SGS Laboratories, Toronto.

For the Tellus samples, Energy Dispersive Polarised X-Ray Fluorescence (ED(P)- XRF) spectrometers were used to analyse those elements for which the WD-XRF spectrometers were insufficiently sensitive. Certified Reference Material (CRM) standards were used to calibrate the instruments. The PANalytical software was used for spectral deconvolution and to fit calibration curves, applying matrix correction by internal ratio Compton correction method. The calibrations were validated by analysis of a wide range of RMs. The detectors were calibrated weekly. All backgrounds and peaks were corrected for instrument drift using two external ratio monitors, when required. Quality control was maintained by regular analysis of two glass monitor samples containing 47 elements at nominally 30 mg/ kg and 300 mg/kg. Results were presented as run charts for statistical analysis using statistical process control software (SPC).

The lower limits of detection are theoretical values for the concentration equivalent to three standard deviations (99.7 % confidence interval) above the background count rate for the analyte in an iron-rich alumino-silicate matrix. For silicate matrices the practical detection limits for most elements approach the theoretical values due to high instrumental stability. LLDs were calculated from a matrix blank and the ‘synthetic’ Pro-Trace standards.

Individual results are not reliable below the quoted lower limits, but reliable estimates of the average or typical values over an area may be obtained at lower levels of concentration; meaningful distribution patterns may thus be recognised for some elements at levels lower than the LLD.

The data are described in Young, Mike; Donald, Alex, eds. 2013 A guide to the Tellus data. Belfast, UK, Geological Survey of Northern Ireland, 233pp. available for free download from: http://nora.nerc.ac.uk/509171/

The Tellus survey was funded by the Department of Enterprise, Trade and Investment (DETI), now the Department for the Economy (DfE) in Northern Ireland and the INTERREG IVA programme of the European Union (EU) Regional Development Fund.