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;

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.; ;

Actual value and historical data chart for Switzerland Population In Largest City

About this dataset

Context

This list ranks the 2 cities in the Switzerland County, IN by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of cities in the Switzerland County, IN by their Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Switzerland County Other Race Population: This tells us how much of the entire Switzerland County, IN Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

View yearly updates and historical trends for Switzerland Population in the Largest City. Source: World Bank. Track economic data with YCharts analytics.

Settlement development of the largest Swiss cities. Map types: Symbols, Choropleths. Spatial extent: Switzerland. Time: before 1850 up to present

About this dataset

Context

This list ranks the 2 cities in the Switzerland County, IN by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Switzerland County, IN by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Switzerland County Black Population: This tells us how much of the entire Switzerland County, IN Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

About this dataset

Context

This list ranks the 2 cities in the Switzerland County, IN by Hispanic Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Pacific Islander Population: This column displays the rank of cities in the Switzerland County, IN by their Hispanic Native Hawaiian and Other Pacific Islander (NHPI) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Pacific Islander Population: The Hispanic Pacific Islander population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Pacific Islander. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Switzerland County Hispanic Pacific Islander Population: This tells us how much of the entire Switzerland County, IN Hispanic Pacific Islander population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

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.

Techsalerator’s Location Sentiment Data for Switzerland

Techsalerator’s Location Sentiment Data for Switzerland provides valuable insights into public sentiment, environmental mood, and acoustic trends across various Swiss locations. This dataset is essential for businesses, researchers, policymakers, and technology developers looking to understand the evolving sentiments and soundscapes in the Swiss context.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Switzerland

Techsalerator’s Location Sentiment Data for Switzerland delivers a comprehensive analysis of emotional sentiment and environmental sounds across urban, rural, and scenic locations. This data is crucial for urban development, environmental monitoring, social studies, and AI-based sentiment analysis applications.

Top 5 Key Data Fields

• Location of Data Capture – Identifies the geographic region where the sentiment and sound data was collected, aiding in regional sentiment analysis.
• Sentiment Score – Provides an aggregated sentiment score (positive, negative, or neutral) for each recorded location based on local interactions and public opinions.
• Audio Frequency Spectrum – Analyzes sound frequencies present in various environments, supporting studies on urban noise, nature sounds, and public health.
• Decibel Levels (dB) – Measures sound intensity in different locations, helping in noise pollution studies and assessing the impact of sound on the Swiss population.
• Time of Capture – Captures the exact time and date of data collection, allowing for analysis of sentiment and soundscape variations between day and night.

Top 5 Location Sentiment Trends in Switzerland

• Urban Sentiment Shifts – Increased urbanization and tourism in cities like Zurich and Geneva are contributing to shifts in public sentiment, impacting local business and social dynamics.
• Tourism and Regional Sentiment – The tourist influx in locations such as Interlaken and Lucerne has influenced sentiment patterns, especially in terms of local hospitality and cultural identity.
• Mountain Soundscapes – In alpine areas, there is a growing interest in environmental acoustics, including the study of wildlife and natural sound environments in the Swiss Alps.
• Swiss Political Sentiment – Political movements and national elections have an observable impact on sentiment in major cities, helping analysts understand voter behavior and public opinion trends.
• Climate Change Awareness – Sentiment around climate change is rising, with increasing attention on environmental concerns in areas like Geneva and the Swiss Jura.

Top 5 Applications of Location Sentiment Data in Switzerland

• Urban Planning and Smart Cities – Understanding sentiment and noise levels helps urban planners create more harmonious and people-centered cities.
• Tourism and Hospitality Industry – Analyzing regional sentiment helps businesses tailor services to meet customer expectations and improve local experiences.
• Environmental and Climate Research – Sentiment and acoustic data aid in understanding public awareness and concerns regarding climate change and conservation efforts.
• Public Policy and Government – Local sentiment data provides valuable input for policymakers in creating programs that align with the concerns of Swiss citizens.
• AI and Social Media Analysis – Enhancing sentiment analysis tools with localized data for better understanding of public moods and behaviors on digital platforms.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Switzerland, contact info@techsalerator.com with your specific requirements. Techsalerator offers customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Location of Data Capture
• Sentiment Score (Positive, Negative, Neutral)
• Audio Frequency Spectrum
• Decibel Levels (dB)
• Time of Capture
• Sentiment Analysis (Mood Patterns)
• Noise Pollution Analysis
• Cultural Sentiment
• Tourism Impact
• Climate Sentiment

Techsalerator’s Location Sentiment Data for Switzerland offers a comprehensive view of public sentiment and sound data, providing critical insights for businesses, environmental researchers, urban developers, and policymakers across the country.

In 2023, the average rent for apartments and houses in Switzerland peaked at ** Swiss francs per square meter. That was an increase from ***** Swiss francs per square meter in 2022 and the largest rise since 2017. In 2023, Zurich and Geneva were the Swiss cities with the highest rents.

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.

About this dataset

Context

This list ranks the 2 cities in the Switzerland County, IN by Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Asian Population: This column displays the rank of cities in the Switzerland County, IN by their Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Asian Population: The Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Switzerland County Asian Population: This tells us how much of the entire Switzerland County, IN Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

This vector map provides a detailed reference layer for Switzerland in the local projection system CH1903+ LV95 designed to be overlaid on imagery. This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, buildings, and administrative boundaries. This vector tile layer provides unique capabilities for customization and high-resolution display. The layers in this map are built using the same data sources used for the LV95 Swiss Topographic: swissTLM3D, swissBOUNDARIES3D, and swissTLMRegio provided by swisstopo.Use this Map This map is designed to be used as a reference layer in a web map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map. Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers. See the Switzerland Vector Basemaps group for other vector web maps. For details on how to customize this map, please refer to this article.DataThe source data can be downloaded from swisstopo's website.Data vintage: March 2025. The service is updated annually.

This vector layer provides a detailed vector basemap for Switzerland in the local projection system CH1903+ LV95 featuring a classic Esri topographic map style. Layer designed for use with a Hillhade relief for added context. This vector tile layer provides unique capabilities for customization and high-resolution display.This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, administrative boundaries, and shaded relief for added context. It is built on the datasets swissTLM3D, swissBOUNDARIES3D, and swissTLMRegio, and Swiss Map Vector 10 provided by swisstopo and is enhanced with owner parcels, roads and sidewalks provided by following cantons: AargauLV95 Swiss Topographic Map - Overview, Appenzell I.Rh., Basel-Landschaft, Basel-Stadt, Bern, Fribourg, Genève, Glarus, Graubünden, Schaffhausen, Schwyz, Solothurn, St. Gallen, Thurgau, Ticino, Uri, Valais, Zug and Zürich.This is a multisource map style. This layer also includes vector contour lines. Even though there are two source paths in the layer's json, these are referenced from a single vector tile layer in this web map. The root.json style file calls two vector Hosted Tile Layers to display all the data in the map. One source (esri) contains all the basemap tiles for this layer. The other source (contours) contains all the contour lines. Use the Map Viewer (not Classic) to view all the features in this layer as intended.Use this MapThis map is designed to be used as a basemap layer or reference layer in a web map. You can add this layer to a web map and save as your own map. If you would like to use this map as a basemap layer in a web map, you may use the vector basemap LV95 Swiss Topographic (with Contours and Hillshade) web map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers. For details on how to customize this map, please refer to this article.DataThe source data can be downloaded from swisstopo's website and geodienste.ch.Data vintage: March 2025. The service is updated annually.Data vintage Contours: 2017

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.

In this paper we explore how policy discourses on urban sustainability impact the governing of urban food gardening in favoring community gardens. Our main hypothesis is that community gardens better accommodate the tensions created by the discourses of the compact and green city compared to other types of food gardening, especially allotment gardens. In the context of the Swiss cities of Lausanne and Zurich, analysis of policy documents confirms this hypothesis by identifying four frames that orient policies toward favoring community gardening: (i) Adapting green space planning to densification favors community gardening with their modest, flexible and multifunctional design, (ii) Revaluating the role of urban food gardening in urban sustainability represents community gardening as a new multifunctional benchmark, (iii) Reorganizing urban food gardening fosters diversity in gardening opportunities which in turn supports a variety of forms of community gardening, (iv) Justifying urban food gardening through public values and needs supports community gardening with their cost-efficient green space management, lower land management and more active citizen participation. In this vein, urban policymakers continually turn to community gardens as a strategic urban planning tool that gives urban green space greater legitimacy in the wake of the densifying city. Overall, urban food gardens continue to be negotiated between space-related marginalization and socio-political significance serving different needs to urban citizens. This results in the need of a more sophisticated planning approach considering different types of urban gardens related to their location in the built city, associated functions, and user groups.

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。

The Swiss city of Zurich ranks among the most expensive European cities for a Starbucks coffee. In 2016, the average cost of a Latte Grande from Starbucks coffeehouses in Zurich was 6.33 euros. In London, UK the same drink cost less than half the price, averaging 3.09 euros.

Coffee shop prices across Europe

Coffee prices fluctuate across different markets in Europe, largely due to varying economies. Another coffee price index, for example, ranked the high-income Switzerland and Nordic countries as having the highest prices for a cappuccino in European restaurants. The coffeehouse landscape also varies by country, with the presence of coffee chains high in some markets, while other independent cafés are the more prominent choice for local coffee consumption.

Starbucks in Europe

The U.S. coffeehouse chain Starbucks is a major player in Europe, ranking as the second leading coffee shop chain based on number of stores. The brand is present across all major European markets, including Russia and Turkey. The largest number of Starbucks stores in Europe, however, are based in the UK.

This statistic illustrates the hotel occupancy rate in Zurich in Switzerland from 2011 to 2017, with forecast figures for 2018 and 2019. The occupancy rate of hotels in Zurich was measured at ** percent in 2017 and was forecast to decrease to ** percent in 2018 and increase by *** percent to ** percent in 2019. Average daily room rates was predicted to decrease and revenue per available room in Zurich was predicted to remain unchanged in 2018 and 2019. Comparing occupancy rates with other European city destinations, hotel performance in Zurich ranked higher than the second biggest city in Switzerland, Geneva