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.

A May 2024 study analyzed the small towns in Italy with a population of under five thousand with the highest average monthly number of Google searches in 2023. Based on the analysis, two Sicilian destinations, Favignana and San Vito Lo Capo, recorded the highest figure, each with an average of 91,890 monthly Google searches in 2023. Portofino in Liguria followed in the ranking, with 91,330 monthly Google searches on average that year.

Less than 25,000 people. Average is population weighted average of monitoring sites in small towns. Note: PM10 concentrations are given in micrograms per cubic metre of air, or µg/m3. Source: Regional councils of Northland, Waikato, Manawatu-Wanganui, Wellington, West Coast, Canterbury, Otago, Southland;

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.

The Vatican City, often called the Holy See, has the smallest population worldwide, with only 496 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 populated country in the world, with over 1.4 billion inhabitants.

About this dataset

Context

The dataset tabulates the Little Valley town median household income by race. The dataset can be utilized to understand the racial distribution of Little Valley town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Little Valley Town, New York median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Little Valley Town, New York (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Little Valley town median household income by race. You can refer the same here

A dataset listing Maine cities by population for 2024.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Little Falls Town, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,960, while the mean income for the highest quintile (20% of households with the highest income) is 251,798. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 424,298, which is 168.51% higher compared to the highest quintile, and 1932.14% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 median household income. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Little Valley town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Little Valley town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 91.05% of the total residents in Little Valley town. Notably, the median household income for White households is $55,050. Interestingly, despite the White population being the most populous, it is worth noting that American Indian and Alaska Native households actually reports the highest median household income, with a median income of $114,167. This reveals that, while Whites may be the most numerous in Little Valley town, American Indian and Alaska Native households experience greater economic prosperity in terms of median household income.

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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Little Valley town.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Valley town median household income by race. You can refer the same here

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

A dataset listing Iowa cities by population for 2024.

U.S. Census Bureau QuickFacts statistics for Ossining town, Westchester County, New York. 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.

In the beginning of the 18th century Duisburg was a small university town with almost 3000 inhabitants and still heavily dominated by agriculture. In addition, crafts and trade, in particular textile industry, were other important branches of industry. Politically and economically Duisburg was dominated by a reformed confession Catholic and Lutheran minority confessions,The study deals with the serious demographic and social changes within the town. Main topic of this works is the presentation of the population development of Duisburg and its social structures in the 18th and the beginning of the 19th century based on church records. Besides the empirical statistical evaluation, the numbers are set in relation to the political/legal and religious circumstances of this time and space. Using the data from the censuses from 1714 and 1811 as well as other fiscally motivated surveys, socio-professional shift models of Duisburg´s population of the beginning and the end of the investigation period are developed and connected with the economic background. This is related to the question, in how far religious affiliation influenced population behavior (birth rates, marriages, mortal rates) and the migration behavior. The local Duisburg results are also placed in a wider context to bridge the gap between the micro and macro historical perspective. The entire data base of this study is archived in form of DBASE- files structured by topics. In Histat you only find small parts of the entire data set (only time series). Data tables in Histat:01. Duisburg´s population in the 18th and the early 19th (1714-1815)02. Births in five year averages (1713-1814)03. Model of Duisburg´s population development (1714-1812)04. Vital statistics on Duisburg´s three confessional groups and the total population (1713-1814)05. Duisburg´s prices for wheat, rye, barley and buckwheat in Reichstaler, silver pennies (Silbergroschen) and pennies (Pfennige) per Berlin bushels (Berliner Scheffel) (1692-1782)

A dataset listing West Virginia cities by population for 2024.

Abstract

The project uses public opinion polling to gather and then analyze a sample that represents the entire population of each of four different countries of Central Asia: Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan.

Geographic coverage

The project uses public opinion polling to gather and then analyze a sample that represents the entire population of the country.

Kind of data

Sample survey data [ssd]

Sampling procedure

For all four Central Asian countries in this survey, the sampling procedure is a three-stage stratified clustered one. Census data on the territorial dispersion of the population is used as the base to start the sampling methodology. The sampling procedure takes the total population of the country, considers geographic units within the country as either urban or rural, and then develops random procedures to select who to survey in three stages: first by randomly selected smaller geographic urban and units in each province (the primary sampling units or PSUs), second randomly chosing households within these units, and third, to randomly select which household member to interview in each household.

The sampling frame used to divide these four countries into smaller geographic units to randomly sample from differs slightly for each Central Asian country, based on differences in data availability on the population of the country and its dispersion. Subsequent sections explain the sampling methodology used and how this sampling frame differs in each country. Then all four countries have PSUs, random selection of households, and random sampling of individuals within households using the same methods.

Uzbekistan has 12 provinces, the Republic of Karakalpakstan, and the city of Tashkent. Each province has several districts for a total of 168 districts in the country. Each district has a number of cities, small towns and villages. Of the 233 cities and small towns in Uzbekistan, 76 cities are subordinated directly to provinces due to their importance. The population of Uzbekistan was 25,523,000 people, of which 9,410,700 (37%) were urban residents, and the 16,112,300 (63%) were rural residents as of May 2002. Several districts, practically inaccessible from an absence of transportation or remote location, are excluded from the sampling frame. These two cities, one small town, and one district in Navoi have a population of 95,300, 0.9% of the urban population and 0.1% of the rural population of the country - a total of 0.4% of the population of Uzbekistan is excluded from the sampling frame.

The sampling frame for Uzbekistan has primary sampling units (PSUs) of two types: - MK ("Mahallinskiy Komitet") - town makhalla committee. Makhallas are the traditional neighborhood committees which have been revived (and in some urban areas artificially created) by the Uzbek government; - SSG ("Selskiy Skhod Grazhdan") - village council. This type has been used for rural areas in all recent surveys.

The sampling scheme then has the following three standard stages: - proportionate stratification by population of provinces; - for all provinces (include Tashkent city as urban stratum): - proportionate stratification by urban/rural population within provinces; - PPS-sampling of PSUs within urban/rural strata; - sequential random sampling of households (Secondary Sampling Units - SSUs) in selected PSUs; - Kish grid based sampling of respondents. Thus, the sampling is three-stage stratified clustered sampling.

There are 63 PSUs are selected from the sampling frames, with the number of respondents to be interviewed in each varying between 17 and 29 in different PSUs.

The sample distribution by the main demographic characteristics can be compared with data of Statistical Department of Republic of Uzbekistan from January 1, 2002.

Mode of data collection

Face-to-face [f2f]

Research instrument

To perform questioning, the following documents have been prepared (attached): - Questionnaire (in Uzbek, in Russian and in Karakalpak languages). - Sets of cards (in Uzbek, in Russian and in Karakalpak languages). - Forms of the respondent's sampling and records of the households' visits with Kish's cards (in Russian and Uzbek languages). - Forms of the households' sampling in selected points of questioning (in Russian language). - Sampling instructions (in Russian and Uzbek languages). - Instructions on households and respondents' sampling (in Russian and Uzbek languages). - Examples how to fill in sampling forms - Covering letter to local authorities of 2 types (in Cyrillic and in Latin).

Response rate

During the fieldwork, 766 cases of non-response were registered (non-eligible units are excluded from this count). The average response rate is about 66% (1,500 of 2,266 attempts). Generally, the non-response case was registered if an interviewer had made up to two failed callbacks. the response rate in rural areas is higher than in urban areas. In Tashkent city very much high level of refusals is observed (response rate barely about 38%). This is caused mainly by the following factors: a) rural residents are more willing to cooperate; b) they are less active in sense of movement, therefore more reachable; c) the theme of interview sets people on the alert; d) population registration and register maintenance in cities are generally worse which leads to poor quality sampling frames. The influence of first two factors is aligned lately because of a falling of a scale of living of people.

40% of all the causes in the urban areas is the "household members refused contacting respondent" (cause 7), as compared with the corresponding 31.2% in the rural areas. This cause has the most spread for urban people and the second at the prevalence for rural areas (about 31% of all causes of non-response), because the theme of interview (the internal politic, interethnic problem etc.) makes people mistrustful and situation with the criminality (especially in the cities) is very complicated. Otherwise, cause 10 ("not at home for a long time") is second at the prevalence for urban areas (about 37%) and first for rural areas (about 39% of all non-response causes). This cause is spread for urban and rural people because they migrate in searches of earnings. The similar reasons called cause 3 "nobody at home" and 4 "respondent was not at home by that time" (8.2% and 2,3% for urban and 5.1% and 3.6% for rural areas accordingly). Besides for these causes there is one more explanation - employment of urban population and "cotton campaign" for rural population. The causes 6, 8, and 9 met not frequently. Therefore we may not make any conclusions. The sampling frame quality is revealed by comparing the share of cause 11 "address was not found, does not exist"- 4.8% in the urban areas versus 6.4% in the rural. In the urban areas 2.8% of the non-response are "Address is not residential" (cause 12). In the rural areas this cause makes 4.2% of all causes of non-response. In most cases it originates from that a household, in order to get an additional land plot from a makhalla committee for running subsidiary economy, declares itself to be actually consisting of two households - parents' and a new, young one. Then the makhalla committee registers a new household and allocates a plot. However, this "household" continues living with the parents, making the new address not residential. Most urban cases are connected with fitting apartments for small offices, cafes, renting to foreigners, etc. More apartments in the cities are thrown (owners have left in searching of earnings).

A dataset listing Indiana cities by population for 2024.

In 2023, there were approximately 55.94 million people living in rural areas in the United States, while about 278.98 million people were living in urban areas. Within the provided time period, the number of people living in urban U.S. areas has increased significantly since totaling only 126.46 million in 1960.

The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.

A dataset listing South Dakota cities by population for 2024.

About this dataset

Context

The dataset tabulates the Little Falls town median household income by race. The dataset can be utilized to understand the racial distribution of Little Falls town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Little Falls Town, New York median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Little Falls Town, New York (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Little Falls town median household income by race. You can refer the same here