How many incorporated places are registered in the U.S.?

There were 19,502 incorporated places registered in the United States as of July 31, 2019. 16,410 had a population under 10,000 while, in contrast, only 10 cities had a population of one million or more.

Small-town America

Suffice it to say, almost nothing is more idealized in the American imagination than small-town America. When asked where they would prefer to live, 30 percent of Americans reported that they would prefer to live in a small town. Americans tend to prefer small-town living due to a perceived slower pace of life, close-knit communities, and a more affordable cost of living when compared to large cities.

An increasing population

Despite a preference for small-town life, metropolitan areas in the U.S. still see high population figures, with the New York, Los Angeles, and Chicago metro areas being the most populous in the country. Metro and state populations are projected to increase by 2040, so while some may move to small towns to escape city living, those small towns may become more crowded in the upcoming decades.

This link contains downloadable data for the Atlas of Rural and Small-Town America which provides statistics by broad categories of socioeconomic factors: People: Demographic data from the American Community Survey (ACS), including age, race and ethnicity, migration and immigration, education, household size, and family composition. Jobs: Economic data from the Bureau of Labor Statistics and other sources, including information on employment trends, unemployment, and industrial composition of employment from the ACS. County classifications: Categorical variables including the rural-urban continuum codes, economic dependence codes, persistent poverty, persistent child poverty, population loss, onshore oil/natural gas counties, and other ERS county typology codes. Income: Data on median household income, per capita income, and poverty (including child poverty). Veterans: Data on veterans, including service period, education, unemployment, income, and other demographic characteristics.

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Population in small towns by region, table content and every 5 years

This Alberta Official Statistic describes the percent change in Alberta’s population between 1986 and 2011 by 5-year census cycles. The population is divided into "Larger Urban Centres" and Rural and Small Town areas. Within rural Alberta, the population is divided into four categories with each category consecutively representing lesser integration with urban economies. The four categories are called Metropolitan Influenced Zones (MIZ) and capture urban integration based on the percent of the working population commuting to urban centers. The categories are: Strong MIZ (where 30% or more of the workforce commutes to an urban core) Moderate MIZ (where 5% to 29% commute to any urban core) Weak MIZ (where greater than 0% but less than 5% commute to any urban core) No MIZ (where there are no residents commuting to an urban core)

As of 2019, most rural inhabitants in Africa resided close to small and mid-sized towns. The nearest city to almost ** percent of the rural population had between 10,000 and ****** inhabitants. Smaller shares of rural households, on the other hand, lived closer to larger urban areas. As of the same year, roughly half of the rural residents lived within ** kilometers from a city.

About this dataset

Context

The dataset tabulates the Little Falls 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 Little Falls 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 Little Falls town was 1,473, a 0.27% decrease year-by-year from 2022. Previously, in 2022, Little Falls town population was 1,477, a decline of 1.07% compared to a population of 1,493 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Little Falls town decreased by 69. In this period, the peak population was 1,584 in the year 2007. 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 Little Falls town is shown in this column.
• Year on Year Change: This column displays the change in Little Falls 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 Little Falls town Population by Year. You can refer the same here

A dataset listing New York cities by population for 2024.

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About this dataset

Context

The dataset tabulates the Little Falls 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 Little Falls 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 2022, the population of Little Falls town was 1,485, a 0.80% decrease year-by-year from 2021. Previously, in 2021, Little Falls town population was 1,497, a decline of 0.00% compared to a population of 1,497 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Little Falls town decreased by 57. In this period, the peak population was 1,584 in the year 2007. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Little Falls town is shown in this column.
• Year on Year Change: This column displays the change in Little Falls 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 Little Falls town Population by Year. You can refer the same here

A dataset listing South Dakota cities by population for 2024.

This data layer produced by the National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program provides a geographic locale framework that classifies all U.S. territory into twelve categories ranging from Large Cities to Remote Rural areas. NCES uses this framework to describe the type of geographic area where schools and school districts are located. The criteria for these classifications are defined by NCES, but they rely on standard geographic areas developed and maintained by the U.S. Census Bureau. The 2016 NCES Locale boundaries are based on geographic areas represented in Census TIGER/Line 2016. The NCES Education Demographic and Geographic Estimate (EDGE) program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to annually update the locale boundaries. For more information about the NCES locale framework, and to download the data, see: https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries. The classifications include:Large City (11): Territory inside an Urbanized Area and inside a Principal City with population of 250,000 or more.Midsize City (12): Territory inside an Urbanized Area and inside a Principal City with population less than 250,000 and greater than or equal to 100,000.Small City (13): Territory inside an Urbanized Area and inside a Principal City with population less than 100,000.Suburb – Large (21): Territory outside a Principal City and inside an Urbanized Area with population of 250,000 or more.Suburb - Midsize (22): Territory outside a Principal City and inside an Urbanized Area with population less than 250,000 and greater than or equal to 100,000.Suburb - Small (23): Territory outside a Principal City and inside an Urbanized Area with population less than 100,000.Town - Fringe (31): Territory inside an Urban Cluster that is less than or equal to 10 miles from an Urbanized Area.Town - Distant (32): Territory inside an Urban Cluster that is more than 10 miles and less than or equal to 35 miles from an Urbanized Area.Town - Remote (33): Territory inside an Urban Cluster that is more than 35 miles of an Urbanized Area.Rural - Fringe (41): Census-defined rural territory that is less than or equal to 5 miles from an Urbanized Area, as well as rural territory that is less than or equal to 2.5 miles from an Urban Cluster.Rural - Distant (42): Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an Urbanized Area, as well as rural territory that is more than 2.5 miles but less than or equal to 10 miles from an Urban Cluster.Rural - Remote (43): Census-defined rural territory that is more than 25 miles from an Urbanized Area and is also more than 10 miles from an Urban Cluster.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

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

A dataset listing Washington cities by population for 2024.

About this dataset

Context

The dataset tabulates the population of Little Compton town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Little Compton town. The dataset can be utilized to understand the population distribution of Little Compton town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Little Compton 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 Little Compton town.

Key observations

Largest age group (population): Male # 60-64 years (211) | Female # 65-69 years (242). 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 Little Compton town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Little Compton town is shown in the following column.
• Population (Female): The female population in the Little Compton 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 Little Compton 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 Little Compton town Population by Gender. You can refer the same here

This Alberta Official Statistic describes the percentage of the population that reported having an Aboriginal identity in 2011. The population is divided into larger urban centres and rural and small town areas. Within the larger urban centres, the population is divided between Census Metropolitan Areas (CMA) and two different sizes of Census Agglomerations (CA). Within rural and small town Alberta, the population is divided into four categories with each category consecutively representing less integration with urban economies. The four categories are called Metropolitan Influence Zones (MIZ) and capture urban integration by measuring the percentage of the working population commuting to urban centers. The categories are: Strong MIZ (where 30% to 49% of the workforce commutes to an urban core) Moderate MIZ (where 5% to 29% commute to an urban core) Weak MIZ (where 1% to 4% commute to an urban core) No MIZ (where there are no residents commuting to an urban core)

A dataset listing Montana cities by population for 2024.

For the past ten years, the growth debate has been urban-centric, at least in Germany, while rural areas have been largely associated with depopulation. Despite their importance, small towns fall into a systematic perception gap in scientific and planning discourses. Against this background, this paper applies a threefold conceptualization of scalar relations paying analytical attention to (1) population trajectories, (2) the relation of spatial proximity and development, and (3) the influence of international migration on population development. Covering the period 1961–2018, this paper shows that despite a current demographic respite due to increased international migration, many small towns will continue to face the long-term consequences of population decline. This is accompanied by an increasing spatial differentiation of population growth rates. The relation between proximity to large centres and population growth is weakening, giving rise to other factors, e.g. residential amenities and competition between small and medium-sized towns. Based on this quantitative assessment, we conclude that small towns are the most dynamic settlement type in Germany and, at the same time, extremely heterogeneous in terms of trajectories and underlying driving factors. We also discuss conceptual aspects regarding process-understanding, terms, categories, and tools to analytically grasp the complexity of small towns.

Small towns are concentrated settlements with 50-199 inhabitants. Statistics Sweden develops boundaries for small towns using register data, such as Statistics Sweden’s total population register (RTB) and Lantmäteriet’s real estate map and coordinated building registers. Open data is displayed as polygons and by 2015 small towns have been updated every five years. Since 2016, they are updated every three years.

A dataset listing Tennessee cities by population for 2024.