Population density (people per sq. km of land area) in Switzerland was reported at 225 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Switzerland - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Historical dataset showing Switzerland population density by year from 1961 to 2022.

Population density. Map types: Lines, Choropleths. Spatial extent: Switzerland. Times: 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020. Spatial units: Cantons, Cantons, habitable area, Districts, Districts, habitable area, Communes, Communes, habitable area

Switzerland Population Density: People per Square Km data was reported at 214.243 Person/sq km in 2017. This records an increase from the previous number of 211.897 Person/sq km for 2016. Switzerland Population Density: People per Square Km data is updated yearly, averaging 168.153 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 214.243 Person/sq km in 2017 and a record low of 137.480 Person/sq km in 1961. Switzerland Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Population density of Switzerland went up by 0.83% from 220.3 people per sq. km in 2021 to 222.2 people per sq. km in 2022. Since the 1.07% improve in 2012, population density jumped by 9.76% in 2022. Population density is midyear population divided by land area in square kilometers.

Population density: point cloud. Map type: Symbols. Spatial extent: Switzerland. Times: 2000, 2005, 2010, 2015, 2020. Spatial unit: Communes, settlement area

Switzerland Population Density: Inhabitants per sq km data was reported at 222.120 Person in 2022. This records an increase from the previous number of 220.310 Person for 2021. Switzerland Population Density: Inhabitants per sq km data is updated yearly, averaging 189.390 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 222.120 Person in 2022 and a record low of 169.810 Person in 1990. Switzerland Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Switzerland – Table CH.OECD.GGI: Social: Demography: OECD Member: Annual.

BackgroundAssessing exposure to infections in early childhood is of interest in many epidemiological investigations. Because exposure to infections is difficult to measure directly, epidemiological studies have used surrogate measures available from routine data such as birth order and population density. However, the association between population density and exposure to infections is unclear. We assessed whether neighbourhood child population density is associated with respiratory infections in infants.MethodsWith the Basel-Bern lung infant development study (BILD), a prospective Swiss cohort study of healthy neonates, respiratory symptoms and infections were assessed by weekly telephone interviews with the mother throughout the first year of life. Using population census data, we calculated neighbourhood child density as the number of children < 16 years of age living within a 250 m radius around the residence of each child. We used negative binomial regression models to assess associations between neighbourhood child density and the number of weeks with respiratory infections and adjusted for potential confounders including the number of older siblings, day-care attendance and duration of breastfeeding. We investigated possible interactions between neighbourhood child population density and older siblings assuming that older siblings mix with other children in the neighbourhood.ResultsThe analyses included 487 infants. We found no evidence of an association between quintiles of neighbourhood child density and number of respiratory symptoms (p = 0.59, incidence rate ratios comparing highest to lowest quintile: 1.15, 95%-confidence interval: 0.90–1.47). There was no evidence of interaction with older siblings (p = 0.44). Results were similar in crude and in fully adjusted models.ConclusionsOur study suggests that in Switzerland neighbourhood child density is a poor proxy for exposure to infections in infancy.

Comprehensive socio-economic dataset for Switzerland including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Agglomerations: population density (1930-2000). Map types: Lines, Choropleths. Spatial extent: Switzerland. Times: 1930, 1941, 1950, 1960, 1970, 1980, 1990, 2000. Spatial units: Communes, Agglomerations

Density of physicians of Switzerland rose by 1.23% from 4.4 number per thousand population in 2020 to 4.4 number per thousand population in 2021. Since the 0.63% upward trend in 2011, density of physicians soared by 15.88% in 2021.

This study contains selected demographic, social, economic, public policy, and political comparative data for Switzerland, Canada, France, and Mexico for the decades of 1900-1960. Each dataset presents comparable data at the province or district level for each decade in the period. Various derived measures, such as percentages, ratios, and indices, constitute the bulk of these datasets. Data for Switzerland contain information for all cantons for each decennial year from 1900 to 1960. Variables describe population characteristics, such as the age of men and women, county and commune of origin, ratio of foreigners to Swiss, percentage of the population from other countries such as Germany, Austria and Lichtenstein, Italy, and France, the percentage of the population that were Protestants, Catholics, and Jews, births, deaths, infant mortality rates, persons per household, population density, the percentage of urban and agricultural population, marital status, marriages, divorces, professions, factory workers, and primary, secondary, and university students. Economic variables provide information on the number of corporations, factory workers, economic status, cultivated land, taxation and tax revenues, canton revenues and expenditures, federal subsidies, bankruptcies, bank account deposits, and taxable assets. Additional variables provide political information, such as national referenda returns, party votes cast in National Council elections, and seats in the cantonal legislature held by political groups such as the Peasants, Socialists, Democrats, Catholics, Radicals, and others. Data for Canada provide information for all provinces for the decades 1900-1960 on population characteristics, such as national origin, the net internal migration per 1,000 of native population, population density per square mile, the percentage of owner-occupied dwellings, the percentage of urban population, the percentage of change in population from preceding censuses, the percentage of illiterate population aged 5 years and older, and the median years of schooling. Economic variables provide information on per capita personal income, total provincial revenue and expenditure per capita, the percentage of the labor force employed in manufacturing and in agriculture, the average number of employees per manufacturing establishment, assessed value of real property per capita, the average number of acres per farm, highway and rural road mileage, transportation and communication, the number of telephones per 100 population, and the number of motor vehicles registered per 1,000 population. Additional variables on elections and votes are supplied as well. Data for France provide information for all departements for all legislative elections since 1936, the two presidential elections of 1965 and 1969, and several referenda held in the period since 1958. Social and economic data are provided for the years 1946, 1954, and 1962, while various policy data are presented for the period 1959-1962. Variables provide information on population characteristics, such as the percentages of population by age group, foreign-born, bachelors aged 20 to 59, divorced men aged 25 and older, elementary school students in private schools, elementary school students per million population from 1966 to 1967, the number of persons in household in 1962, infant mortality rates per million births, and the number of priests per 10,000 population in 1946. Economic variables focus on the Gross National Product (GNP), the revenue per capita per household, personal income per capita, income tax, the percentage of active population in industry, construction and public works, transportation, hotels, public administration, and other jobs, the percentage of skilled and unskilled industrial workers, the number of doctors per 10,000 population, the number of agricultural cooperatives in 1946, the average hectares per farm, the percentage of farms cultivated by the owner, tenants, and sharecroppers, the number of workhorses, cows, and oxen per 100 hectares of farmland in 1946, and the percentages of automobiles per 1,000 population, radios per 100 homes, and cinema seats per 1,000 population. Data are also provided on the percentage of Communists (PCF), Socialists, Radical Socialists, Conservatives, Gaullists, Moderates, Poujadists, Independents, Turnouts, and other political groups and p

Risk factors for respiratory symptoms.

Interaction between child density and number of siblings as predictor of respiratory symptoms.

Aggregate data from the areas of economy, culture, administration and population-statistical data.

Topics: for the reference time interval of 1900 to 1970 in steps of 10 years the following was surveyed: cultivated land in hectares; population density; percentage of population in cities; total population; number of households; migrations (beginning with 1888); total Swiss population; married men over 20 years; population according to age classes; birth rate; country of origin of immigrants; native language; religious affiliation; identification numbers of employed population classified according to area of business; number of businesses; number of factory workers; electricity consumption; infant mortality (likewise since 1888); number of doctors; data on the education system; number of schoolchildren; public expenditures; tax revenue; number of imprisonments; election abstentions; vote proportions of parties; seat distribution in Canton Council; distance in kilometers to canton capital; size of area.

Characteristics of included infants from the BILD cohort.

CH：人口密度：每平方公里人口在12-01-2017达214.243Person/sq km，相较于12-01-2016的211.897Person/sq km有所增长。CH：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为168.153Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达214.243Person/sq km，而历史最低值则出现于12-01-1961，为137.480Person/sq km。CEIC提供的CH：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的瑞士 – 表 CH.世行.WDI：人口和城市化进程统计。

Illegal hunting represents a major threat to the conservation of predators, but its impact remains difficult to assess as there are strong incentives to conceal this criminal activity. Attributing declines of carnivores to poaching is therefore an important conservation challenge. We present a case study of the Eurasian lynx (Lynx lynx) in the Swiss Alps (Valais) where the current distribution range is smaller than in the recent past and population density is by ≥80% lower than in the adjacent Swiss Prealps. We tested four hypotheses to explain this lower density: (1) a too low density of camera-traps deployed for lynx surveys in Valais compared to the Prealps (methodological artifact); (2) less favorable environmental conditions around the camera-trap sites; (3) lower densities of the main prey; and (4) poaching. We estimated lynx and ungulate densities and environmental conditions at trail camera sites and could clearly reject the first three hypotheses because: (1) the survey protocol was similarly effective; (2) environmental conditions around the trapping sites in Valais were even more favorable for lynx detection than in the Prealps; and (3) prey supply was even larger in Valais. Concerning hypothesis 4, we discovered a network of illegal lynx traps (neck snares) in the main immigration corridor into Valais from the thriving adjacent lynx population in the Prealps, suggesting intense local poaching. Our findings substantiate the suspicions of long-lasting lynx poaching as a threat to the establishment and survival of the Valais population. The fact that instances of poaching were publicly known since 1995 but remained unabated for at least two decades, until the first conviction occurred, questions the commitment of local authorities to address this case of wildlife crime. Our study demonstrates the need for inquiries about poaching of top predators to be carried out at the highest levels of jurisdiction to avoid any risk of collusion between law enforcement agents and poachers.

In 2022, Switzerland had the highest number of practicing nurses per capita, that is, for every 1,000 population there were 18 practicing nurses in Switzerland. This is followed by Norway and Iceland. This statistic portrays the number of practicing nurses in selected countries as of 2021, per 1,000 population.

Comparison of clinical and perinatal attributes between clustered* and nonclustered cases of CL both unadjusted and adjusted for local child population density.