In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

Paris was Western Europe's largest city in 1650, with an estimated 400 thousand inhabitants, which is almost double it's population 150 years previously. In second place is London, with 350 thousand inhabitants, however it has grown by a substantially higher rate than Paris during this time, now seven times larger than it was in the year 1500. Naples remains in the top three largest cities, growing from 125 to 300 thousand inhabitants during this time. In the previous list, the Italian cities of Milan and Venice were the only other cities with more than one hundred thousand inhabitants, however in this list they have been joined by the trading centers of Lisbon and Amsterdam, the capital cities of the emerging Portuguese and Dutch maritime empires.

It is estimated that the largest cities in Western Europe in 1330 were Paris and Granada. At this time, Paris was the seat of power in northern France, while Granada had become the largest multicultural city in southern Spain, controlled by the Muslim, Nasrid Kingdom during Spain's Reconquista period. The next three largest cities were Venice, Genoa and Milan, all in northern Italy, renowned as important trading cities during the middle ages. In October 1347, the first wave of the Black Death had arrived in Sicily and then began spreading throughout Europe, decimating the population.

Population in the largest city (% of urban population) in European Union was reported at 15.91 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. European Union - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The average for 2023 based on 27 countries was 74.4 percent. The highest value was in Belgium: 98.19 percent and the lowest value was in Slovakia: 54.03 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

In the year 1500, the share of Western Europe's population living in urban areas was just six percent, but this rose to 31 percent by the end of the 19th century. Despite this drastic change, development was quite slow between 1500 and 1800, and it was not until the industrial revolution when there was a spike in urbanization. As Britain was the first region to undergo the industrial revolution, from around the 1760s until the 1840s, these areas were the most urbanized in Europe by 1890. The Low Countries Prior to the 19th century, Belgium and the Netherlands had been the most urbanized regions due to the legacy of their proto-industrial areas in the medieval period, and then the growth of their port cities during the Netherlands' empirical expansion (Belgium was a part of the Netherlands until the 1830s). Belgium was also quick to industrialize in the 1800s, and saw faster development than its larger, more economically powerful neighbors, France and Germany. Least-urban areas Ireland was the only Western European region with virtually no urbanization in the 16th and 17th century, but the industrial growth of Belfast and Dublin (then major port cities of the British Empire) saw this change by the late-1800s. The region of Scandinavia was the least-urbanized area in Western Europe by 1890, but it saw rapid economic growth in Europe during the first half of the following century.

By 1800, London had grown to be the largest city in Western Europe with just under one million inhabitants. Paris was now the second largest city, with over half a million people, and Naples was the third largest city with 450 thousand people. The only other cities with over two hundred thousand inhabitants at this time were Vienna, Amsterdam and Dublin. Another noticeable development is the inclusion of many more northern cities from a wider variety of countries. The dominance of cities from France and Mediterranean countries was no longer the case, and the dispersal of European populations in 1800 was much closer to how it is today, more than two centuries later.

Link to this report's codebookWe are pleased to launch the 2019 SDG Index and Dashboards Report for European Cities (prototype version). This is the first report comparing the performance of capital cities and a selection of large metropolitan areas in the European-Union (EU) and European Free Trade Association (EFTA) on the 17 Sustainable Development Goals (SDGs). In total, results for 45 European cities are presented in this first prototype version. The report was prepared by a team of researchers from the Sustainable Development Solutions Network (SDSN) and the Brabant Center for Sustainable Development (Telos, Tilburg University).It builds on SDSN’s experience in designing SDG indicators for nations and metropolitan areas. The report also builds on TELOS’ previous work on “Sustainability Monitoring of European Cities” (2014) prepared in collaboration with the European Commission’s Directorate-General for Environment (Zoeteman et al. 2014) which led to the development of an interactive platform on request of the Dutch Ministry of Interior and Kingdom Relations (Zoeteman et al. 2016)1.This report comes at a key opportunity for Europe to increase its focus on the SDGs, with the election of the new European Parliament in May, the new Presidency of the Council of the EU moving to Finland in July, and the arrival of a new European Commission by the end of the year. The European Union can and should strengthen its policy measures to achieve all of the SDGs. In that context, the European Commission’s January 2019 Reflection Paper “Towards a sustainable Europe by 2030” highlights various scenarios to support the SDGs over the next decade. The report by the European Commission highlights the opportunities to address the SDGs as part of the next EU Urban Agenda.Achieving the SDGs will require, at the local level, deep transformations in transportation, energy and urban planning and new approaches to address poverty and inequalities in access to key public services including health and education. The SDSN estimates that about two-thirds (65%) of the 169 SDG targets underlying the 17 SDGs can only be reached with the proper engagement of, and coordination with, local and regional governments (SDSN 2015).Similarly, UN-Habitat estimates that around one-third of all SDGs indicators have a local or urban component2. The Urban Agenda for the European Union launched in May 2016 (Pact of Amsterdam), recognizes the crucial role of cities in achieving the SDGs. Over two-thirds of EU citizens live in urban areas while about 85% of the EU’s GDP is generated in cities (European Commission 2019). The urban population in Europe is projected to rise to just over 80% by 2050 (European Commission 2016).This 2019 SDG Index and Dashboards for European Cities (prototype version) finds that no European capital city or large metropolitan area has of yet fully achieved the SDGs. Nordic European cities – Oslo, Stockholm, Helsinki and Copenhagen – are closest to the SDG targets but still face challenges in achieving one or several of the SDGs. Overall, the cities in Europe perform best on SDG 3 (Health and Well-Being), SDG 6 (Clean Water and Sanitation), SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation and Infrastructure). By contrast, performance is lowest on SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action) and SDG 15 (Life on Land).As always, our analysis is constrained by the availability, quality and comparability of data. These data constraints are even greater at the subnational level. Despite the ground breaking work conducted by the European Commission – notably via Eurostat and the Joint Research Centre – to define territorial levels and metropolitan areas and to standardize subnational data and indicators, major gaps remain to monitor all of the SDGs. A table summarizing some of these major gaps is included in this report.The need to expand and strengthen SDG monitoring in regions and municipalities across Europe in the coming years was raised extensively in the consultation made by SDSN as part of its 2019 study on “Exposing EU policy gaps to address the Sustainable Development Goals” prepared in collaboration with the European Economic and Social Committee (Lafortune and Schmidt-Traub 2019) . This was also one of the recom- mendations made by ESAC during the consultation phase for the “2017 Sustainable development in the European Union — Monitoring report on progress towards the SDGs in an EU context” (European Statistical Advisory Committee (ESAC) 2017).We hope this first 2019 SDG Index and Dashboards Report for European Cities (prototype version) will help to identify the major SDG priorities in urban Europe. All data and analyses included in this report are available on SDSN’s and TELOS’ data portals (www.sdgindex.org and www.telos.nl). Individual city profiles are accessible online. We very much welcome comments and suggestions for filling gaps in the data used for this index and for improving the analysis and presentation of the results. Please contact us at info@sdgindex.org or telos@uvt.nl.Jeffrey Sachs,Director SDSNGeert Duijsters,Dean Tilburg School of economics, Tilburg University - Telos

This statistic shows the largest urban settlements in the Netherlands in 2021. In 2021, around 1.13 million people lived in Amsterdam, making it the largest city in the Netherlands. Population of the Netherlands With the global financial crisis in 2008 as well as the Euro zone crisis, many countries in Europe suffered a great economic impact. In spite of the crisis, the Netherlands maintained a stable economy over the past decade. The country's unemployment rate, for example, has been kept at a relatively low level in comparison to other countries in Europe also affected by the economic crisis. In 2014, Spain had an unemployment rate of more than 25 percent. The Netherlands' population has also seen increases in growth in comparison to previous years, with the figures slowly decreasing since 2011. As a result of the increase in population, the degree of urbanization - which is the share of the population living in urban areas - has increased, while the size of the labor force in the Netherlands has been relatively stable over the past decade. The population density of inhabitants per square kilometer in the Netherlands has also increased. Large cities in the Netherlands have experienced the impact of the population density growth and increase in the size of the labor force first hand. Three cities in the Netherlands have over half a million residents (as can be seen above). Additionally, more and more visitors are coming to the kingdom: The number of tourists in the Netherlands has increased significantly since 2001, a change which has also impacted the country's metropolises. Due to its location and affordable accommodation prices, the country’s tourism industry is developing and the largest cities in the Netherlands are taking advantage of it.

A dataset of grid level (500m by 500m) accessibility indicators measured in peak traffic and free flow conditions for all cities (represented by the Functional Urban Area) with more than 250 thousand people in EU27, the UK, Switzerland and Norway.

In 1500, the largest city was Paris, with an estimated 225 thousand inhabitants, almost double the population of the second-largest city, Naples. As in 1330, Venice and Milan remain the third and fourth largest cities in Western Europe, however Genoa's population almost halved from 1330 until 1500, as it was struck heavily by the bubonic plague in the mid-1300s. In lists prior to this, the largest cities were generally in Spain and Italy, however, as time progressed, the largest populations could be found more often in Italy and France. The year 1500 is around the beginning of what we now consider modern history, a time that saw the birth of many European empires and inter-continental globalization.

In this paper, the authors construct a unique data set of Internet offer prices for flats in 48 large European cities from 24 countries. The data are collected between January and May 2012 from 33 websites, where the advertisements of flats for sale are placed. Using the resulting sample of 750,000 announcements the authors compute the average city-specific house prices. Based on this information they investigate the determinants of the apartment prices. Four factors are found to be relevant for the flats’ price level: income per capita, population density, unemployment rate, and income inequality. The results are robust both to excluding variables and to applying two alternative estimation techniques: OLS and quantile regression. Based on their estimation results the authors are able to identify the cities, where the prices are overvalued. This is a useful indication of a build-up of house price bubbles.

This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland". More details of the data are available in the upcoming data description article (manuscript can be found in this repository).

Abstract: Access to good quality green spaces in urban regions is vital for the well-being of citizens. In this article, we present data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.

Related research article:

Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. Applied Geography, 157, 102973. https://doi.org/10.1016/j.apgeog.2023.102973

Green areas in agglomerations are geographically defined by Statistics Sweden as part of the production of official statistics on green spaces and green areas in urban areas. The data made available here is thus primarily intended for statistical production.

A green area is defined by Statistics Sweden as an area of contiguous green areas of at least 0.5 hectares and is publicly available. Pasture is included in green areas, but not arable land. Green areas are defined geographically to within urban areas. The minimum accounting unit is 0.5 hectares. The definition therefore does not take into account whether the areas are designated as green areas in the municipalities’ overview or detailed plans.

The demarcation of green spaces is based on satellite data that is co-processed with geographic and register data from Lantmäteriet.

Data are available for two different reference times, 2010 and 2015. The 2010 data cover only green areas in the 37 largest agglomerations according to the 2010 urban demarcation. The 2015 data includes green areas in all agglomerations according to the 2015 urban demarcation.

This database provides construction of Large Urban Regions (LUR) in the world. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rozenblat, 2020 or Rogov & Rozenblat, 2020 for Russia). In doing so we use four principal urban concepts (Pumain et al., 1992): local administrative units (Municipality or localities: MUNI), morphological urban area (MUA), functional urban area (FUA) and conurbation that we call Large Urban Region (LUR). The LURs are the spatial extensions of influence of one or several FUAs or MUAs. MUAs and FUAs are defined by various national or international sources. We implemented LURs using criteria such as the population distribution among one or several MUAs or FUAs, road networks, access to an airport, distance from a core, presence of multinational firms. FUAs and MUAs perimeters, if they form a part of a LUR, belong to a unique LUR. In this database we provide the composition of the LURs in terms of local administrative units (MUNI), Morphological Urban Area (MUA), Functional Urban Area (FUA).

When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'. The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities. This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2) The extent of this dataset is the EU 27 (2007) plus Croatia (HR), Norway (NO) and Switzerland (CH). The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry. A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

It is estimated that the cities of Cordova (modern-day Córdoba) and Palermo were the largest cities in Europe in 1050, and had between fifteen and twenty times the population of most other entries in this graph, Despite this the cities of Cordova (the capital city of the Umayyad caliphate, who controlled much of the Iberian peninsula from the seventh to eleventh centuries), and Palermo (another Arab-controlled capital in Southern Europe) were still the only cities in Western Europe with a population over one hundred thousand people, closely followed by Seville. It is also noteworthy to point out that the five largest cities on this list were importing trading cities, in modern day Spain or Italy, although the largest cities become more northern and western European in later lists (1200, 1330, 1500, 1650 and 1800). In 1050, todays largest Western European cities, London and Paris, had just twenty-five and twenty thousand inhabitants respectively.

The period of European history (and much of world history) between 500 and 1500 is today known as the 'Dark Ages'. Although the term 'Dark Ages' was originally applied to the lack of literature and arts, it has since been applied to the lack or scarcity of recorded information from this time. Because of these limitations, much information about this time is still being debated today.

The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8. The metropolitan areas considered in this study are those from this study of functional urban area definition. The corresponding geofile is available at this adress. Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context. As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from Natural Data and then selected by comparison with the polygones. Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included. The calculation of driving distances around centers have been performed with OpenRouteService (personal API key required, free of charge).

This dataset provides statistics on labour productivity, for large regions (TL2) and small regions (TL3).

Data source and definition

Labour productivity is measured as gross value added per employment at place of work by main economic activity. Regional gross value added and employment data are collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, labour productivity data in current prices are transformed into constant prices and PPP measures (link).

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting, see the list of OECD metropolitan regions (xlsx) and the EU methodology (link).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.