This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 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 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 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).

As of 2023, the population density in London was by far the highest number of people per square km in the UK, at *****. Of the other regions and countries which constitute the United Kingdom, North West England was the next most densely populated area at *** people per square kilometer. Scotland, by contrast, is the most sparsely populated country or region in the United Kingdom, with only ** people per square kilometer. Countries, regions, and cities According to the official mid-year population estimate, the population of the United Kingdom was just almost **** million in 2022. Most of the population lived in England, where an estimated **** million people resided, followed by Scotland at **** million, Wales at **** million and finally Northern Ireland at just over *** million. Within England, the South East was the region with the highest population at almost **** million, followed by the London region at around *** million. In terms of urban areas, Greater London is the largest city in the United Kingdom, followed by Greater Manchester and Birmingham in the North West and West Midlands regions of England. London calling London's huge size in relation to other UK cities is also reflected by its economic performance. In 2021, London's GDP was approximately *** billion British pounds, almost a quarter of UK GDP overall. In terms of GDP per capita, Londoners had a GDP per head of ****** pounds, compared with an average of ****** for the country as a whole. Productivity, expressed as by output per hour worked, was also far higher in London than the rest of the country. In 2021, London was around **** percent more productive than the rest of the country, with South East England the only other region where productivity was higher than the national average.

THIS DATASET HAS BEEN WITHDRAWN and superseded by UK Gridded Population 2011 based on Census 2011 and Land Cover Map 2015 (https://catalogue.ceh.ac.uk/id/0995e94d-6d42-40c1-8ed4-5090d82471e1). This dataset contains gridded population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2007 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 2007 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.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsPopulation DensityDefinition: This dataset provides Census 2021 estimates that classify usual residents in England and Wales by population density (number of usual residents per square kilometre). The estimates are as at Census Day, 21 March 2021.

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.

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.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

This report was released in September 2010. However, recent demographic data is available on the datastore - you may find other datasets on the Datastore useful such as: GLA Population Projections, National Insurance Number Registrations of Overseas Nationals, Births by Birthplace of Mother, Births and Fertility Rates, Office for National Statistics (ONS) Population Estimates

FOCUSON**LONDON**2010:**POPULATION**AND**MIGRATION**

London is the United Kingdom’s only city region. Its population of 7.75 million is 12.5 per cent of the UK population living on just 0.6 per cent of the land area. London’s average population density is over 4,900 persons per square kilometre, this is ten times that of the second most densely populated region.

Between 2001 and 2009 London’s population grew by over 430 thousand, more than any other region, accounting for over 16 per cent of the UK increase.

This report discusses in detail the population of London including Population Age Structure, Fertility and Mortality, Internal Migration, International Migration, Population Turnover and Churn, and Demographic Projections.

Population and Migration report is the first release of the Focus on London 2010-12 series. Reports on themes such as Income, Poverty, Labour Market, Skills, Health, and Housing are also available.

REPORT:

Read the full report in PDF format.

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PRESENTATION:

To access an interactive presentation about population changes in London click the link to see it on Prezi.com

DATA:

To access a spreadsheet with all the data from the Population and Migration report click on the image below.

Report data

MAP:

To enter an interactive map showing a number of indicators discussed in the Population and Migration report click on the image below.

Interactive Maps

FACTS:

● Top five boroughs for babies born per 10,000 population in 2008-09:

1. Newham – 244.4
2. Barking and Dagenham – 209.3
3. Hackney – 205.7
4. Waltham Forest – 202.7
5. Greenwich – 196.2

-32. Havering – 116.8

-33. City of London – 47.0

● In 2009, Barnet overtook Croydon as the most populous London borough. Prior to this Croydon had been the largest since 1966

● Population per hectare of land used for Domestic building and gardens is highest in Tower Hamlets

● In 2008-09, natural change (births minus deaths) led to 78,000 more Londoners compared with only 8,000 due to migration. read more about this or click play on the chart below to reveal how regional components of populations change have altered over time.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows two condensed maps which use dots and proportional circles to illustrate the distribution of population of French and British origin, respectively, according to the 1951 census of Canada. Each map is accompanied by a pie chart which shows the British origin and French origin percentage population distribution by province and territory. For Canadian census purposes, a person's origin or cultural group is traced through the father to the paternal ancestor on first arrival to this continent. The term 'British' embraces all those of British Isles origin, that is, it includes those from the United Kingdom of Great Britain and Northern Ireland, the Isle of Man, the Channel Islands and the Republic of Ireland.

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at August 2022 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 184 MB).

Mid-year (30 June) estimates of the usual resident population for Lower layer Super Output Areas (LSOAs) in England and Wales by single year of age and sex.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows the distribution of population in what is now Canada circa 1851, 1871, 1901, 1921 and 1941. The five maps display the boundaries of the various colonies, provinces and territories for each date. Also shown on these five maps are the locations of principal cities and settlements. These places are shown on all of the maps for reference purposes even though they may not have been in existence in the earlier years. Each map is accompanied by a pie chart providing the percentage distribution of Canadian population by province and territory corresponding to the date the map is based on. It should be noted that the pie chart entitled Percentage Distribution of Total Population, 1851, refers to the whole of what was then British North America. The name Canada in this chart refers to the province of Canada which entered confederation in 1867 as Ontario and Quebec. The other pie charts, however, show only percentage distribution of population in what was Canada at the date indicated. Three additional graphs are included on this plate and show changes in the distribution of the population of Canada from 1867 to 1951, changes in the percentage distribution of the population of Canada by provinces and territories from 1867 to 1951 and elements in the growth of the population of Canada for each ten-year period from 1891 to 1951.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows two condensed maps which use dots and proportional circles to illustrate the distribution of population of French and British origin, respectively, according to the 1951 census of Canada. Each map is accompanied by a pie chart which shows the British origin and French origin percentage population distribution by province and territory. For Canadian census purposes, a person's origin or cultural group is traced through the father to the paternal ancestor on first arrival to this continent. The term 'British' embraces all those of British Isles origin, that is, it includes those from the United Kingdom of Great Britain and Northern Ireland, the Isle of Man, the Channel Islands and the Republic of Ireland.

This boundary dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria.

This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing.

These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities.

This boundary dataset was generated to define the extent of the study area, which comprises the border between Syria and Turkey, Syria and Iraq, the River Tigris and the River Euphrates. All related data collected was confined within this boundary dataset with the exception of the archaeological dataset. Archaeological sites were identified and mapped along both banks of the River Euphrates. Also, the town of Dayr az-Zawr, where the 1963 precipitation and temperature monthly values were collected for one of the datasets, falls outside the Jazira Region.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Niger. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Liberia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

These data include gridded estimates of population sizes at approximately 100 m resolution with national coverage across South Sudan. This includes estimates of total population sizes and population counts in 40 different age-sex groups. It also includes a breakdown of the total population sizes into internally displaced persons (IDPs) and non-IDPs. These results were produced using publicly available census projections from the South Sudan National Bureau of Statistics and displacement data from the International Organisation for Migration (IOM) and the United Nations Refugee Agency (UNHCR), as well as building footprints from Maxar/Ecopia that were derived from recent satellite imagery. Note that this dataset is most likely to represent South Sudan's population distribution as of September 2020 given the age of the input data.1. SSD_population_v2_0_gridded.zipThis zip file contains three rasters in geotiff format:SSD_population_v2_0_gridded_population.tif This geotiff raster contains estimates of total population size for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan. NA values represent grid cells where no building footprints were present. Zero values represent grid cells that contain building footprints but are estimated to contain no people due to displacement of people away from those grid cells. These population estimates include decimals (e.g. 10.3 people). This provides more accurate population totals when grid cells are summed. A population estimate of 0.5 people in each of two neighboring grid cells would indicate an expectation that one person lives somewhere within those two grid cells. SSD_population_v2_0_gridded_nonidps.tif This geotiff raster contains estimates of non-internally displaced persons (non-IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have not been displaced from another area. This raster plus the SSD_population_v2_0_gridded_idps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster. SSD_population_v2_0_gridded_idps.tif This geotiff raster contains estimates of internally displaced persons (IDPs) for each approximately 100 m grid cell (0.0008333 decimal degrees grid) across South Sudan, i.e. the number of people who have been displaced from another area. This raster plus the SSD_population_v2_0_gridded_nonidps.tif raster equal the values given in the SSD_population_v2_0_gridded_population.tif raster.2. SSD_population_v2_0_agesex.zip This zip file contains 40 rasters in geotiff format:Each raster provides gridded population estimates for an age-sex group. These were derived from the SSD_population_v2_0_gridded_population.tif raster. Note that, in this dataset, we do not provide age-sex group estimates for non-IDPs and IDPs separately. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. These are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1 year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”. We provide an addition four rasters that represent demographic groups often targeted by programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced by the WorldPop Research Group at the University of Southampton. Data Citation: WorldPop (School of Geography and Environmental Science, University of Southampton). 2021. South Sudan 2020 gridded population estimates from census projections adjusted for displacement, version 2.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00709 CREDITS: The modelling work was led by Claire Dooley with support from Chris Jochem and oversight by WorldPop director Andy Tatem and GRID3 lead Attila Lazar. The support of the whole WorldPop group is acknowledged, as well as the our GRID3 partners (UNFPA, Columbia University and Flowminder). This work was supported with funding from the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Department for International Development (DFID).This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation. The primary intended use of these data was aiding the BMGF field teams.The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go to here.

Linking variation in quantitative traits to variation in the genome is an important, but challenging task in the study of life-history evolution. Linkage maps provide a valuable tool for the unravelling of such trait-gene associations. Moreover, they give insight into recombination landscapes and between- species karyotype evolution. Here we used genotype data, generated from a 10k SNP-chip, of over 2000 individuals to produce high-density linkage maps of the great tit (Parus major), a passerine bird, which serves as a model species for ecological and evolutionary questions. We created independent maps from two distinct populations: a captive F2-cross from The Netherlands (NL) and a wild population from the United Kingdom (UK). The two maps contained 6554 SNPs in 32 linkage groups, spanning 2010 cM and 1917 cM for the NL and UK populations respectively, and were similar in size and marker order. Subtle levels of heterochiasmy within and between chromosomes were remarkably consistent between the populations, suggesting that local departures from sex-equal recombination rates have evolved. This key and surprising result would have been impossible to detect if only one population was mapped. A comparison with zebra finch Taeniopygia guttata, chicken Gallus gallus and the green anole lizard Anolis carolinensis genomes provided further insight into the evolution of avian karyotypes.