Berlin attracted the most tourist arrivals in Germany in 2023, which is perhaps to be expected. But the country has other city destinations with a lot to offer as well. Among them Hamburg in the north with the largest port in Germany and Munich, the capital of Bavaria, in the south. All three cities also have busy airports and railways. The top three Berlin recorded a significant drop in travel accommodation numbers in 2020, the year the COVID-19 pandemic began. Figures began to pick up again later, with 724 establishments open in 2024, though this was still less than in previous years. The numbers may also have to do with competition from such accommodation platforms as Airbnb. Hamburg is also catching up after the pandemic in terms of tourism, with noticeably more travel arrivals recorded recently. While 2021 saw around 3.3 million arrivals, there were already roughly 6.4 in 2023. Meanwhile, the average occupancy rate in Munich travel accommodation was also noticeably higher in 2022 than in previous years, with an almost 48 percent total. City tourism As of 2023, around 33.7 million people in Germany preferred city trips as a vacation. Numbers decreased somewhat in recent years. Various factors contribute to making a city attractive for tourists, among them mobility options, the ease of getting around, and spending time outside. In 2022, Germany’s largest cities were evaluated in terms of walking and cycling space availability. Munich scored highest at 51 percent, followed by Cologne with 46 percent and Hamburg with 42 percent. That same year, these cities were also evaluated in terms of traffic safety for pedestrians and cyclists. This time, Hamburg came first at 74 percent, and Berlin followed with 72 percent.

In 2023, around 27.4 percent of residents in Bremen were at risk of living in poverty. This list shows the 15 cities in Germany with the highest at-risk-of-poverty rates.

Out of the four largest cities in Germany, Munich received the highest score for space for walking and cycling in 2022. Munich scored 51 percent in the space for people category, which indicates how much road space is allocated to walking and cycling infrastructure, as well as levels of construction. Munich was followed by Cologne, which scored 46 percent.

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.

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

As of mid-2023, Hamburg was the German city with the highest public electric vehicle charging infrastructure index score, at ** percent. Munich was the only other German city with a score above ** percent.

Chart and table of population level and growth rate for the Berlin, Germany metro area from 1950 to 2025.

Social and economic figures for 67 large West German cities. The data aggregated at city level have been collected for most topics over several years, but not necessarily over the entire reference time period.

Topics: 1. Situation of the city: surface area of the city; fringe location in the Federal Republic.

1. Residential population: total residential population; German and foreign residential population.

2. Population movement:live births; deaths; influx; departures; birth rate; death rate; population shifts; divorce rate; migration rate; illegitimate births.

3. Education figures: school degrees; occupational degrees; university degrees.

4. Wage and income: number of taxpayers in the various tax classes as well as municipality income tax revenue in the respective classes; calculated income figures, such as e.g. inequality of income distribution, mean income or mean wage of employees as well as standard deviation of these figures; GINI index.

5. Gross domestic product and gross product: gross product altogether; gross product organized according to area of business; gross domestic product; employees in the economic sectors.

6. Taxes and debts: debt per resident; income tax and business tax to which the municipality is entitled; municipality tax potential and indicators for municipality economic strength.

7. Debt repayment and management expenditures: debt repayment, interest expenditures, management expenditures and personnel expenditures.

8. From the ´BUNTE´ City Test of 1979 based on 100 respondents per city averages of satisfaction were calculated. satisfaction with: central location of the city, the number of green areas, historical buildings, the number of high-rises, the variety of the citizens, openness to the world, the dialect spoken, the sociability, the density of the traffic network, the OEPNV prices {local public passenger transport}, the supply of public transportation, provision with culture, the selection for consumers, the climate, clean air, noise pollution, the leisure selection, real estate prices, the supply of residences, one´s own payment, the job market selection, the distance from work, the number of one´s friends, contact opportunities, receptiveness of the neighbors, local recreational areas, sport opportunities and the selection of further education possibilities.

9. Traffic and economy: airport and Intercity connection; number of kilometers of subway available, kilometers of streetcar, and kilometers of bus lines per resident; car rate; index of traffic quality; commuters; property prices; prices for one´s own home; purchasing power.

10. Crime: recorded total crime and classification according to armed robbery, theft from living-rooms, of automobiles as well as from motor vehicles, robberies and purse snatching; classification according to young or adult suspects with these crimes; crime stress figures. 12. Welfare: welfare recipients and social expenditures; proportion of welfare recipients in the total population and classification according to German and foreign recipients; aid with livelihood; expenditures according to the youth welfare law; kindergarten openings; culture expenditures per resident. 13. Foreigners: proportion of foreigners in the residential population.

11. Students: number of German students and total number of students; proportion of students in the residential population.

12. Unemployed: unemployment rate; unemployed according to employment office districts and employment office departments.

13. Places of work: workers employed in companies, organized according to area of business.

14. Government employees: full-time, part-time and total government employees of federal government, states and municipalities as well as differentiated according to workers, employees, civil servants and judges.

15. Employees covered by social security according to education and branch of economy: proportion of various education levels in the individual branches of the economy.

This statistic shows the degree of urbanization in Germany from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 77.77 percent of Germany's total population lived in urban areas and cities. Urbanization in Germany Currently, about three quarter of the German population live in urban areas and cities, which is more than in most nations around the world. Urbanization, as it can be seen in this graph, refers to the number of people living in an urban area and has nothing to do with the actual geographical size or footprint of an area or country. A country which is significantly bigger than Germany could have a similar degree of urbanization, just because not all areas in the country are inhabitable, for example. One example for this is Russia, where urbanization has reached comparable figures to Germany, even though its geographical size is significantly bigger. However, Germany’s level of urbanization does not make the list of the top 30 most urbanized nations in the world, where urbanization rates are higher than 83 percent. Also, while 25 percent of the population in Germany still lives in rural areas, rural livelihoods are not dependent on agriculture, as only 0.75 percent of GDP came from the agricultural sector in 2014. So while Germany's urbanization rate is growing, a significant percentage of the population is still living in rural areas. Furthermore, Germany has a number of shrinking cities which are located to the east and in older industrial regions around the country. Considering that population growth in Germany is on the decline, because of low fertility rates, and that a number of cities are shrinking, the urban population is likely shifting to bigger cities which have more economic opportunities than smaller ones.

Out of the four largest cities in Germany, Hamburg was scored the safest for walking and cycling in 2022. The northern German city scored ** percent in the safe roads category, which indicates how many pedestrian and cyclist fatalities occurred in the city in relation to its population.

This data includes Luxembourg cities with population 5 thousand and more created by GfK MACON company, Germany.

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.

The compilation of commercial property price indices is challenging. Policymakers urge for timely, reliable and comprehensive data. In Germany, lack of data prevents the calculation of official figures by the national statistical authority. Different applications of price indices need different definitions of commercial real estate. Commercial property price indices according to these definitions are constructed on the basis of existing data for 127 German towns and cities (that cover about one-third of German population). The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Price increases for all definitions have been strongest in the seven largest cities. The definitions tend to lead to more marked differences for medium-sized towns.

The present study is an exemplary analysis of the development and the changes of living in a big city of the 19th century. The study tries to develop indicators for the evaluation of the urban housing provision during the process of urbanization that relate occurrence and extent of housing shortage with quantifiable standards of housing provision looking at the example of Hamburg in the second part of the 19th century. Thereby a long term analysis of the material circumstances of housing conditions will be shown and at the same time the concept of housing shortage in its historical relativity through a comparison with the living conditions in the Federal Republic of Germany will be clarified. Based on this, in the second step standards and social disparities of housing in their social stratification and in its urban structure change will be examined. In how far the example of Hamburg in the late 19th century can be generalized will be reviewed through a comparison of housing types and housing provision levels of German cities using a cross-sectional comparison of 1905. “Based on a comparison of metropolitan living situations it will be attempted to show the level and extend of disparities in the housing provision in the late 19th century using a typology of metropolitan housing structures. We assume that the housing structure of a city can only be represented in a very imperfect way using a more or less arbitrary chosen random variable. Much more information can be derived from the correlation of many different characteristics of the basic structure of urban housing, that can be understood as quasi-independent variables and that build the basis of the comparison. For a hierarchical cluster analysis with 27 variables 30 cities were considered for statistical classification and an empirical typecast. The used data is based on statistical housing surveys of German cities that were made in the context of the population census from the first December of 1905 and on the information if the profession and establishment census from July 1907. Three fourth of the cities (with more than 100.000 inhabitants) of the German Empire were selected” (Wischermann, a. cit., p. 401).“The excellent public statistics from Hamburg were, especially in the turn of the century, adjusted with many private surveys. Therefor the state of the quantitative sources for Hamburg is probably the best one of the German Empire… The situation of the sources for the investigation of the development of the housing circumstances in the 19th century in Hamburg is better than in other parts of Germany. This data basis enabled this study going back until the beginning of the industrialization; it enabled the investigation of one of the most important German case studies of changes in urban housing in the 19th century in a city that was at the frontier of English, French and Berlin zones of influence on the development of living circumstances in Germany. It is also a paradigm for the investigation of structural changes of housing within a city under the impact of economic transitions (extension of the harbor), hygienic innovation (since the cholera epidemic) and several urban development projects. Especially in the German urbanization period quality and structure of housing in the developing cities are connected with the urban area and the socio-spatial differentiation of housing in an extent that has not been known so far.” (Wischermann, a. cit., p. 13, p. 15). Data tables in HISTAT:(In addition, the cross-sectional data for 1905 for cluster analysis of German cities (30 cities, 27 variables for the year 1905) can be ordered at the GESIS data archive number ZA8474). A. Tables from the appendixA.01 Development of the population in the inner city and the suburbs of Hamburg (1817-1866)A.02 Housing stock in the inner city and the suburbs of Hamburg (1817-1866)A.03 Present local population of Hamburg and the districts (1867-1910)A.04 Movement of the population in Hamburg (1864-1913)A.05 The population density of Hamburg’s districts (1871-1910)A.06 Housing stock density of Hamburg’s districts(1867-1910)A.07 The provision level of housing in Hamburg (1867-1912)A.08 Internal density of Hamburg and its districts A: residents per room without kitchens (1885-1910)A.09 Internal density of Hamburg and its districts B: inhabitants per heated room (1885-1910)A.10 Occupancy of Hamburg and its districts: residents per apartment (1867-1910)A.11 Empty rooms and their rental value in Hamburg (1866-1913)A.12 Empty rooms in Hamburg and its districts (1867-1910) A.13 New construction, alteration and demolition statistics of the city of Hamburg (1885-1912)A.14 Small housing production in Hamburg (1896-1912)A.15 Landowner relations in Hamburg (1875-1910)A.16 Development of floor living in Hamburg (1867-1910)A.17 Residents after number of floors on Hamburg (1867-1910)A.18 Basement apartments in the districts of Hamburg (1867-1910)A.19 Apartments in the back building in Ham...

Among the four largest cities in Germany, Cologne scored the highest for availability of shared bicycles and e-scooters, indicating that it had the largest number of shared micro-mobility vehicles in relation to its population size. Hamburg ranked second among the largest cities in the country.

BackgroundLittle is known about knowledge, attitudes and behaviors concerning Chagas disease (CD) among Latin American migrants in Germany to inform public health decision making.MethodsA cross-sectional, questionnaire-based study was conducted between March 2014 and October 2019 among Latin American migrants in six cities in Germany to obtain information on migration history, socioeconomic and insurance status, knowledge about CD, potential risk factors for Trypanosoma cruzi infection, and willingness to donate blood or organs.Results168 participants completed the questionnaire. The four countries with the highest proportion of participants contributing to the study population were Colombia, Mexico, Peru and Ecuador. Before migrating to Europe, the majority of the study population resided in an urban setting in houses made of stone or concrete, had higher academic education and was integrated into the German healthcare and healthcare insurance system. The majority of all study participants were also willing to donate blood and organs and a quarter of them had donated blood previously. However, many participants lacked basic knowledge about symptoms and modes of transmission of Chagas disease. One out of 56 serologic tests (1.8%) performed was positive. The seropositive female participant born in Argentina had a negative PCR test and no signs of cardiac or other organ involvement.ConclusionsThe study population does not reflect the population structure at risk for T. cruzi infection in endemic countries. Most participants had a low risk profile for infection with T. cruzi. Although the sample size was small and sampling was not representative of all persons at risk in Germany, the seroprevalence found was similar to studies previously conducted in Europe. As no systematic screening for T. cruzi in Latin American blood and organ donors as well as in women of child-bearing age of Latin American origin is implemented in Germany, a risk of occasional transmission of T. cruzi remains.

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.

The dataset covers the North Sea region (United Kingdom, Netherlands, Belgium, France, Denmark, Norway and Germany) and is made up of GIS shapefiles containing population numbers of (approximately) 100 cities with the highest population, for 8 reference years 1300, 1500, 1700, 1850, 1900, 1950, 1990 and 2015. The data set has been compiled on the basis of existing literature, supplemented with further research and provides a more detailed insight into the urbanization process around the North Sea.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Industry titles and their 4-digit codes are based on the 2022 North American Industry Classification System. 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..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle.."With a computer" includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..When this table is iterated for a place of birth group, the total population excludes individuals born at sea..When this table is iterated for a place of birth outside of the U.S., the total population is limited to the foreign-born population..When this table is iterated for place of birth in ...

In 2023, total tourist arrivals amounted to around 12.1 million, and 4.27 million of these were visitors from abroad. Berlin is the German capital and one of the most popular tourist destinations in the country, as well as among European cities in general. Tourist magnet Berlin’s residential population was over 3.7 million people in 2022. Visitors could choose from a total of 724 travel accommodation establishments to stay at. Among these, the most represented were bed and breakfast locations, followed by hotels and vacation homes. The city recorded around 29.6 million tourist overnight stays in 2023, which was an increase compared to the year before. In addition to a thriving accommodation industry and lots of famous history, Berlin offers a lot of opportunities to enjoy nature in or not far from the city. The German capital also houses one of the most famous attractions in Europe and the world - the Berlin Wall. Berlin booming Berlin is one of the three German city-states, alongside Hamburg and Bremen. This means that in addition to being metropolitan areas, all three also have federal state status. Germany has a total of 16 federal states. Berlin as the capital is recognizable all over the world for its still visible traces of both East and West German history.