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

🇩🇪 Germany

The largest age groups among Berlin’s residential population were aged 25-39 and 40-59 years. The latter was actually the most represented age group in the German capital. The age group with the least number of residents were babies aged younger than one year. Slowly growing population Berlin’s residential population has been growing in recent years, though at a slow pace. Generally, the urban population in Germany has been increasing, with over 77 percent living in cities. Berlin does not have the most expensive rent space in Germany, compared to Munich in the south or Frankfurt in central Germany, which could be a draw for younger age groups moving to the capital. On the other hand, just as in the rest of the country, the city’s age group structure is affected by a struggling birth rate. Uncertain future Based on recent figures, Berlin’s total population was almost at four million. Germany’s population count currently stands at almost 84.5 million and is forecast to decrease rather than increase in the 2020s.

In der vorliegenden Studie werden vorhandene Daten zur Bevölkerungsstatistik (Geburten, Heiraten, Sterbefälle, Einwohner) auf der Basis der Preußischen Provinzen sowie anderer deutscher territorialer Gebietseinheiten für den Zeitraum von 1816 bis 1871 korrigiert und neu berechnet sowie fehlende Daten geschätzt. Zu den wesentlichen Verbesserungen dieser Datenkompilation gehört eine Verbreiterung des Quellmaterials, die Bestimmung der Bevölkerung für die Jahre zwischen den Volkszählungen auf der Basis des berichteten natürlichen Bevölkerungswachstums, und die Korrekturen der Bevölkerungsangaben der Volkszählungen aus den späten 1810er und 1830er Jahren. Die neu berechneten Bevölkerungsreihen legen es nahe, die Periode zwischen 1810 und 1870 als eine post-Malthusianische Epoche für Deutschland zu charakterisieren: ein hohes jährliches Bevölkerungswachstum geht einher mit weitestgehend stabilen Real-Löhnen für eine lange Periode. Die Expansion der Nachfrage nach Arbeit kompensiert die negativen Effekte des Bevölkerungswachstums auf den materiellen Wohlstand der Bevölkerung. (Georg Fertig et. al. (2018), S. 1) Zum Untersuchungsraum:Die Autoren haben es sich zum Ziel gesetzt, den Untersuchungsraum unter analytischen Gesichtspunkten so zu definieren, dass die Datenreihen mit den Daten des nachfolgenden Deutschen Reichs ab 1871 sowie mit den Daten für Deutschland in den Grenzen von 1990 vergleichbar sein sollen.Die Ergebnisse beziehen sich auf „diejenigen Gebiete, die sowohl Teil des Alten Reichs in den Grenzen von 1792 waren als auch zum Deutschen Bund zählten und schließlich beim 1871 neu gegründeten Kaiserreich verbleiben. Damit bleiben die historischen Kerngebiete Polens ebenso unberücksichtigt wie das mit dem Alten Reich nur lose verbundenen Ostpreußen. “ (Georg Fertig et.al. (2018), S. 4). Methodische Probleme:Je weiter die bevölkerungsstatistischen Daten zurückliegen, desto größer wird das Problem fehlender und verzerrter Werte. Die Autoren haben auf der Grundlage des ihnen zur Verfügung stehenden Quellenmaterials in der Forschung entwickelte Zählverbesserungen und Datenkorrekturen berücksichtigt. Dabei haben sie nach Möglichkeit zeitgenössisches Material herangezogen, um primär für die Zeit vor 1841 Bevölkerungsangaben zu korrigieren und zu ergänzen. Darüber hinaus haben sie die Interpolation der Bevölkerungsgröße für die Jahre zwischen den Volkszählungen im Vergleich zu den bisherigen Studien anders vorgenommen. Damit weichen die von den Autoren entwickelten Reihen von den bislang vorliegenden Zusammenstellungen teilweise deutlich ab. (Georg Fertig et.al. (2018), S. 7). Die erfassten und berechneten Zeitreihen-Daten beinhalten Vitalreihen (Geburten, Heiraten, Sterbefälle, Tot- und Lebendgeborene), den Einwohnerzahlen sowie die Größe der Territorien. Folgende Datentabellen können aus histat downgeloadet werden: A. Bevölkerungsstand und -bewegung in Preußen nach ProvinzenA.01 Provinz Holstein, 1815-1871A.02 Provinz Lauenburg, 1815-1871A.03 Provinz Brandenburg (ohne Berlin), 1815-1871A.04 Provinz Hessen-Nassau, 1866-1871A.05 Provinz Hohenzollern, 1815-1871A.06 Provinz Ostpreußen, 1815-1871A.07 Provinz Pommern, 1815-1871A.08 Provinz Posen, 1815-1871A.09 Provinz Sachsen, 1815-1871A.10 Provinz Schlesien, 1815-1871A.11 Provinz Westfalen, 1815-1871A.12 Provinz Westpreußen, 1815-1871A.13 Rheinprovinz, 1815-1871A.14 Provinz Berlin, 1815-1871 B. Weitere TerritorienB.01 Bevölkerungsstand und -bewegung der Region ´Amt Bergdorf´, 1815-1871B.02 Bevölkerungsstand und -bewegung der Hansestadt Bremen, 1815-1871B.03 Bevölkerungsstand und -bewegung der Stadt Hamburg, 1815-1871B.04 Bevölkerungsstand und -bewegung der Stadt Lübeck, 1815-1871B.05 Bevölkerungsstand und -bewegung der Stadt Frankfurt am Main, 1815-1871B.06 Bevölkerungsstand und -bewegung des Fürstentums Lippe-Detmold, 1815-1871B.07 Bevölkerungsstand und -bewegung des Fürstentums Schaumburg-Lippe, 1815-1871B.08 Bevölkerungsstand und -bewegung des Fürstentums Waldeck-Pyrmont, 1815-1871B.09 Bevölkerungsstand und -bewegung des Großherzogtums Oldenburg, 1815-1871B.10 Bevölkerungsstand und -bewegung des Großherzogtums Baden, 1815-1871B.11 Bevölkerungsstand und -bewegung Hessens, 1815-1871B.12 Bevölkerungsstand und -bewegung des Großherzog. Mecklenburg-Schwerin, 1815-1871B.13 Bevölkerungsstand und -bewegung des Großherzog. Mecklenburg-Strelitz (einschließlich des Fürstentums Ratzeburg), 1815-1871B.14 Bevölkerungsstand und -bewegung des Herzogtums Anhalt, 1815-1871B.15 Bevölkerungsstand und -bewegung des Herzogtums Braunschweig, 1815-1871B.16 Bevölkerungsstand und -bewegung im Herzogtum Nassau (bis 1865), 1815-1865B.17 Bevölkerungsstand und -bewegung des Herzogtums Schleswig, 1815-1871B.18 Bevölkerungsstand und -bewegung im Königreich Württemberg, 1815-1871B.19 Bevölkerungsstand und -bewegung im Königreich Bayern, 1815-1871B.20 Bevölkerungsstand und -bewegung im Königreich Hannover, 1815-1871B.21 Bevölkerungsstand und -bewegung im Königreich Sachsen, 1815-1871B.22 B...

“In the context of general urban research and also in historical investigations problems of housing provision and housing policies in the period of advanced industrialization play an important role. Those questions are most of the times related with the consequences of industrialization and urbanization because besides the necessary infrastructure services like the expansion of public transport, canalization, water and energy provision and facilities for health care, problems related to an adequate housing provision were more and more emphasized because the private housing economy was not able to deal with the high demand especially for small apartments. Especially the shortage of small apartments caused that questions and problems related to housing were considered as system-critically more than other social-political areas.” (Steitz, a. cit., p. 393f). Contemporary reformers and those who discussed questions related to housing formulated a high number of local political measures. Based on those discussions the author formulates his research question: “Which local political measures were undertaken by the communities of the German Empire between 1875 and 1914 under which circumstances? Some historical studies in this subject are already investigated local housing policies especially regarding the housing construction for workers in the entire German Empire. The local conditions and the measures undertaken by the different communities varied significantly. Therefore the present study tends to analyze the extent of communal housing policies looking at the case study of Frankfurt am Main because this city played an important role in this subject area” (Steitz, a. a. O., p. 397).

Data tables in HISTAT: A.01 Per capita tax burden in Frankfurt am Main, Berlin and Prussian communities with more than 10.000 inhabitants, in Mark (1890-1913) A.02 Development of population on the basis of the 1910 incorporated territory including Frankfurt (1871-1910) A.03 Relative per capita tax burden in Frankfurt am Main (1890-1905) A.04 Public debt of Frankfurt am Main (1887-1907) A.05 Proportion of expenditure for infrastructure spending on total expenditure and revenues, as well as on the direct tax burden, Frankfurt am Main (1872-1898) A.06 Share of taxes and operating surplus of the total ordinary revenues of the budget of the city of Frankfurt am Main (1904-1913) A.07 Overview of surpluses and grants from the regular budget of the city of Frankfurt am Main (1898-1913) A.08a Expenditures of Frankfurt am Main (1872-1881) A.08b Expenditures of Frankfurt am Main (1881-1897) A.08c Expenditures of the regular household of Frankfurt am Main (1898-1905) A.08d Expenditures for construction in the extraordinary budget of Frankfurt am Main (1898-1910) A.09 Ratio of urban mortgage sum of leaseholder on urban ground in Frankfurt am Main (1902-1905) A.10 Urban construction of small apartments in Frankfurt am Main (1904-1913) A.11Buildings and apartments constructed by charitable construction and housing associations (1868-1914) A.12 Proportion of newly built apartments on the total number of new apartments in Frankfurt am Main (1884-1914) A.13 Share of charitably built apartments on the total number of available and occupied apartments in Frankfurt am Main (1871-1910)

This data set includes R scripts & additional tables created by Lennart Gries for a master's thesis conducted at Johann Wolfgang Goethe-Universität Frankfurt am Main and Senckenberg Biodiversity- and Climate Research Centre (SBiK-F), being supervised by Prof. Susanne Fritz and Prof. Markus Pfenninger.

Temperature-dependent generation time data for ectotherm animals was collected from literature and incorporated into population models to predict a number of generations per year under different climatic scenarios. An increase in generations per year implies an increase of evolutionary rate for ectotherms with rising temperatures under stronger climate change scenario.

The extent of gene flow during the range expansion of non-native species influences the amount of genetic diversity retained in expanding populations. Here, we analyse the population genetic structure of the raccoon dog (Nyctereutes procyonoides) in north-eastern and central Europe. This invasive species is of management concern because it is highly susceptible to fox rabies and an important secondary host of the virus. We hypothesized that the large number of introduced animals and the species’ dispersal capabilities led to high population connectivity and maintenance of genetic diversity throughout the invaded range. We genotyped 332 tissue samples from seven European countries using 16 microsatellite loci. Different algorithms identified three genetic clusters corresponding to Finland, Denmark and a large ‘central’ population that reached from introduction areas in western Russia to northern Germany. Cluster assignments provided evidence of long-distance dispersal. The results of an Approximate Bayesian Computation analysis supported a scenario of equal effective population sizes among different pre-defined populations in the large central cluster. Our results are in line with strong gene flow and secondary admixture between neighbouring demes leading to reduced genetic structuring, probably a result of its fairly rapid population expansion after introduction. The results presented here are remarkable in the sense that we identified a homogenous genetic cluster inhabiting an area stretching over more than 1500km. They are also relevant for disease management, as in the event of a significant rabies outbreak, there is a great risk of a rapid virus spread among raccoon dog populations.

With the heightened threat to Germany's Jewish population following the Nazi Party's ascent to power in 1933, many German Jews chose to flee or emigrate. In 1933, Germany's Jewish population was approximately 500,000 people; by the end of the war, it is estimated that 300,000 fled the country, and 165,000 were murdered in the Holocaust. In order to flee, most Jewish emigrants from Germany had to give up the majority of their wealth to the German state, whose emigration tax and seizure of property stripped Jews of their financial assets. Destination and transit For Germany's Jewish refugees, the most common destination country was the United States, and almost half of all these refugees would arrive in the U.S. over this 12 year period. As the United States had a strict quota of 27,000 German migrants per year, many refugees were forced to enter via other countries. France was the second most common destination country, receiving 100,000 refugees. However, France was also used as a transit country for German Jews wishing to travel further afield, especially after it was annexed by Germany in 1940. This was also true for several other European countries, such as the Netherlands, which had provided protection for German Jews in the mid-1930s, before rapidly becoming very unsafe following the outbreak of war in 1939. The Frank family Possibly the most famous example of this was the story of Anne Frank and her family. Anne had been born in Frankfurt, Germany in 1929, but her family moved to the Netherlands in 1934 after Hitler came to power. The family then led a relatively comfortable and successful life in Amsterdam, with her father, Otto, founding his own businesses. When the Netherlands was invaded by the Germans in 1940, the family tried to emigrate once more; Otto had been granted a single Cuban visa in 1942, but the family was forced to go into hiding as the restrictions tightened. For the next two years, with the help of non-Jewish friends, they lived in secret in the upper floor of Otto's business premises with several other Jewish refugees, in a small space concealed behind a bookcase. In August 1944, through unknown means, the group was betrayed and then arrested by Dutch authorities, and the Frank family was sent to Auschwitz-Birkenau thereafter. Anne's mother, Edith, died of starvation in Auschwitz within five months of her capture, while Anne and her sister, Margot, died one month later after being transferred to the Bergen-Belsen camp in Germany. Otto was the sole survivor of the group. Otto's secretary, Miep Gies, had saved Anne's diary the day after the group was arrested, which she then gave to Otto; he then devoted much of the remainder of his life to the publication and promotion of his daughter's diary, which has now become one of the most famous and widely-read books in recent history. Additionally, the hiding space is now open to the public, and has become one of the Netherlands' most popular tourist museums.

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.

Résidents ayant leur résidence principale au 31.12. selon certaines caractéristiques et indicateurs (classes d'âge, sexe, nationalité, AFR, TFR, quartier, naissances, décès, religion, état matrimonial, emménagement, départ, déménagement, ménages). Source : registres de la population.

Identifying patterns in the effects of temperature on species' population abundances could help develop a general framework for predicting the consequences of climate change across different communities and realms. We used long-term population time series data from terrestrial, freshwater, and marine species communities within central Europe to compare the effects of temperature on abundance across a broad range of taxonomic groups. We asked whether there was an average relationship between temperatures in different seasons and annual abundances of species in a community, and whether species attributes (temperature range of distribution, range size, habitat breadth, dispersal ability, body size, and lifespan) explained interspecific variation in the relationship between temperature and abundance. We found that, on average, warmer winter temperatures were associated with greater abundances in terrestrial communities (ground beetles, spiders, and birds) but not always in aquatic communities (freshwater and marine invertebrates and fish). The abundances of species with large geographical ranges, larger body sizes, and longer lifespans tended to be less related to temperature. Our results suggest that climate change may have, in general, positive effects on species’ abundances within many terrestrial communities in central Europe while the effects are less predictable in aquatic communities.