This statistic shows the ten biggest cities in Switzerland, as of 2020, by number of inhabitants. In 2020, Zurich was Switzerland's most-populous city with approximately 421,878 inhabitants. See Switzerland's population figures for comparison.

Population in largest city in Switzerland was reported at 1443349 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Switzerland - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Switzerland Population in Largest City data was reported at 1,356,037.000 Person in 2017. This records an increase from the previous number of 1,341,453.000 Person for 2016. Switzerland Population in Largest City data is updated yearly, averaging 951,846.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,356,037.000 Person in 2017 and a record low of 535,471.000 Person in 1960. Switzerland Population in Largest City 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 in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Switzerland Population in Largest City: as % of Urban Population data was reported at 20.309 % in 2017. This records a decrease from the previous number of 20.328 % for 2016. Switzerland Population in Largest City: as % of Urban Population data is updated yearly, averaging 20.220 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 20.747 % in 2007 and a record low of 19.215 % in 1963. Switzerland Population in Largest City: as % of Urban Population 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: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

Population in the largest city (% of urban population) in Switzerland was reported at 21.5 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Switzerland - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This statistic illustrates the European cities in the DACH region (Germany, Austria and Switzerland), for their annual rental yields as of 2016. It can be seen that Berlin, in Germany, had the largest annual rental yield, with a return of 4.9 percent at that time. Frankfurt (Germany) and Linz (Austria) completed the top three, with annual rental yields of 4.1 percent and 3.4 percent respectively as of 2016.

About this dataset

Context

The dataset tabulates the population of Swiss town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Swiss town. The dataset can be utilized to understand the population distribution of Swiss town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Swiss town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Swiss town.

Key observations

Largest age group (population): Male # 65-69 years (46) | Female # 65-69 years (65). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Swiss town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Swiss town is shown in the following column.
• Population (Female): The female population in the Swiss town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Swiss town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town Population by Gender. You can refer the same here

The Swiss Städtekonferenz Mobilität (transl: City Conference on Mobility) publishes a "Städtevergleich Mobilität" (transl: City comparison on mobility) for the six biggest German-speaking Swiss cities (Basel, Bern, Luzern, St.Gallen, Winterthur and Zürich), every couple of years. It is based on combining federal data and data the cities collect themselves. The reports are published here: https://skm-cvm.ch/de/Info/Fakten/Stadtevergleich_Mobilitat

Every year, modal shares between cities are reported. Currently reported years are 2010, 2015, 2021. Modal shares are calculated as the percentages of the main mode of transport per trip ("Hauptverkehrsmittel pro Weg", p. 18 of report for 2021).

This repository provides a dataset, which is a manual transcription of modal shares reported on page 18 of the report for 2021 to make modal share data available in CSV format. The original report is attached as well. The repository contains the following files:

• modal_shares_staedtevergleich.csv: The modal shares dataset (CSV file)
• variables.csv: Variable descriptions for modal_shares_staedtevergleich.csv (CSV file)
• staedtevergleich_2021_231030.pdf: PDF report (original data source) retrieved on 15.5.2025 from https://skm-cvm.ch/de/Info/Fakten/Stadtevergleich_Mobilitat

Based on a wide variety of categories, the top major global smart cities were ranked using an index score, where a top index score of ** was possible. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, and expert perception. In more detail, the index also includes provision of smart parking and mobility, recycling rates, and blockchain ecosystem among other factors that can improve the standard of living. In 2019, Zurich, Switzerland was ranked first, achieving an overall index score of ****. Spending on smart city technology is projected to increase in the future.

Smart city applications Smart cities use data and digital technology to improve the quality of life, while changing the nature and economics of infrastructure. However, the definition of smart cities can vary widely and is based on the dynamic needs of a cities’ citizens. Mobility seems to be the most important smart city application for many cities, especially in European cities. For example, e-hailing services are available in most leading smart cities. The deployment of smart technologies that will incorporate mobility, utilities, health, security, and housing and community engagement will be important priorities in the future of smart cities.

The data collected on members of the local elites of the three largest city-regions (Basel, Geneva and Zurich) are integrated in the more general OBELIS database on Swiss Elites. Currently, the OBELIS database includes elites from four sectors at the national level: Economic, Political, Administrative and Academic (+ national sociability associations) and covers nine dates: 1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020. The elite status of individuals is defined by the position/function held in these four spheres at the date mentioned. A description of all the different samples of the Swiss elites can be consulted on the website. The data allows researchers to understand the elites through a relational analysis (network analysis) to highlight the interrelations between these elites. The data is also suitable to conduct prosopographical analysis. As for national elites, the identification of local elites of the three largest Swiss city-regions also follows a positional approach by selecting all individuals occupying leading positions in the major local economic, political, cultural and academic institutions for the 7 benchmark years: 1890, 1910, 1937, 1957, 1980, 2000 and 2020. For the economic sphere we collected information on all the committee members of the regional chambers of commerce as well as directors of the most important companies of the three cities’ leading economic sectors. This includes the major banks and insurance companies for the financial sector; for Basel, all the major textile (until 1937) and chemical-pharmaceutical companies; for Geneva, the major watch-making companies, as well as a few other industrial companies; and for Zurich, all the major companies from the machine industry. The total number of companies varies from 49 in 1890 to 35 in 2020. The smaller sample for the recent period is due to the strong concentration process in all economic sectors, involving mergers and acquisitions as well as bankruptcies. For these companies, all CEOs/general directors and directors’ board members were taken into account. For the political sphere, we included all members of the cantonal (regional) and local (city) parliaments and governments for Geneva and Zurich, whereas in Basel, where the city’s territory fully coincides with the canton, only the members of the cantonal parliament and government were considered. For the academic sphere we include all full and extraordinary (associate) professors of the three cities’ universities until 1957, and, for the more recent dates, a selection of professors according to the occupation of institutional positions or according to their scientific reputation. Finally, the committee members of the three cities’ fine art societies are included as urban elites from the cultural sphere.

About this dataset

Context

The dataset tabulates the Swiss town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Swiss town. The dataset can be utilized to understand the population distribution of Swiss town by age. For example, using this dataset, we can identify the largest age group in Swiss town.

Key observations

The largest age group in Swiss, Wisconsin was for the group of age 65 to 69 years years with a population of 111 (14.76%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Swiss, Wisconsin was the Under 5 years years with a population of 11 (1.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Swiss town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Swiss town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town Population by Age. You can refer the same here

The dataset contains the population of the resident population at the end of the corresponding month.

The resident population includes:- the permanent resident population at the main residence: all persons who are registered with their main residence in the city of St.Gallen and have Swiss citizenship or a foreign citizenship with a residence or settlement permit - the non-permanent foreign resident population: foreign nationals with a short-stay permit, temporarily admitted persons, persons in need of protection and applicants for asylum as far as they are registered with the municipal population control - persons with a secondary residence (so-called "weekly residents"): registered residents in the city of St.Gallen with a main residence elsewhere in Switzerland or abroad. A secondary residence is usually established in connection with a job or a visit to a training institution in the city of St.Gallen.

It is based on data from the Population Services of the City of St.Gallen (processed under the name "STADTSGPOP" by the Statistical Office).

The permanent resident population is the reference population for population statistics.The permanent resident population includes:All Swiss nationals having their main place of residence in SwitzerlandForeign nationals who have held a residence or permanent residence permit for a minimum of 12 months.The population evolves due to certain demographic movements (births, immigrations, deaths and emigrations). Since 2007, the average increase has been over 1%, making Switzerland one of the most dynamic countries in Europe in terms of population growth.

On 31 December 2022, Switzerland's permanent resident population was 8 815 400, i.e. 0.9% more than in 2021. This population growth was slightly higher than in previous years. At the same time, the population is getting increasingly older.The Population and Households Statistics are part of the surveys conducted within the framework of the Federal population census. The statistics provides information regarding population size and composition of the permanent resident population at the end of a year as well as population change during the same year.Features registered:Individuals: date of birth, gender, marital status, citizenship, place of residence, place of birth, place of previous residence, household composition.Foreign nationals: residence permit, duration of stay.For data protection reasons, absolute values ​​from 1 to 3 cannot be given in standard evaluations and are therefore indicated in this data set as a class with the value «3».The service is in the Swiss coordinate system CH1903+ LV95.

Many countries have some kind of energy-system transformation either planned or ongoing for various reasons, such as to curb carbon emissions or to compensate for the phasing out of nuclear energy. One important component of these transformations is the overall reduction in energy demand. It is generally acknowledged that the domestic sector represents a large share of total energy consumption in many countries. Increased energy efficiency is one factor that reduces energy demand, but behavioral approaches (known as “sufficiency”) and their respective interventions also play important roles. In this paper, we address citizens’ heterogeneity regarding both their current behaviors and their willingness to realize their sufficiency potentials—that is, to reduce their energy consumption through behavioral change. We collaborated with three Swiss cities for this study. A survey conducted in the three cities yielded thematic sets of energy-consumption behavior that various groups of participants rated differently. Using this data, we identified four groups of participants with different patterns of both current behaviors and sufficiency potentials. The paper discusses intervention types and addresses citizens’ heterogeneity and behaviors from a city-based perspective.

CH：最大城市人口：占城镇人口百分比在12-01-2017达20.309%，相较于12-01-2016的20.328%有所下降。CH：最大城市人口：占城镇人口百分比数据按年更新，12-01-1960至12-01-2017期间平均值为20.220%，共58份观测结果。该数据的历史最高值出现于12-01-2007，达20.747%，而历史最低值则出现于12-01-1963，为19.215%。CEIC提供的CH：最大城市人口：占城镇人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的瑞士 – 表 CH.世界银行：人口和城市化进程统计。

We established our study sites in the cities of Basel, Lugano and Zurich. We characterised urban tree cover of each city using a rectangular grid with squares of 100x100 m. Within each square, we measured the area covered by urban trees using the European Union's Copernicus Land Monitoring Service information, Urban Atlas Street Tree Layer 2018 https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c. Then we assigned each square to three categories of urban tree cover that roughly represented the main types of urban uses: 1) low cover, industrial/commercial areas, 0-20% tree cover; 2) intermediate cover, residential areas, 20-40% tree cover; 3) high cover, urban parks and cemeteries, 40-60% tree cover. -br/--br/- We measured bird predation rate on the non-native insect larvae of the horse chestnut leaf miner (HCLM) Cameraria ohridella, an invasive moth that lays eggs and completes its larval and pupal development stages within the leaves of the horse chestnut Aesculus hippocastanus. The larvae feed on leaf fluids and tissue while creating mines within the leaf, which ultimately results in early leaf browning and loss of photosynthetic activity. We collected roughly ten twigs from each tree, including five twigs from the inner and five from the outer crown. Each twig carried 2-4 leaves, for a total of 3408 leaflets. Leaves were stored for ~10 weeks in a refrigerated room at 4 degrees C. We checked all HCLM mines using a stereo microscope and distinguished among open and close mines. Open mines were further divided based on the opening hole, whether it was a small round exit hole of the larva or the parasitoid, or an irregularly shaped, large bird predation hole.

Lousonna: the activities and economy of an ancient town through an archaeological and digital study of its finds (Lausanne, Switzerland)

An SNSF-funded project (Swiss National Science Foundation) : Lousonna: activités et économie d'une ville antique par l'étude archéologique et numérique de ses mobiliers. - Intermediate repository for completed data. Main language: French

These datasets are the product of a research group funded by the SNSF. They include all the coins, glass objects and metallic or non-metallic artefacts known as small finds discovered during excavations in the town of Lousonna (ancient Lausanne, Switzerland) between 1850 and 2020. A table describing the contexts of discovery is included along with three tables dedicated to the finds. These data were produced between 2015 and 2023, using a common methodology for documenting archaeological finds. Photographs, technical drawings and geomatic projections are not included here, but can be requested from the authors. The data are produced by the authors directly from the artefacts. The interactive projections will be available according to FAIR principles on the ArkeoGIS platform by 2026. It can be considered as 99% complete. The remaining 1% will take years to complete. A second deposit will then be made. The research to which they relate is still in progress as part of three PhD: A. Crausaz : Small finds from Lousonna ; N. Consiglio : Coinage in Roman Settlements in Western Switzerland ; F. Pirrami : Roman Glass Artefacts from Vidy, Yverdon and Nyon. At the end of these three PhD (2026), all the raw data will again be uploaded and made available in a definitive form. If authorized by the public research bodies, the data will be made available under a public open licence.

About this dataset

Context

The dataset tabulates the Swiss town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Swiss town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Swiss town was 840, a 0.48% increase year-by-year from 2022. Previously, in 2022, Swiss town population was 836, an increase of 1.58% compared to a population of 823 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Swiss town increased by 38. In this period, the peak population was 840 in the year 2005. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Swiss town is shown in this column.
• Year on Year Change: This column displays the change in Swiss town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town Population by Year. You can refer the same here

Relative risks (RR) and 95% confidence interval (CI) of mortality associated with tropical nights (TNs) for each city from 1980–2018 controlling or not for mean temperature, and the average number of TNs city and the number of deaths. (XLSX)

About this dataset

Context

The dataset tabulates the population of Swiss town by race. It includes the population of Swiss town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Swiss town across relevant racial categories.

Key observations

The percent distribution of Swiss town population by race (across all racial categories recognized by the U.S. Census Bureau): 73.14% are white, 2.26% are Black or African American, 20.48% are American Indian and Alaska Native, 0.53% are some other race and 3.59% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Swiss town
• Population: The population of the racial category (excluding ethnicity) in the Swiss town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Swiss town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town Population by Race & Ethnicity. You can refer the same here