This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Dataset Overview

This dataset provides comprehensive statistics on migration in the United Kingdom from 1901 to 2023. It includes data on immigration, emigration, net migration, and detailed breakdowns by nationality, reason for migration, visa categories, and regional distributions. The data is sourced from the UK Parliament’s Commons Library briefing paper titled “Migration Statistics”, which aims to explain the concepts and methods used in measuring migration and offers a range of data on migration in the UK and European Union countries.

Dataset Folder: CBP06077-data.xlsx

2.2 (1) - Long-term international migration estimates in the UK

2.2 (2) - Estimated average annual net migration in the UK, 1901-2021

2.5 - Long-term international migration estimates in the UK, by nationality

2.6 (1) - Immigration by main reason for migration

2.6 (2) - Entry clearance visas granted by category, excluding tourist visas

2.6 (3) - Work visas granted by current category and prior equivalent

4.1 - Immigration and net migration of foreign nationals in EU countries and the UK, 2021

4.2 - Foreign-national and foreign-born populations of EU countries, 2021

5.1 - Estimated number of EU nationals living in the UK by nationality, 2021

5.2 - EU nationals by region, United Kingdom, 2021

5.4 (1) - Estimated number of British nationals living in EU countries, 2017

5.4 (2) - UN estimates of British citizens living in other EU countries, 2020

Cover Note - Additional information about the dataset

Dataset Contents

The dataset comprises multiple Excel files, each corresponding to specific tables and figures from the original report. Below is a detailed description of each file:

1. Long-term International Migration Estimates in the UK

•  Filename: long_term_international_migration_estimates_uk.xlsx
•  Description: Annual estimates of immigration, emigration, and net migration in the UK from 1991 to 2012.
•  Columns:
•  Year ending
•  Immigration
•  Emigration
•  Net migration

2. Estimated Average Annual Net Migration in the UK (1901-2021)

•  Filename: estimated_average_annual_net_migration_1901_2021.xlsx
•  Description: Decadal average net migration estimates based on census data from 1901 to 2012.
•  Columns:
•  Decade
•  Censuses ending
•  Average annual net migration

3. Long-term International Migration Estimates by Nationality

•  Filename: long_term_migration_by_nationality.xlsx
•  Description: Immigration, emigration, and net migration figures broken down by British, EU, and Non-EU nationals from 1991 to 2012.
•  Columns:
•  Year ending
•  Immigration: British, EU, Non-EU
•  Emigration: British, EU, Non-EU
•  Net migration: British, EU, Non-EU

4. Immigration by Main Reason for Migration

•  Filename: immigration_by_reason.xlsx
•  Description: Immigration figures categorized by main reasons such as work, accompanying/joining family, study, other, and none stated, from 1991 to 2012.
•  Columns:
•  Year ending
•  Work related
•  Accompany/Join
•  Study
•  Other
•  None Stated

5. Entry Clearance Visas Granted by Category (Excluding Tourist Visas)

•  Filename: entry_clearance_visas_granted.xlsx
•  Description: Data on entry clearance visas granted in work, study, family, and other categories from 2006 to 2024.
•  Columns:
•  Year
•  Work: Main applicants, Including dependants
•  Study: Main applicants, Including dependants
•  Family: All
•  Other: All

6. Work Visas Granted by Category

•  Filename: work_visas_granted_by_category.xlsx
•  Description: Details of work visas granted, categorized into Worker (T2), Temporary Worker (T5), Investor/Business Development/Talent (T1), and others from 2010 to 2024.
•  Columns:
•  Year
•  Worker (T2)
•  Temporary Worker (T5)
•  Investor, Business Development and Talent (T1)
•  Other
•  Total

7. Immigration and Net Migration of Foreign Nationals in EU Countries (2021)

•  Filename: immigration_net_migration_eu_2021.xlsx
•  Description: Immigration and net migration figures of foreign nationals in EU countries and the UK for the year 2021.
•  Columns:
•  Country
•  Immigration
•  Net migration

8. Foreign-national and Foreign-born Populations of EU Countries (2021)

•  Filename: foreign_population_eu_2021.xlsx
•  Description: Number and percentage of foreign-national and foreign-born populations in EU countries as of 2021.
•  Columns:
•  Country
•  FOREIGN NATIONAL: Number, As % of population
•  FOREIGN BORN: Number, As % of population
•  Total Population

9. Estimated Number of EU Nationals Living in the UK by Nationality (2021)

•  Filename: eu_nationals_in_uk_2021.xlsx
•  Description: Estimates of EU nationals residing in the UK, broken down by country of nationality for 2021.
•  Columns:
•  Country of nationality
•  Stock

10. EU Nationals by Region in the UK (2021)

•  Filename: eu_nationals_by_region_uk_2021.xlsx
•  Descri...

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

StatBank dataset: FOLK1C Title: Population in the first quarter by region, sex, age (5-year intervals), ancestry and country of origin Period type: quarter Period format (time in data): yyyyKq The oldest period: 2008K1 The most recent period: 2024K4

Abstract

In 2013, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

This is the 1st version of the 2013 Cross-Sectional User Database as released in July 2015.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Serbia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United Kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

Country Information Dataset

Overview

Welcome to the Country Information Dataset, meticulously curated by Aadarsh Vani. This dataset serves as an extensive resource for anyone interested in exploring the rich tapestry of countries around the globe, providing detailed information on various aspects of each nation.

Dataset Description

This dataset contains valuable insights into countries worldwide, featuring the following attributes:

• country_name: The name of the country.
• capital_city: The capital city of the country.
• currency: The official currency used.
• official_lang: The main languages spoken.
• population: The most recent population estimate.
• area: The total land area (in square kilometers).
• continent: The continent to which the country belongs (e.g., Asia, Europe).
• largest_city: The largest city by population.
• independence_year: The year the country gained independence.
• landmarks: Notable landmarks and attractions.
• national_animals: The officially recognized national animals.
• national_bird: The country's national bird.
• govt_type: The type of government (e.g., republic, monarchy).
• dish: A popular traditional dish.
• Major Religions: The predominant religions practiced.
• leader: The current political leader or head of state.
• driving_side: The side of the road on which vehicles drive.
• national_sport: The recognized national sport.
• major_fest: Important festivals celebrated in the country.

Purpose

The aim of this dataset is to provide a comprehensive and reliable resource for researchers, data scientists, and cultural enthusiasts. It can facilitate analysis and visualizations that reveal global patterns in demographics, cultures, and economies.

Applications

• Comparative studies of countries based on population, area, and GDP.
• Analysis of cultural diversity through languages, foods, and religions.
• Visualization of geographical data to highlight landmarks and major cities.
• Research into the historical context of independence and governance.

Data Format

• The dataset is available in CSV format, ensuring ease of access and compatibility with data analysis platforms.

Acknowledgment

Created by Aadarsh Vani, this dataset is a labor of love aimed at enriching the understanding of our world's countries. I encourage users to share their insights, visualizations, and analyses arising from this dataset. Together, we can foster a deeper appreciation of global diversity!

Thank you for exploring this dataset, and I hope it inspires your work in studying the fascinating intricacies of countries worldwide.

Note: This data set will be updated frequently to keep it updated by adding new columns and updating the updated values. Kindly use it for practice and projects only as it has missing values and may have unintentional wrong data in some cells.

The MEDIATIZED EU project aims to study how the media discourses are constructed to foster or hamper the European project and how they resonate among the public by focusing on the elite-media-public triangle. The research was conducted in seven target countries: Ireland, Belgium, Estonia, Spain, Portugal, Hungary and Georgia.

This dataset is part of the integration of the MEDIATIZED EU project research data into the EU’s Open Research Data Pilot. In accordance with the Data Management Plan, public opinion survey data were deemed suitable for being openly shared through ORDP to be accessible and of use to other academic researchers in Europe and worldwide. Quantitative data derived from surveys was deemed suitable, with the only concerns being the heterogeneous nature of some of the survey questions in each target country.

The aim of the population surveys was to investigate public opinion about the media and elites in their country and the EU and how they interpret elite and media discourses on Europeanisation and European integration. The merged database allows the project participants and other researchers to compare their national research results with phenomena in other participating countries.

This dataset contains a subset of integrated survey data including those survey questions where comparative data was available. The final deliverable contains this subsection of the survey data which has been weighted and cleaned, in .SAV and .XLS formats, and provides the requisite codebook for the dataset.

For more on the MEDIATIZED EU project, visit our website at mediatized.eu or view our CORDIS profile at: https://cordis.europa.eu/project/id/101004534

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004534. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

The deliverables will have restricted access at least until the end of ECFAS

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

* Malta was added to the dataset

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	1km	The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities
GHS-BUILT	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	1km	The ENACT is a population density for the European Union that take into account major daily and monthly population variations
JRC Open Power Plants Database (JRC-PPDB-OPEN)	-	Europe’s open power plant database
GHS functional urban areas (GHS-FUA R2019A)	1km	City and its commuting zone (area of influence of the city in terms of labour market flows)
GHS Urban Centre Database (GHS-UCDB R2019A)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
Open Street Map (OSM)	-	BF, Transportation Network, Utilities Network, Places of Interest
CEMS	-	Data from Rapid Mapping activations in Europe
GeoNames	-	Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.
Global Administrative Areas	-	Administrative areas of all countries, at all levels of sub-division
NUTS3 Population Age/Sex Group	-	Eurostat population by age ansd sex statistics interesected with the NUTS3 Units
FLOPROS		A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

Analysis of ‘E7042 - Population Aged One Year and Over Usually Resident and Present by Country of Previous Residence and Percentage who Lived Outside the State for One Year or More 2011 to 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/196ba74f-78f4-44b3-a7c9-4f914e1f68d3 on 11 January 2022.

--- Dataset description provided by original source is as follows ---

Population Aged One Year and Over Usually Resident and Present by Country of Previous Residence and Percentage who Lived Outside the State for One Year or More 2011 to 2016

--- Original source retains full ownership of the source dataset ---

The most important key figures about population, households, birth, mortality, changes of residence, marriages, marriage dissolutions and change of nationality of the Dutch population. Data available from: 1899 Status of the figures: All data in this publication are final data. Changes as of 9 april 2021: The figures for the period 1987 to 1994 with regard to 'Emigration including the balance of the administrative corrections' have been corrected. The correction is due to duplications present in some of our source files. The differences are minimal. The figures for 1997 with regard to Emigration, including the balance of the administrative corrections for persons with nationality 'European Union (excluding the Netherlands)', and persons with country of birth 'European Union (excluding the Netherlands)' have been corrected. The correction is due to a calculation error. The topics 'Live born children, relative' and 'Sex ratio' have switched places. Changes as of 24 March 2020: The table has been revised. The following changes have been made: Population on January 1: - The number of 'Women' in 2012 has been corrected. - The figures for 'Migration background Suriname' and 'Migration background (former) Netherlands Antilles' have been changed for 1971 up to and including 1994. The changes are the result of a method change in the past, which was not reflected in the table at the time. The figures now match all other sections of StatLine. Population development: 'Emigration' has been changed to 'Emigration including administrative corrections', 'Migration balance' has been changed to 'Migration balance including administrative corrections'. Figures on emigration, including the balance of administrative corrections, provide a better picture of actual emigration than figures on emigration excluding these corrections. Due to the change, the figures for 1977 up to and including 2016 have changed. Live born children: The 2015 figures for 'Live born children from mothers aged 25 to 29, relative' and 'Live born children from mothers aged 30 or older, relative' have been adjusted. Mortality: - The figures for 'Life expectancy at birth: men' and 'Life expectancy at birth: women' for 1950 up to and including 1962, 1972, 1982, 1991, 1999, 2009 and 2011 have been corrected. - The figures for 'Mortality <1 year after birth, relative' for 1994 and 2011 have been corrected. - The figure for 'Mortality <1 year after birth, relative' for 2011 has been corrected. - The figures for 'Deceased by cause of death' have been removed from the table. (For more information: 3. LINKS TO RELEVANT TABLES AND ARTICLES). Foreign migration by nationality: - Various topics related to 'Emigration including administrative corrections' have been added. - 'Total immigration' has been corrected for 1993 and 1996. - 'Immigration, European Union (excluding the Netherlands)' has been adjusted for 2004 and 2013. - 'Emigration excluding administrative corrections, Dutch' has been corrected for 1995 and 2012. - 'Emigration excluding administrative corrections, Total non-Dutch' has been corrected for 1995 and 2012. - 'Emigration excluding administrative corrections, European Union' has been adjusted for 2004, 2005 and 2013. Foreign migration by country of birth: - Various topics related to 'Emigration including administrative corrections' have been added. - 'Total immigration' has been corrected for 1993 and 1996. - 'Immigration, European Union (excluding the Netherlands)' has been adjusted for 1987 up to and including 1990 and for 2004. - 'Immigration, Suriname and the Netherlands Antilles' has been corrected for 2012. - 'Immigration, Netherlands Antilles' has been corrected for 2012. - 'Emigration excluding administrative corrections, European Union (excluding the Netherlands)' has been adjusted for 1989, 1999 and 2004. - 'Emigration excluding administrative corrections, Indonesia' has been corrected for 1994. - 'Emigration excluding administrative corrections, Suriname and the Netherlands Antilles' has been corrected for 1997. - 'Emigration excluding administrative corrections, Netherlands Antilles' has been corrected for 1997. - 'Emigration excluding administrative corrections, Specific emigration areas' has been corrected for 1995. Foreign migration by country of origin / destination: - 'Total immigration' has been corrected for 1996. - 'Immigration, European Union (excluding the Netherlands)' has been adjusted for 2004. - 'Immigration, Indonesia, Suriname and the Netherlands Antilles' has been corrected for 2007 and 2010 up to and including 2016. - 'Immigration, Suriname and the Netherlands Antilles' has been corrected for 2010 up to and including 2016. - 'Immigration, Indonesia' has been corrected for 2013. - 'Immigration, Netherlands Antilles' has been corrected for 2010 up to and including 2016. - 'Emigration excluding administrative corrections, European Union (excluding the Netherlands)' has been adjusted for 1998 and 2004. - 'Emigration excluding administrative corrections, Indonesia, Suriname and the Netherlands Antilles' has been corrected for 2010 up to and including 2016. - 'Emigration excluding administrative corrections, Suriname and Netherlands Antilles' has been corrected for 2010 up to and including 2016. - 'Emigration excluding administrative corrections, Netherlands Antilles' has been corrected for 2010 up to and including 2016. - 'Emigration excluding administrative corrections, Turkey' has been corrected for 2012. The corrections are the result of manual actions. The differences concern rounding differences and are minimal. The adjustments with regard to the European Union are generally the result of a changed calculation method. When will the new figures be published? The figures for the population development in 2019 and the population on 1 January 2020 will be published in the first quarter of 2021.

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.

Comprehensive, annually-updated population datasets at ZIP code and administrative levels for 247 countries, spanning from 1975 to 2030, including historical, current, and projected population figures, enriched with attributes like area size, multilingual support, UNLOCODEs, IATA codes, and time zones.

In the Netherlands it was customary for the traditional post-war censuses until 1971 to provide an overview of population concentrations with the corresponding population numbers. For this, the so-called local classification was used. Already at the 1947 Census, the administrative level of the municipalities was considered less suitable for sketching a picture of the distribution of the population across the country, because of the strong differences in the size and extent of the municipalities. Since 1947, the number of municipalities has more than halved. A national overview of the spatial distribution of the population is therefore important, even more than fifty years ago, all the more so because the last overview dates from three decades ago. On the occasion of the Dutch virtual census 2001, CBS picked up this thread again. The desire of the European Commission to obtain statistical information about the population of the so-called 'urban areas' via the European Census Program 2001 also played a role in this. As a result of this programme, Statistics Netherlands has compiled the statistical data for all population centres, and not just for those centers that are designated as 'urban areas' by the European Union. Data: reporting year 2001. Changes compared to the previous version. As of December 7, 2012, the naming of the population centers in Friesland has been supplemented with the Frisian naming. Status of the figures: final

This dataset provides values for GDP PER CAPITA PPP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As part of the project "Social Stratification in Eastern Europe after 1989," sample surveys were conducted in 1993 and 1994 in six countries: Bulgaria, the Czech Republic, Hungary, Poland, Russia, and Slovakia. Using a questionnaire common to all countries, national probability samples of approximately 5,000 members of the adult population were surveyed in five of the six countries in 1993; in Poland, due to the lack of local funds, the data collection was delayed until 1994 and the sample size was reduced to approximately 3,500. To permit analyses of special interest to urban geographers (the Dutch funding was provided by a study committee of the Dutch NSF consisting of sociologists and urban geographers), over-samples of the populations of Prague and Warsaw were surveyed, with the sample sizes sufficient to bring the sum of cases from the over-sample and the national sample in each country to approximately 1,500. (About 900 cases each are available for Budapest and Sofia, generated by the national sample design. Thus, a four city comparison of Eastern European capitals is feasible.) The design of the survey called for exactly comparable wording of questions, and variation in the response categories only where national variations in circumstances (e.g., different religious distributions) warranted it. Country teams were free to add local questions at the end of the questionnaire. To ensure such comparability, the questionnaire was translated into each local language and then back-translated into English; the back-translated versions were compared as a group by a multi-lingual team and discrepancies in wording corrected. Inevitably, despite our best intentions, minor variations crept into the questionnaire. These are identified at appropriate places in the Codebook. The local language questionnaires are shown in Appendix G (Vol. II). (Probability samples of about 1,000 members of the old elite and about 1,000 members of the new elite in each country except Slovakia were also surveyed, using a similar but not identical questionnaire. These surveys have a separate codebook, which may be found under the title "Social Stratification in Eastern Europe after 1989: Elite Survey".)

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Dataset title: Deaths from all causes in Western Europe by month, 1914-1918 Related publication: More, A. F. et al. (2020). The impact of a six-year climate anomaly on the ‘Spanish Flu’ Pandemic and WWI. GeoHealth, American Geophysical Union. Figures 2 and 3. Dataset source: Bunle, H. (1954). Le Mouvement naturel de la population dans le monde de 1906 à 1936. Paris, Institut national d’études démographiques, pp. 432-438. N.B. Please cite the original source if you use this dataset. N.B. Please note that Bunle did not publish mortality statistics for Belgium, Bulgaria, and several other countries for the period 1914-20 due to his inability to find reliable sources, as indicated in his footnotes and on p. 12. This dataset includes countries of western Europe with the most reliable data. Units: Thousands of deaths. Each monthly figure should be multiplied by 1000 to obtain the total deaths for a specific month. Each year is divided in 12 monthly entries, with decimals increasing by 0.083 (1/12) for each month.

The Comprehensive Food Consumption Database is a source of information on food consumption across the European Union (EU). It contains detailed data for a number of EU countries. The database plays a key role in the evaluation of the risks related to possible hazards in food in the EU and allows estimates of consumers’ exposure to such hazards, a fundamental step in EFSA’s risk assessment work. The database was also relevant for other fields of EFSA’s work, such as the assessment of nutrient intakes of the EU population. EFSA used its food classification system ‘FoodEx’ to categorise all foods and beverages included in the Comprehensive Database.

Summary statistics from the database enable quick screening for chronic and acute exposure to substances and organisms that may be found in the food chain. In the database, dietary surveys and food consumption data for each country are divided by category. These include: age, from infants to adults aged 75 years or older; food group (over 1,500) and type of consumption, covering both regular and high consumption thus allowing calculations to be tailored to each category of consumer.

The statistics on food consumption are reported in grams per day (g/day) and grams per day per kg of body weight (g/kg bw per day). The statistics for chronic food consumption are available for the total population (‘all subjects’) and for consumers of respective food categories. The statistics for acute consumption are available for all days and for the consuming days.

These food consumption statistics are stored and presented in the EFSA Data Warehouse.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.