About this dataset

Context

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

Key observations

Largest age group (population): Male # 70-74 years (15) | Female # 50-54 years (11). 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 Free Soil population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Free Soil is shown in the following column.
• Population (Female): The female population in the Free Soil 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 Free Soil 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 Free Soil Population by Gender. You can refer the same here

This dataset shows census data for Nigeria from government data sources and the World Bank data portal.

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates. Note: The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Abstract

The 2005 Republic of Palau Census of Population and Housing will be used to give a snapshot of Republic of Palau's population and housing at the mid-point of the decade. This Census is also important because it measures the population at the beginning of the implementation of the Compact of Free Association. The information collected in the census is needed to plan for the needs of the population. The government uses the census figures to allocate funds for public services in a wide variety of areas, such as education, housing, and job training. The figures also are used by private businesses, academic institutions, local organizations, and the public in general to understand who we are and what our situation is, in order to prepare better for our future needs.

The fundamental purpose of a census is to provide information on the size, distribution and characteristics of a country's population. The census data are used for policymaking, planning and administration, as well as in management and evaluation of programmes in education, labour force, family planning, housing, health, transportation and rural development. A basic administrative use is in the demarcation of constituencies and allocation of representation to governing bodies. The census is also an invaluable resource for research, providing data for scientific analysis of the composition and distribution of the population and for statistical models to forecast its future growth. The census provides business and industry with the basic data they need to appraise the demand for housing, schools, furnishings, food, clothing, recreational facilities, medical supplies and other goods and services.

Geographic coverage

A hierarchical geographic presentation shows the geographic entities in a superior/subordinate structure in census products. This structure is derived from the legal, administrative, or areal relationships of the entities. The hierarchical structure is depicted in report tables by means of indentation. The following structure is used for the 2005 Census of the Republic of Palau:

Republic of Palau State Hamlet/Village Enumeration District Block

Analysis unit

Individuals Families Households General Population

Universe

The Census covered all the households and respective residents in the entire country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2005 Palau Census of Population and Housing comprises three parts: 1. Housing - one form for each household 2. Population - one for for each member of the household 3. People who have left home - one form for each household.

Cleaning operations

Full scale processing and editing activiities comprised eight separate sessions either with or separately but with remote guidance of the U.S. Census Bureau experts to finalize all datasets for publishing stage.

Processing operation was handled with care to produce a set of data that describes the population as clearly and accurately as possible. To meet this objective, questionnaires were reviewed and edited during field data collection operations by crew leaders for consistency, completeness, and acceptability. Questionnaires were also reviewed by census clerks in the census office for omissions, certain inconsistencies, and population coverage. For example, write-in entries such as "Don't know" or "NA" were considered unacceptable in certain quantities and/or in conjunction with other data omissions.

As a result of this review operation, a telephone or personal visit follow-up was made to obtain missing information. Potential coverage errors were included in the follow-up, as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Subsequent to field operations, remaining incomplete or inconsistent information on the questionnaires was assigned using imputation procedures during the final automated edit of the collected data. Allocations, or computer assignments of acceptable data in place of unacceptable entries or blanks, were needed most often when an entry for a given item was lacking or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in previous censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in lace of blanks or unacceptable entries enhanced the usefulness of the data.

Another way to make corrections during the computer editing process is substitution. Substitution is the assignment of a full set of characteristics for a person or housing unit. Because of the detailed field operations, substitution was not needed for the 2005 Census.

Sampling error estimates

Sampling Error is not applicable to full enumeration censuses.

Data appraisal

In any large-scale statistical operation, such as the 2005 Census of the Republic of Palau, human- and machine-related errors were anticipated. These errors are commonly referred to as nonsampling errors. Such errors include not enumerating every household or every person in the population, not obtaining all required information form the respondents, obtaining incorrect or inconsistent information, and recording information incorrectly. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires.

To reduce various types of nonsampling errors, a number of techniques were implemented during the planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

About this dataset

Context

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

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel 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 Excel Population by Age. You can refer the same here

An Act for ascertaining the Number of the Inhabitants of the Colony of New South Wales in the Year One thousand eight hundred and forty-one, 1840 (4 Victoria Act No. 26) required every householder, employer of servants and proprietor and occupier of land to complete the census schedule on the second day ('or on the days immediately subsequent thereto') of March 1841.

The 1841 Census was more complete than its predecessors, as the population was recorded in police districts, counties and towns. There was a broader tabulation of results which included age groups, conjugal condition (married or unmarried), religious denomination and civil condition. Civil condition provided statistical information on the number of bond (convict) or free males and females in a household, whether they were born in the colony, arrived free, held a ticket of leave, and whether they were in government employment or private assignment.

The Census was taken by specially appointed collectors generally responsible to a Commissioner or a Bench of Magistrates, the collector completed printed forms, known as Form ‘A’ for each household in the allotted territory. After the Census magistrates were instructed to check the returns and send abstracts to the Colonial Secretary, designated Form ‘C’. The returns were then gathered together, statistics extracted and the final returns made.

This series comprises bound volumes of Form C . (NRS 1281).

The Form C records: number of return, name of establishment (usually head of household), number of each age group for males, and then for females (the age divisions are under two, two and under seven, seven and under 14, 14 and under 21, 21 and under 45, 45 and under 60, 60 and upwards); married or single; civil condition: free (born in colony, arrived free, other free persons), bond (ticket of leave, in government employment, in private assignment); then religion divided into Church of England, Church of Scotland, Wesleyan Methodists, other Protestant dissenters, Roman Catholics, Jews, Mohammedans and Pagans; occupation divided into land proprietors, merchants, bankers, and professional men; shopkeepers and other retail dealers; mechanics and artificers; shepherds and others in the care of sheep; gardeners, stockmen and persons employed in agriculture; domestic servants; all other persons not included in the foregoing classes; totals for males, for females, and for both; houses - further divided into stone or brick, wood, total; finished or unfinished; inhabited or uninhabited. The columns are totalled at the bottom of each sheet.

As well as these Abstracts of returns, there are also a number of "condensed" abstracts of returns, filled in on Form C. These enumerate the running numbers covered by each sheet of abstracts eg. one-20, 21-40 and give sums for each group as well as grand total.

Form C abstracts are arranged by district following the order in the Returns of the Colony for 1841. 'Condensed' Abstracts are filed with the district abstracts to which they pertain.

Berrima-Port Phillip (X946-49)
Queanbeyan-Yass (X950-51)

References
1) State Records New South Wales Website, "Concise Guide to the State Archives (Ca - Commissioners): Colonial Secretary, later Chief Secretary, later Services; s. Population and Statistics, a. Musters and Census Records, ii. Census,23. 1841 Census: Abstracts of returns, CGS 1282."
2) State Records New South Wales Website, "Introduction to the 1841 Census: Index to the 1841 Census, Background".
3) State Records New South Wales Website, "Short Guide 12 - Muster and Census Records, 1788 - 1901".

Abstract copyright UK Data Service and data collection copyright owner. The aim of Free Census is to transcribe all the UK 19th century census returns. It is part of Free UKGEN and is a sister project to FreeBMD and Free REG. The intention is that all three of the UK primary sources for the family historian will be online and “free to view.” Main Topics: This data is a transcription of all census enumerators books of the Cornwall registration districts. Variables include: address of household, forename and surname, age at last birthday (rounded down to nearest 5-year point for people over 15), occupation, and whether born in Cornwall or another English county or in Scotland, Ireland or Foreign Parts and additional notes.

Abstract copyright UK Data Service and data collection copyright owner. This study contains a complete transcript of the Wiltshire returns of the census of 1841. Main Topics: Using microfiche loaned to the project by the LDS, volunteers, recruited online, transcribed the pages of the enumerators' books for the Wiltshire 1841 census. Other volunteers checked the data using Free Census software. Finally, the organiser validated the data, using yet another piece of Free Census software. The original data was collected in 1841. the raw data was in the form of microfiche, organised in accordance with the PRO regulations

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Group quarters: A collective household is a group of persons that does not live in an ordinary household, but lives in a collective establishment, sharing meal times.

Kind of data

Census/enumeration data [cen]

Sampling procedure

SAMPLE DESIGN: Systematic manual sorting into lots with different sample units, according to target population.

SAMPLE UNIT: Private dwellings and individuals for group quarters and compte a part

SAMPLE FRACTION: 5%

SAMPLE UNIVERSE: The microdata sample includes mainland France and Corsica.

SAMPLE SIZE (person records): 2,320,901

Mode of data collection

Face-to-face [f2f]

Research instrument

Separate forms for buildings, group quarters (collective households), group quarters (compte a part), private households, and boats. Four forms for individuals (living in group quarters and private dwellings; two different forms for people compte a part; living in boats).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

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Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

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Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

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• Accurate at zip code and administrative level

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• Standardized and unified demographic data structure

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Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Free Soil. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Free Soil. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2021

In terms of income distribution across age cohorts, in Free Soil, the median household income stands at $68,233 for householders within the 25 to 44 years age group, followed by $63,504 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $35,805.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific 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 Free Soil median household income by age. You can refer the same here

Abstract

The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect, process and disseminate detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

Geographic coverage

The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the following so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the areas of the Transkei and Bophuthatswana. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

The units of analysis of the 1980 census includes households, individuals and institutions

Universe

The 1980 South African census covered all household members (usual residents).

The 1980 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980) were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons are employed as domestics by you? (Include garden workers) (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Free Soil Township, Michigan, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Free Soil Township, Michigan reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Free Soil township households based on income levels.

Key observations

• For Family Households: In Free Soil township, the majority of family households, representing 31.35%, earn $75,000 to $99,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 1.19%, have incomes falling $150,000 to $199,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Free Soil township, the majority of non-family households, accounting for 20.8%, have income $35,000 to $39,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $150,000 to $199,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Free Soil Township, Michigan (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Free Soil Township, Michigan
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Free Soil Township, Michigan
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Free Soil Township, Michigan

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 Free Soil township median household income. You can refer the same here

Context

Federal Superfund sites are some of the most polluted in the United States. This dataset contains a multifaceted view of Superfunds, including free-form text descriptions, geography, demographics and socioeconomics.

Content

The core data was scraped from the National Priorities List (NPL) provided by the U.S. Environmental Protection Agency (EPA). This table provides basic information such as site name, site score, date added, and links to a site description and current status. Apache Tika was used to extract text from the site description pdfs. The addresses were scraped from site status pages, and used to geocode to latitude and longitude and Census block group. The block group assignment was used to join with the Census Bureau's planning database, a rich source of nationwide demographic and socioeconomic data. The full source code used to generate the data can be found here, on github.

I have provided three separate downloads to explore:

• priorities_list_full.json: the NPL containing all geographic, site information, text descriptions, and Census Bureau data from the relevant block groups.
• pdb_tract.csv: the planning database aggregated on the tract level with an additional indicator (has_superfund) noting whether or not the tract contains the address of a Superfund site.
• pdb_block_group.csv: the planning database aggregated on the block group level with an additional indicator (has_superfund) noting whether or not the block group contains the address of a Superfund site.

Some caveats:

1. The planning database contains 300+ columns. For a full description of these columns, please see the documentation here.
2. Since the Google geocoder is relatively aggressive in providing address matches, geocoding was done through a hierarchy of queries (full address, city-state-zip, and zipcode only) to prevent gross errors. The address string used to geocode is noted through the 'geocode_source' column.
3. While this data is linked to demographic and socioeconomic data based on either the block group (tract for pdb_tract.csv), the impacts of a particular site's pollution may extend beyond these geographic regions.

Acknowledgements

I would like to thank the EPA and the Census Bureau for making such detailed information publicly available. For relevant academic work, please see Burwell-Naney et al. (2013) and references, both to and therein.

Please let me know if you have any suggestions for improving the dataset!

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..Examples of "Internet access without a subscription" include cases such as free Internet service provided by a respondent's town or city or free Internet service a university may provide for their students..Internet access refers to whether or not a household uses or connects to the Internet, regardless of whether or not they pay for the service to do so. Data about Internet access was collected by asking if the respondent or any member of the household accessed the Internet. The respondent then selected one of the following three categories: "Yes, by paying a cell phone company or Internet service provider"; "Yes, without paying a cell phone company or Internet service provider"; or "No access to the Internet at the house, apartment or mobile home". Only respondents who answered "Yes, by paying a cell phone company or Internet service provider" were asked the subsequent question about the types of service they had access to such as dial-up, broadband (high speed) service such as cable, fiber-optic, or DSL, a cellular data plan, satellite or some other service..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not b...

This dataset contains information about 2,280 people who lived in Frederick, Maryland (including Frederick City) between 1785 to 1841. The information about enslaved, freed, or liminal status persons is drawn from the family records, deeds of manumission, bills of sale, and runaway advertisements of three prominent slave owning families: the McPhersons, the Potts, and the Murdochs. Information about free persons is drawn from the 1832 Census of Free Negroes in Frederick County conducted by the Maryland Colonization Society (hereafter referred to as “the 1832 Census”). Data pulled from the 1832 Census constitutes the majority of this dataset. This preeminence of free African Americans, whose freedom status before and after 1832 is unknown, documents Frederick County’s significant free Black population. By including enslaved, liminal status, and free people in one place, this dataset spotlights Frederick County’s diverse African American population and demonstrates the fluidity of Black freedom in antebellum Maryland.