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.

The largest Western European city in 1200 was Palermo, with 150 thousand inhabitants. This is a great decrease in the number 150 years previously, where the population was 350 thousand. The city of Cordova also decreased by almost 400 thousand in this time, possibly because of the declining Arabian control and influence in the area. Seville is the third largest city on this list, although it's overall population decreased by ten thousand since 1050. The largest cities are generally in Spain or Italy, although the second largest city on this list is Paris, with 110 thousand inhabitants. In the lists that follow, Paris remains at the top as either the largest (1500 and 1650) or second largest (1330 and 1800) city in Western Europe.

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.

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.

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.

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.

Between 1500 and 1800, London grew to be the largest city in Western Europe, with its population growing almost 22 times larger in this period. London would eventually overtake Constantinople as Europe's largest in the 1700s, before becoming the largest city in the world (ahead of Beijing) in the early-1800s.

The most populous cities in this period were the capitals of European empires, with Paris, Amsterdam, and Vienna growing to become the largest cities, alongside the likes of Lisbon and Madrid in Iberia, and Naples or Venice in Italy. Many of northwestern Europe's largest cities in 1500 would eventually be overtaken by others not shown here, such as the port cities of Hamburg, Marseilles or Rotterdam, or more industrial cities such as Berlin, Birmingham, and Munich.

Data on European cities were collected in the Urban Audit and in the Large City Audit project. The projects' ultimate goal is to contribute towards the improvement of the quality of urban life: it supports the exchange of experience among European cities; it helps to identify best practices; it facilitates benchmarking at the European level and provides information on the dynamics within the cities and with their surroundings.

At the city level, the Urban Audit contains more than 130 variables and more than 50 indicators. These indicators are derived from the variables collected by the European Statistical System.

The data is published in 20 tables within 2 main groups, plus a perception survey table:

Cities and greater cities (urb_cgc)

Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1)
Population structure - cities and greater cities (urb_cpopstr)
Population by citizenship and country of birth - cities and greater cities (urb_cpopcb)
Fertility and mortality - cities and greater cities (urb_cfermor)

Living conditions - cities and greater cities (urb_clivcon)

Education - cities and greater cities (urb_ceduc)

Culture and tourism - cities and greater cities (urb_ctour)
Labour market - cities and greater cities (urb_clma)
Economy and finance - cities and greater cities (urb_cecfi)
Transport - cities and greater cities (urb_ctran)
Environment - cities and greater cities (urb_cenv)

Functional Urban Area (urb_luz)

Population on 1 January by age groups and sex - Functional Urban Area (urb_lpop1)
Population structure - Functional Urban Area (urb_lpopstr)
Population by citizenship and country of birth - Functional Urban Area (urb_lpopcb)
Fertility and mortality - Functional Urban Area (urb_lfermor)
Living conditions - Functional Urban Area (urb_llivcon)
Education - Functional Urban Area (urb_leduc)
Labour market - Functional Urban Area (urb_llma)
Transport - Functional Urban Area (urb_ltran)
Environment - Functional Urban Area (urb_lenv)
Perception survey results (urb_percep)

Data has been collected on two spatial levels in the Urban Audit:

• The City (C) according to the administrative definition, as the basic level,
• The Functional Urban Area (FUA) being an approximation of the functional urban zone centered around the city

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.

Data on European cities were collected in the Urban Audit and in the Large City Audit project. The projects' ultimate goal is to contribute towards the improvement of the quality of urban life: it supports the exchange of experience among European cities; it helps to identify best practices; it facilitates benchmarking at the European level and provides information on the dynamics within the cities and with their surroundings.

At the city level, the Urban Audit contains more than 130 variables and more than 50 indicators. These indicators are derived from the variables collected by the European Statistical System.

The data is published in 20 tables within 2 main groups, plus a perception survey table:

Cities and greater cities (urb_cgc)

Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1)
Population structure - cities and greater cities (urb_cpopstr)
Population by citizenship and country of birth - cities and greater cities (urb_cpopcb)
Fertility and mortality - cities and greater cities (urb_cfermor)

Living conditions - cities and greater cities (urb_clivcon)

Education - cities and greater cities (urb_ceduc)

Culture and tourism - cities and greater cities (urb_ctour)
Labour market - cities and greater cities (urb_clma)
Economy and finance - cities and greater cities (urb_cecfi)
Transport - cities and greater cities (urb_ctran)
Environment - cities and greater cities (urb_cenv)

Functional Urban Area (urb_luz)

Population on 1 January by age groups and sex - Functional Urban Area (urb_lpop1)
Population structure - Functional Urban Area (urb_lpopstr)
Population by citizenship and country of birth - Functional Urban Area (urb_lpopcb)
Fertility and mortality - Functional Urban Area (urb_lfermor)
Living conditions - Functional Urban Area (urb_llivcon)
Education - Functional Urban Area (urb_leduc)
Labour market - Functional Urban Area (urb_llma)
Transport - Functional Urban Area (urb_ltran)
Environment - Functional Urban Area (urb_lenv)
Perception survey results (urb_percep)

Data has been collected on two spatial levels in the Urban Audit:

• The City (C) according to the administrative definition, as the basic level,
• The Functional Urban Area (FUA) being an approximation of the functional urban zone centered around the city

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 city of Paris in France had an estimated gross domestic product of 757.6 billion Euros in 2021, the most of any European city. Paris was followed by the spanish capital, Madrid, which had a GDP of 237.5 billion Euros, and the Irish capital, Dublin at 230 billion Euros. Milan, in the prosperous north of Italy, had a GDP of 228.4 billion Euros, 65 billion euros larger than the Italian capital Rome, and was the largest non-capital city in terms of GDP in Europe. The engine of Europe Among European countries, Germany had by far the largest economy, with a gross domestic product of over 4.18 trillion Euros. The United Kingdom or France have been Europe's second largest economy since the 1980s, depending on the year, with forecasts suggesting France will overtake the UK going into the 2020s. Germany however, has been the biggest European economy for some time, with five cities (Munich, Berlin, Hamburg, Stuttgart and Frankfurt) among the 15 largest European cities by GDP. Europe's largest cities In 2023, Moscow was the largest european city, with a population of nearly 12.7 million. Paris was the largest city in western Europe, with a population of over 11 million, while London was Europe's third-largest city at 9.6 million inhabitants.

Our Population Density Grid Dataset for Western Europe offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

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.

Costs of coastal flooding and protection are essential information for risk assessment and natural hazards research, but there are few systematic attempts to quantify cost curves beyond the case study level. Here, we present a set of systematically derived damage and protection cost curves for the 600 largest (by area) European coastal cities. The city clusters were identified by an automated cluster algorithm from CORINE land cover 2012 data, following the Urban Morphological Zone (UMZ) definition.The data provides detailed cost curves for direct flood damages at flood heights between 0 and 12 m on a 0.5 m increment. Costs estimates are based on depth damage functions for different land use obtained from the European Joint Research Center. The necessary mapping between land use and land cover is based on Land Use/Cover Area frame Survey (LUCAS) 2015 primary data. The underlying inundation maps were derived from the European Digital Elevation Model (EU-DEM).Furthermore, the data contain curves for the cost of protection at the same heights and increments as the damage curves, assuming no previously installed protection. These curves are available both for a low and high cost scenario and are based on hypothetical protection courses derived from cluster data and inundation maps.All cost estimates are given in Euro and were inflation-adjusted to 2016 price levels. For spatial reference, we include the individual raster tiles depicting the extent of each city cluster.The research leading to these results has received funding from the European Community's Seventh Framework Programme under Grant Agreement No. 308497 (Project RAMSES). Supplement to: Prahl, Boris F; Boettle, Markus; Costa, Luis; Kropp, Jürgen P; Rybski, Diego (2018): Damage and protection cost curves for coastal floods within the 600 largest European cities. Scientific Data, 5(1), 180034

This vector dataset shows the data of the number of people 75 years old or older in a number of cities across Europe. The data was obtained from Eurostat dataset of cities and greater cities and joined to the Urban Audit 2011-2014 cities' centroids.

Older people tend to be more affected by climate-related hazards, mainly heatwaves but also flooding. The number of older people and their poportion in the population should be considered in planning adaptation to climate change in order to design and implement appropriate actions.

In 2024, Russia had the largest population among European countries at ***** million people. The next largest countries in terms of their population size were Turkey at **** million, Germany at **** million, the United Kingdom at **** million, and France at **** million. Europe is also home to some of the world’s smallest countries, such as the microstates of Liechtenstein and San Marino, with populations of ****** and ****** respectively. Europe’s largest economies Germany was Europe’s largest economy in 2023, with a Gross Domestic Product of around *** trillion Euros, while the UK and France are the second and third largest economies, at *** trillion and *** trillion euros respectively. Prior to the mid-2000s, Europe’s fourth-largest economy, Italy, had an economy that was of a similar sized to France and the UK, before diverging growth patterns saw the UK and France become far larger economies than Italy. Moscow and Istanbul the megacities of Europe Two cities on the eastern borders of Europe were Europe’s largest in 2023. The Turkish city of Istanbul, with a population of 15.8 million, and the Russian capital, Moscow, with a population of 12.7 million. Istanbul is arguably the world’s most famous transcontinental city with territory in both Europe and Asia and has been an important center for commerce and culture for over 2,000 years. Paris was the third largest European city with a population of ** million, with London being the fourth largest at *** million.

The global market for Climate Resilient City is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX%. The key drivers of this growth include the increasing frequency and severity of extreme weather events, the rising population of urban areas, and the growing awareness of the importance of climate resilience. However, the market is also constrained by the high cost of implementing climate-resilient measures and the lack of awareness about the benefits of these measures. North America is the largest regional market for climate resilient cities, followed by Europe and Asia-Pacific. The United States is the largest market in North America, due to its large population and its exposure to a variety of extreme weather events. China is the largest market in Asia-Pacific, due to its rapid urbanization and its growing awareness of the importance of climate resilience.

Résumé

Le projet de recherche MAFE est une initiative de grande ampleur dont l'objectif est d'étudier les migrations entre l'Afrique subsaharienne et l'Europe. - Attention, la documentation des enquêtes MAFE est en langue anglaise. -

The MAFE project is a major research initiative focused on migration between Sub-Saharan Africa and Europe. It brings together ten European and African research centres working on international migration.

In the early XXIth Century, international migration from Sub-Saharan Africa to Europe has generated increasing public and policy attention. The flotilla of boats bringing would-be migrants to the Canary Islands, and attempts to reach Spanish territory in Ceuta and Mellila have drawn a rapid response from Europe in the form of new policy measures. Yet the scope, nature and likely development of Sub-Saharan African migration to Europe remained poorly understood, and, as a result, European polices may be ineffective. A major cause of this lack of understanding was the absence of comprehensive data on the causes of migration and circulation between Africa and Europe.

The MAFE project aimed at overcoming this lack of understanding by collecting unique data on the characteristics and behavior of migrants from Sub-Saharan countries to Europe. The key notion underpinning the project was that migration must not only be seen as a one-way flow from Africa to Europe. The argument was that return migration, circulation and transnational practices are significant and must be understood in order to design better migration policy.

The MAFE project focused on migration flows between Europe (Belgium, France, Italy, the Netherlands, Spain and the UK) and Senegal, the Democratic Republic of Congo and Ghana, which together accounted for over a quarter of all African migration to the EU at the time of the survey. In each of these "migration systems", the survey was designed to document four key areas: - Patterns of migration : *the socio-demographic characteristics of migrants, *the routes of migration from Africa to Europe, and *the patterns of return migration and circulation. - Determinants of migration: looking at departure, but also return and circulation and taking into account the whole set of possible destinations. - Migration and Development: MAFE documents some of the socio-economic changes driven by international migration, looking as often as possible at both ends of the Afro-European migration system, at the individual level. - Migrations and Families: the data collected by the MAFE project can be used to study all sorts of interactions between family formation and international migration. Although the survey was primarily designed to study international migration, it can also be used to study other phenomena, especially in Africa: domestic mobility, labor market participation, family formation, etc. Comparable data was collected in both 3 sending and 6 destination countries, i.e. in sub-Saharan Africa and in Europe. The data are longitudinal - including retrospective migration, education, work and family histories for individuals - and multi-level - (with data collected at the individual and household levels, in addition of macro-contextual data).

Please consult the official MAFE website for further details : https://mafeproject.site.ined.fr/en/

Geographic coverage

Six European countries and three African countries participated in the MAFE surveys. Data collection was carried out in both sending countries in Africa and destination countries in Europe, in order to constitute transnational samples. For MAFE Senegal, data was collected in Senegal (African part) and France, Italy and Spain (European part).

Analysis unit

Individual Household

Univers

SENEGAL Household: Households selected randomly from the updated list of households in the selected primary sampling units. Two strata were distinguished: the households with migrants and those without migrants. Individual: People aged 25-75 at the time of the survey, born in Senegal and who have/had Senegalese citizenship. This lower age limit was set in order to obtain informative life histories. By not including respondents younger than 25, the resources were used more effectively. The place of birth criterion was used to exclude people who were born out of their country of origin in order to exclude second generation migrants in Europe and to increase the homogeneity of sample. Up to two return migrants and partners of migrants, and one randomly selected other eligible person. Return migrants were eligible if their first departure was above at 18 or over.

EUROPE In all the European countries, the surveys were conducted among males and females who were aged 25 and over at the time of the surveys, and who were 18 or over when they had left Africa for the first time for at least one year. For MAFE Senegal, only migrants from Senegal were interviewed. This was a way to reinforce the homogeneity of the sample by excluding people of the 1.5 generation who are often "passive" migrants.

In theory, surveyed individuals must be representative of the whole population with these characteristics in the departure region and in the destination countries. The sample is composed of males and females. In Europe, in spite of a gender demographic disequilibrium, the objective was to include 50% of males and 50% of females in order to allow gender analyses.

Kind of data

survey data

Frequency of data collection

SENEGAL In Senegal, data collection activities started in November 2007 (selection of survey sites in Dakar and listing of households in the selected sites). They ended in September 2008 (data entry and data cleaning). Overall, 11 months were necessary to carry out all the activities related to data collection, and fieldwork lasted a little less than 6 months. Data collection was organized in two separate stages: the household survey was first conducted, and the biographic survey started after the household survey had been completed. The data collected in the household survey was used to prepare a sampling frame of individuals for the biographic survey; quick data entry of part of the questionnaires of the household survey was thus necessary before starting data collection for the biographic survey. Although this approach had advantages, it also lengthened the data collection process. This approach was not used for surveys in Ghana and DR Congo, where both surveys were conducted simultaneously.

EUROPE In France, Italy and Spain the surveys were conducted in 2008, before the start of the EU funded project. Data collection activities lasted approximately 6 months. Note: A second round was carried in Spain in 2010. About 400 Senegalese migrants were interviewed using exactly the same questionnaire. The data will be released in the future. For more information, contact: pau.baizan@upf.edu

Sampling procedure

Probability: Stratified

  SENEGAL

A three-stage stratified random sample was used. At the first stage, primary sampling units (census district) were selected randomly with varying probabilities. At the second stage, households were selected randomly in each of the selected primary sampling units (PSUs). At the third stage, individuals were selected within the households. a) Selection of primary sampling units (first stage) In the Senegal survey, the sample was designed to be probabilistic and representative of the Dakar region, and at the same time to maximize the chance of reaching households 'affected' by international migration (rare population). The sampling frame used to select the primary sampling units was the 2002 Population Census. The census districts (CD) -which are usually used as the primary sampling units in surveys in Senegal - have an average size of 100 households in urban areas. 60 primary sampling units were randomly selected at the first stage. This number of primary sampling units allows reaching a balance between a large dispersion of households (which decreases sampling errors) and a more concentrated sample (which reduces costs). The region of Dakar was divided into 10 strata of equal size, according to the % of migrant households within each of them (in average, 11.6% of the households were 'migrants'). 6 CD's per stratum were drawn, with a probability proportional to the number of households within each CD. In other words, census districts with a large number of migrants were more likely to be selected than those with low numbers of migrants. This approach increases the number of migrants interviewed in the individual survey, while still having a probabilistic sample representative of the target area. The listing of the households in the 60 selected primary sampling units was updated in order to select the sample of households. This stage was essential because a lot of changing occurred in some large neighbourhoods of Dakar since the previous census (2002), especially in suburban areas. This counting also allowed distinguishing between households with and without migrants. b) Selection of households (second stage) The following approach was used in MAFE-Senegal: - Households were selected randomly (using systematic random sampling) from the updated list of households in the selected PSUs. Two strata were distinguished: the households with migrants and those without migrants. A maximum of 50% of households with migrants were drawn in each district. Selected households that could not be reached (absence, refusals,…) were not replaced during the fieldwork. Replacement would distort the computation of sampling weights, and could also lead to bias the sample. To take account of refusals and absences

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2015 and 2016. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..Occupation codes are 4-digit codes and are based on Standard Occupational Classification 2010..Industry codes are 4-digit codes and are based on the North American Industry Classification System 2012. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Methodological changes to data collection in 2013 may have affected language data for 2013. Users should be aware of these changes when using 2013 data or multi-year data containing data from 2013. For more information, see: .Language User Note..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates