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.

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS).

The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided.

Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed.

Four files are available for download:

• Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level.
• Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole
• Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided.
• Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data.

An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015.

Nationality data can be found here: https://data.london.gov.uk/dataset/nationality

Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

European countries are experiencing population decline and the tacit assumption in most analyses is that the decline may have detrimental welfare effects. In this paper we use a survey among the population in the Netherlands to discover whether population decline is always met with fear. A number of results stand out: population size preferences differ by geographic proximity: at a global level the majority of respondents favors a (global) population decline, but closer to home one supports a stationary population. Population decline is clearly not always met with fear: 31 percent would like the population to decline at the national level and they generally perceive decline to be accompanied by immaterial welfare gains (improvement environment) as well as material welfare losses (tax increases, economic stagnation). In addition to these driving forces it appears that the attitude towards immigrants is a very strong determinant at all geographical levels: immigrants seem to be a stronger fear factor than population decline. The data was collected from a Dutch household panel.

Population density by NUTS 3 region

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding 'migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

Abstract

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.

Longitudinal data is limited to income information and a limited set of critical qualitative, non-monetary variables of deprivation, aimed at identifying the incidence and dynamic processes of persistence of poverty and social exclusion among subgroups in the population. The longitudinal component is also more limited in sample size compared to the primary, cross-sectional component. Furthermore, for any given set of individuals, microlevel changes are followed up only for a limited duration, such as a period of four years. For both the cross-sectional and longitudinal components, all household and personal data are linkable. Furthermore, modules providing updated information in the field of social exclusion is included starting from 2005.

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. Labour, education and health observations only apply to persons 16 and older. 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 4th release of 2008 Longitudinal Dataset, as published by Eurostat in March 2012.

Geographic coverage

The survey covers following countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Greece, Spain, France, Ireland, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Iceland, Norway.

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 cross-sectional 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 Documentation.

Mode of data collection

Mixed

This table contains figures about the population of the Netherlands on 1 January. The data can be broken down by gender, age, country of origin, country of birth and region. Figures for specific countries of origin are published if 20,000 or more people with this country of origin live in the Netherlands at any given time. The regional totals presented are aggregates of municipalities. In case of border changes that cross several regional borders, the classification of the municipalities is grouped according to the most recent situation. Statistics Netherlands is switching to a new classification of the population by origin. From now on, where someone was born is more decisive than where someone's parents were born. The word migration background is no longer used. The main division western/non-western is replaced by a division based on continents and common immigration countries. This classification is gradually being introduced in tables and publications with population by origin. Data available from: 2022 Status of the figures: All figures included in the table are final. Changes as of June 16, 2023: The final figures as of January 1, 2023 have been added. Changes as of January 6, 2023: None, this is a new table. This table is the successor of 'Population; migration background, generation, lft, region, Jan 1; 2010-2022'. See section 3. The following changes have been made compared to the discontinued table: - The 'Migration background' tab has been replaced by 'Country of origin'; - The countries of origin Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey are assigned to continent Asia (was Europe); - The tab 'Generation' has been replaced by 'Country of birth'; - The five-year age groups now extend up to and including '100 years or older'. When will new numbers come out? For the time being, only data from 2022 is available in the table. The periods 2010 to 2021 will be added to the table at a later date. In the 3rd quarter of 2024, the figures from 1 January 2024 will be included in this publication.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

Harmonized data file as the basis for comparative analysis of quality of life in the Candidate Countries and the European Union member states, based on seven different data sets, one Eurobarometer survey covering 13 Candidate Countries with an identical set of variables conducted in April 2002, the other six Standard Eurobarometer of different subjects and fielded in different years, each with another set of questions identical with the CC Eurobarometer. Selected aggregate indicators of quality of life ... describing the social situation in the EU15 and Candidate Countries. The countries are tentatively grouped according to affinities following a families of nations logic. The indicators were drawn from various sources, mainly provided by supranational organisations. They are grouped into six categories and recorded in the technical report (page 12 ff.): (1) economy and employment; (2) health; (3) population and family; (4) inequality and social problems; (5) modernisation; (6) political system. Most indicators refer to the year 2000. Deviations from this rule are explained in the list of indicators, together with definitions, coding, and sources. The indicators are added to the harmonized EB data file for all 28 countries in order to provide an opportunity for multi-level analysis. Selected comprehensive indicators and relevant indices have been defined and constructed for quality of life and subjective well-being as well as for poverty and deprivation measures. The CC-Eurobarometer contains several questions on the perceived income situation of a household and on the availability or lack of certain consumer goods. It also provides information on the perception of social integration and general acceptance. (Source: Alber, Jens; Böhnke, Petra; Delhey, Jan; Fliegner, Florian; Gauckler, Britta; Habich, Roland; Keck, Wolfgang; Kohler, Ulrich; Nauenburg, Ricarda; Schiller, Sabine: Quality of Life in the European Union and the Candidate Countries. Technical Report. Results of data inspection, establishing a harmonized data file, recoding procedure and preparation of analysis. Hand-out for the first researchers’ meeting, Brussels, 4-5 March 2003.). Multi-stage, random (probability) sampling. The sampling is based on a random selection of sampling points after stratification by the distribution of the national, resident population in terms of metropolitan, urban and rural areas, i.e. proportional to the population size (for a total coverage of the country) und to the population density. These primary sampling units (PSU) are selected from each of the administrative regions in every country. In the second stage, a cluster of addresses is selected from each sampled PSU. Addresses are chosen systematically using standard random route procedures, beginning with an initial address selected at random. In each household, one respondent is selected by a random procedure, such as the first birthday method.

ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015.

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

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

Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

This dataset contains estimates of the urban population (in thousands of inhabitants) between the years 700 and 2000 in 2,262 European settlements. It is based on previous historical demographic sources that have been critically assessed and systematically complemented with new population estimates for additional time windows, deriving from either quantitative sources or proxies. Missing data are covered by city-specific imputations. It contains European cities with more than 100,000 inhabitants. Furthermore medieval first and second nature geographical data for all cities have been added, as well as their historical names.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

Population growth in The Netherlands by birth, death and migration by sex and region.

In addition to national data, information is presented by group of provinces, province, COROP region and municipality.

The regional totals shown concern cumulated municipal data. Where changes of municipal boundaries transect regional boundaries, the municipal classifications concerns the most recent situation. The municipality of Vianen, for example, was annexed by the province of Utrecht on 1 January 2002, and is classified under the province of Utrecht in the Table.

Data available from: 1942

Status of the figures: All data recorded in this publication are final data. Up to 1977 data may differ from other published data on StatLine. This is due to differences between the data files used by Statistics Netherlands and the official data as published in 'Loop van de bevolking per gemeente'.

Changes as of 9 July 2025: Final figures of 2024 have been added.

When will new figures be published? In the 3rd quarter of 2026 figures of 2025 will be added in this table.

BackgroundSocioeconomic inequalities in alcohol-related mortality have been documented in several European countries, but it is unknown whether the magnitude of these inequalities differs between countries and whether these inequalities increase or decrease over time.Methods and FindingsWe collected and harmonized data on mortality from four alcohol-related causes (alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy; alcoholic liver cirrhosis; and accidental poisoning by alcohol) by age, sex, education level, and occupational class in 20 European populations from 17 different countries, both for a recent period and for previous points in time, using data from mortality registers. Mortality was age-standardized using the European Standard Population, and measures for both relative and absolute inequality between low and high socioeconomic groups (as measured by educational level and occupational class) were calculated.Rates of alcohol-related mortality are higher in lower educational and occupational groups in all countries. Both relative and absolute inequalities are largest in Eastern Europe, and Finland and Denmark also have very large absolute inequalities in alcohol-related mortality. For example, for educational inequality among Finnish men, the relative index of inequality is 3.6 (95% CI 3.3–4.0) and the slope index of inequality is 112.5 (95% CI 106.2–118.8) deaths per 100,000 person-years. Over time, the relative inequality in alcohol-related mortality has increased in many countries, but the main change is a strong rise of absolute inequality in several countries in Eastern Europe (Hungary, Lithuania, Estonia) and Northern Europe (Finland, Denmark) because of a rapid rise in alcohol-related mortality in lower socioeconomic groups. In some of these countries, alcohol-related causes now account for 10% or more of the socioeconomic inequality in total mortality.Because our study relies on routinely collected underlying causes of death, it is likely that our results underestimate the true extent of the problem.ConclusionsAlcohol-related conditions play an important role in generating inequalities in total mortality in many European countries. Countering increases in alcohol-related mortality in lower socioeconomic groups is essential for reducing inequalities in mortality. Studies of why such increases have not occurred in countries like France, Switzerland, Spain, and Italy can help in developing evidence-based policies in other European countries.

This table contains figures on the population of the Netherlands on 1 January by gender, age, country of birth and region. The regions included in the table are parts of the country, provinces, COROP areas and municipalities. The regional totals presented are aggregates of municipalities. In case of border changes that cross several regional borders, the classification of the municipalities is grouped according to the most recent situation. For example: for the municipality of Vianen, which was transferred from the province of South Holland to the province of Utrecht on 01-01-2002, it is grouped in the table in the province of Utrecht. Data available from: 1996 Status of the figures: All figures included in the table are final. Changes as of June 3, 2022: Final figures as of January 1, 2021 and January 1, 2022 have been added. When will new numbers come out? In the fourth quarter of 2024, final figures as of January 1, 2023 and January 1, 2024 will be included in this publication.

This table contains data on the inhabitants of the Netherlands by sex, age (five-year groups) and a selection of nationalities per region, 1 January. The regions included in the table are parts of the country, provinces, COROP areas and municipalities. The regional totals presented are aggregates of municipalities. In case of border changes that cross several regional borders, the classification of the municipalities is grouped according to the most recent situation. For example: The municipality of Vianen transferred from the province of South Holland to the province of Utrecht on 01-01-2002 and is grouped in the province of Utrecht in this table. Data available from: 1995 Status of the figures: All figures included in the table are final. Changes as of June 3, 2022: The figures as of January 1, 2022 have been added. Changes as of May 2, 2022: Due to the use of a new source file for 2020, changes have occurred in ten nationalities. This also has consequences for changes in some parts of the world, the European Union, the GIPS and the CEE countries in the EU, total Dutch and total non-Dutch nationality. When will new numbers come out? In the second quarter of 2023, the figures as of 1 January 2023 will be included in this publication.

Abstract

In 2009, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway and Switzerland. 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. Labour, education and health observations only apply to persons 16 and older. 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.

The 7th version of the 2009 Cross-Sectional User Database (UDB) as released in July 2015 is documented here.

Geographic coverage

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

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