The Vatican City, often called the Holy See, has the smallest population worldwide, with only *** inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populous country in the world, with over *** billion inhabitants.

A dataset listing Florida cities by population for 2024.

A dataset listing Illinois cities by population for 2024.

A dataset listing Georgia cities by population for 2024.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

A dataset listing Arkansas cities by population for 2024.

A dataset listing Alabama cities by population for 2024.

This dataset illustrates the largest difference between high and low temperatures and the smallest difference between high and low temperatures in cities with 50,000 people or more. A value of -1 means that the data was not applicable. Also included are the rankings, the inverse ranking to be used for mapping purposes, the popualtion, the name of city and state, and the temperature degree difference. Source City-Data URL http//www.city-data.com/top2/c489.html http//www.city-data.com/top2/c490.html Date Accessed November 13,2007

About this dataset

Context

The dataset tabulates the Blue Earth City township 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 Blue Earth City township. The dataset can be utilized to understand the population distribution of Blue Earth City township by age. For example, using this dataset, we can identify the largest age group in Blue Earth City township.

Key observations

The largest age group in Blue Earth City Township, Minnesota was for the group of age 65 to 69 years years with a population of 56 (10.69%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Blue Earth City Township, Minnesota was the 50 to 54 years years with a population of 4 (0.76%). 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 Blue Earth City township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Blue Earth City township 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 Blue Earth City township Population by Age. You can refer the same here

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The urban–rural continuum classifies the global population, allocating rural populations around differently-sized cities. The classification is based on four dimensions: population distribution, population density, urban center location, and travel time to urban centers, all of which can be mapped globally and consistently and then aggregated as administrative unit statistics.Using spatial data, we matched all rural locations to their urban center of reference based on the time needed to reach these urban centers. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of “reference” for a given rural location: proximity to a larger center “dominates” over a smaller one in the same travel time category. This was done for 7 urban categories and then aggregated, for presentation purposes, into “large cities” (over 1 million people), “intermediate cities” (250,000 –1 million), and “small cities and towns” (20,000–250,000).Finally, to reflect the diversity of population density across the urban–rural continuum, we distinguished between high-density rural areas with over 1,500 inhabitants per km2 and lower density areas. Unlike traditional functional area approaches, our approach does not define urban catchment areas by using thresholds, such as proportion of people commuting; instead, these emerge endogenously from our urban hierarchy and by calculating the shortest travel time.Urban-Rural Catchment Areas (URCA).tif is a raster dataset of the 30 urban–rural continuum categories for the urban–rural continuum showing the catchment areas around cities and towns of different sizes. Each rural pixel is assigned to one defined travel time category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban agglomeration sizes. The agglomerations range from large cities with i) populations greater than 5 million and ii) between 1 to 5 million; intermediate cities with iii) 500,000 to 1 million and iv) 250,000 to 500,000 inhabitants; small cities with populations v) between 100,000 and 250,000 and vi) between 50,000 and 100,000; and vii) towns of between 20,000 and 50,000 people. The remaining pixels that are more than 3 hours away from any urban agglomeration of at least 20,000 people are considered as either hinterland or dispersed towns being that they are not gravitating around any urban agglomeration. The raster also allows for visualizing a simplified continuum created by grouping the seven urban agglomerations into 4 categories.Urban-Rural Catchment Areas (URCA).tif is in GeoTIFF format, band interleaved with LZW compression, suitable for use in Geographic Information Systems and statistical packages. The data type is byte, with pixel values ranging from 1 to 30. The no data value is 128. It has a spatial resolution of 30 arc seconds, which is approximately 1km at the equator. The spatial reference system (projection) is EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long). The geographic extent is 83.6N - 60S / 180E - 180W. The same tif file is also available as an ESRI ArcMap MapPackage Urban-Rural Catchment Areas.mpkFurther details are in the ReadMe_data_description.docx

U.S. Census Bureau QuickFacts statistics for Globe city, Arizona. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

Urban centres shape societies, but there is no systematic global approach analysing how countries are organized around multiple urban centres. We advance understanding by delineating 6,100 city–regions worldwide using a novel framework classifying 30,000 urban centres into four tiers and mapping their nested catchment areas based on travel time accessibility. Our results show extensive interconnectedness among urban centres and with their surrounding areas, with 3.2 billion people having physical access to multiple tiers with 1-hour travel time, rising to 4.7 billion for 3-hours travel time. Importantly, among people living in or near towns and small cities, access to intermediate cities is far greater than to large cities. This highlights the essential role intermediate cities play in engaging surrounding populations. For the first time, city–regions around the world are identified systematically, showing great diversity in how societies are organized across urban tiers, depending on geography and national income. The associated spatial dataset is a powerful tool for regional planning, economic development, and natural resource management.

This data represents the Oregon City Limit boundaries. Each city limit is defined as a continuous area within the statutory boundary of an incorporated city, which is the smallest subdivision of an annexed area. It is represented as spatial data (polygon with label point). Purpose: The use of city limits information was identified as a need for general planning purposes within ODOT. It was determined that this would be a frequently used data set that needed to be both spatially referenced and attributed in a GIS base layer. The decision was made to create a statewide coverage of the boundary outlining the city limits for each of the 242 incorporated cities. An incorporated city may have multiple areas that are not contiguous. Each such area is represented separately with its own polygon. The area of the city limits will be calculated from the polygons created. For assessment and taxation purposes, the boundary change process has two key dates. One is the effective date of the boundary change. The other is the filing deadline with the Department of Revenue. While both of these dates relate to boundary changes, they operate independently. The two key dates are March 31 and July 1. These dates help determine the property affected by a boundary change. The district must file its boundary change documents in final approved form to the Department of Revenue Cadastral Information Systems Unit by March 31 and obtain a notice of approval. In order for a district to extend its tax rate to an annexed territory, the district?s annexation must be effective on or before July 1.

Abstract: A continuous area within the statutory boundary of an incorporated city, which is the smallest subdivision of an annexed area. It is represented as spatial data (polygon with label point). The use of city limits information was identified as a need for general planning purposes within ODOT. It was determined that this would be a frequently used data set that needed to be both spatially referenced and attributed in a GIS base layer. The decision was made to create a statewide coverage of the boundary outlining the city limits for each of the 241 incorporated cities. An incorporated city may have multiple areas that are not contiguous. Each such area is represented separately with its own polygon. The area of the city limits will be calculated from the polygons created.

The lowest rural population rates are found in some of the smallest countries in the world and city-states and areas, such as Gibraltar, Monaco, and Singapore, where the whole population lives in urban areas. Apart from these, Qatar is the country with the lowest rural population rate in the world. There, less than one percent of the population lives in rural areas. Belgium follows behind Qatar with less than two percent living in rural areas. On the other hand, Papua New Guinea has the largest rural population in the world.

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2005 Belarus Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Belarus - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Belarus and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was carried out by the Ministry of Statistics and Analysis of the Republic of Belarus, and Research Institute of Statistics of the Ministry of Statistics and Analysis of the Republic of Belarus with the support and assistance of UNICEF and Ministry of Health. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Belarus.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV/AIDS. The population covered by the 2005 MICS is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child.

The 2005 MICS collected data from a nationally representative sample of households, women and children. The primary focus of the 2005 MICS was to provide estimates of key population and health, education, child protection and HIV related indicators for the country as a whole, and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 7 regions for key indicators. Belarus is divided into 7 regions. Each region is subdivided into big cities, small towns and rural areas (selskie sovety). In addition each unit was subdivided into polling stations in urban areas and rural settlements in selskie sovety. In total Belarus includes 20 big cities, 187 small cities and 1388 selskie soveties.

MICS3 is utilizing the sample frame of household surveys that is being used in the republic. To provide uniform distribution of the sample allocation of the households in the republic the selection was carried out in Brest, Vitebsk, Gomel, Grodno, Minsk, Mogilev regions and in Minsk city.

Three stage sampling has been carried out. At the first stage in each of the regions (oblasts) three sampling strata has been created: big cities, small towns and rural areas (selskie sovety); at the second stage - polling stations in urban areas and rural settlements in selskie sovety; at the third stage in the selected settlements the households were selected. Within the strata of big cities, at first stage, 20 big cities were selected with the probability equalling to 1. Within the strata of small towns 29 small towns were sampled systematically with pps and the measure of size was total population of the small towns. The number of small towns in every region (oblast) was selected based on division of the total number of population of all small towns of each region into average household size (2,6), sample share (1/600) and average load of interviewer (40).

Within the strata of rural settlements (selskie sovety) at the first stage of sampling 53 rural settlements were selected systematically with pps and the measure of size was number of households in the rural settlement.

On the second stage of sampling within the big cities and the small towns the polling stations were selected as sampling unit, in the rural settlements - settlements in rural area (selskie sovety).

To cover the whole territory of the selected city the cartographical materials were used on the second stage of sampling within the big cities. The number of the polling stations was calculated based on division of the population of the city into the average size of the family (2,6), sample share (1/600) and estimated number of the households in each polling station (20).

Three polling stations were selected in each small town from the list of the polling stations, ranking by number of voters. In rural areas, taking into account the difficulty of access and scattered nature of settlements, the territories of the rural areas (selskie sovety) were divided into zones and the closest rural settlements were grouped. One zone was selected in each rural area (selskie sovety) and within this zone all settlements were investigated.

Throughout the Republic of Belarus there were 304 polling stations and the rural zones in selskie sovery selected in 2005.

On the third stage of sampling, households were selected from the updated lists systematically taking into account the size of the cluster. In big cities the size of the cluster which is selected from the updated list households within the territory of polling station is 19-20 households, in small towns the size of the cluster is 13-14 households, and in rural areas the size of the cluster is 39-40 households.The size of clusters is not uniform. Variation in cluster sizes for urban and rural settlements was done on purpose since existing sampling plan was considering load of one interviewer, as one of the parameters, and distribution of sampled population into the sampling domains - proportionally to the distribution in general population.

Besides, taking into account the limited representation of children under 5 in the household sample, the additional sub-sample of households with children aged 0-4 was formed. For this purpose, in each of the 304 clusters the lists of households was updated with the information on households with under 5 children through local out-patient health institutions. From these lists with higher probability then for households without children, the households with children aged 0-4 were selected.

The resulting number of households for MICS3 sample in the Republic of Belarus was 7,000, including 2,857 households with children aged 0-4.

Following standard MICS data collection rules, if a household was actually more than one household when visited, then a) if the selected household contained two households, both were interviewed, or b) if the selected household contained 3 or more households, then only the household of the person named as the head was interviewed.

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Belarus MICS were structured questionnaires based on the MICS3 Model Questionnaire. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household listing, education, water and sanitation, household characteristics, child labour, and child discipline.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or

Nigeria is the African country with the largest population, counting over 230 million people. As of 2024, the largest city in Nigeria was Lagos, which is also the largest city in sub-Saharan Africa in terms of population size. The city counts more than nine million inhabitants, whereas Kano, the second most populous city, registers around 3.6 million inhabitants. Lagos is the main financial, cultural, and educational center in the country. Where Africa’s urban population is booming The metropolitan area of Lagos is also among the largest urban agglomerations in the world. Besides Lagos, another most populated citiy in Africa is Cairo, in Egypt. However, Africa’s urban population is booming in other relatively smaller cities. For instance, the population of Bujumbura, in Burundi, could grow by 123 percent between 2020 and 2035, making it the fastest growing city in Africa and likely in the world. Similarly, Zinder, in Niger, could reach over one million inhabitants by 2035, the second fastest growing city. Demographic urban shift More than half of the world’s population lives in urban areas. In the next decades, this will increase, especially in Africa and Asia. In 2020, over 80 percent of the population in Northern America was living in urban areas, the highest share in the world. In Africa, the degree of urbanization was about 40 percent, the lowest among all continents. Meeting the needs of a fast-growing population can be a challenge, especially in low-income countries. Therefore, there will be a growing necessity to implement policies to sustainably improve people’s lives in rural and urban areas.