Abstract copyright UK Data Service and data collection copyright owner.

The paper Udny Yule read to the Royal Statistical Society at the end of the nineteenth century (Yule, 1899) was a landmark in social statistics. He applied multiple regression analysis to a question of social policy, namely reforms to the 19th century system of poverty alleviation in England. To do this, Yule created a dataset from administrative and Census data. Yule’s original dataset was not preserved, but because his data were drawn from public sources, it is possible to reconstruct it, albeit with some slight differences from the original. This report provides a description of how the dataset was reconstructed and how it varies from the one used in the 1899 paper.

The 2007 Census of Law Enforcement Aviation Units is the first systematic, national-level data collection providing information about law enforcement aviation assets and functions. In general, these units provide valuable airborne support for traditional ground-based police operations. An additional role following the September 11, 2001 terrorist attacks is the provision of essential homeland security functions, such as providing critical facility checks of buildings, ports and harbors, public utilities, inland waterways, oil refineries, bridges and spans, water storage/reservoirs, National and/or State monuments, water treatment plants, irrigation facilities, airports, and natural resources. Aviation units are thought to be able to perform critical facility checks and routine patrol and support operations with greater efficiency than ground-based personnel. However, little is presently known about the equipment, personnel, operations, expenditures, and safety requirements of these units on a national level. This information is critical to law enforcement policy development, planning, and budgeting at all levels of government. The data will supply law enforcement agencies with a benchmark for comparative analysis with other similarly situated agencies, and increase understanding of the support that aviation units provide to ground-based police operations.

The BJS Census of State and Local Law Enforcement Agencies (CSLLEA) is conducted every 4 years to provide a complete enumeration of agencies and their employees. Employment data are reported by agencies for sworn and nonsworn (civilian) personnel and, within these categories, by full-time or part-time status. The pay period that included September 30, 2008, was the reference date for all personnel data. Agencies also complete a checklist of functions they regularly perform, or have primary responsibility for, within the following areas: patrol and response, criminal investigation, traffic and vehicle-related functions, detention-related functions, court-related functions, special public safety functions (e.g., animal control), task force participation, and specialized functions (e.g., search and rescue). The CSLLEA provides national data on the number of state and local law enforcement agencies and employees for local police departments, sheriffs' offices, state law enforcement agencies, and special jurisdiction agencies. It also serves as the sampling frame for BJS surveys of law enforcement agencies.

About this dataset

Context

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

Key observations

The largest age group in Fine, New York was for the group of age 60 to 64 years years with a population of 145 (12.03%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Fine, New York was the 25 to 29 years years with a population of 21 (1.74%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Fine town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Fine town 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 Fine town Population by Age. You can refer the same here

In 2018, there were 681 state and local law enforcement training academies that provided basic training instruction to 59,511 recruits. As part of the 2018 Census of Law Enforcement Training Academies (CLETA), respondents provided general information about the academies' facilities, resources, programs, and staff. The core curricula subject areas and hours dedicated to each topic, as well as training offered in some special topics, were also included. The collection included information about recruit demographics, completion, and reasons for non-completion of basic training. BJS administered previous versions of the CLETA in 2002, 2006, and 2013.

To ensure an accurate sampling frame for its Law Enforcement Management and Administrative Statistics (LEMAS) survey, the Bureau of Justice Statistics periodically sponsors a census of the nation's state and local law enforcement agencies. This census, known as the Directory Survey, includes all state and local law enforcement agencies that are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers. As in previous years, the 2000 Directory Survey collected data on the number of sworn and nonsworn personnel employed by each agency, including both full-time and part-time employees. The pay period that included June 30, 2000, was the reference date for all personnel data. A 97.4 percent response rate was obtained from the 17,784 state and local law enforcement agencies operating in the United States. This data collection contains June 2000 data from the fourth Directory Survey. Previous directory censuses were conducted in 1986 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1986: [UNITED STATES] [ICPSR 8696]), 1992 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1992: [UNITED STATES] [ICPSR 2266]), and 1996 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: [UNITED STATES] [ICPSR 2260]). Variables include personnel totals, type of government, type of agency, and whether the agency had the legal authority to hold a person beyond arraignment for 48 or more hours.

In 2016, there were approximately 132,000 full-time federal law enforcement officers who were authorized to make arrests and carry firearms in the United States and its territories. This data collection comes from the Census of Federal Law Enforcement Officers (CFLEO) and describes the agencies, functions, sex, and race of these officers. The data cover federal officers with arrest and firearm authority in both supervisory and non-supervisory roles employed as of September 30, 2016. The Bureau of Justice Statistics (BJS) administered the CFLEO to 86 federal agencies employing officers with arrest and firearm authority. The data do not include officers stationed in foreign countries and also exclude officers in the U.S. Armed Forces.

Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Fine, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Fine town did not include 5, 6, or 7-person households. Across the different household sizes in Fine town the mean income is $72,360, and the standard deviation is $40,894. The coefficient of variation (CV) is 56.51%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $26,888. It then further increased to $101,185 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/fine-ny-median-household-income-by-household-size.jpeg" alt="Fine, New York median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Fine town median household income. You can refer the same here

The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm implemented for the three previous Censuses. Our evaluation leverages the Noisy Measure File (NMF) as well as two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful, especially for Hispanic and multiracial populations. TopDown's post-processing dramatically reduces the NMF noise and produces data whose accuracy is similar to that of swapping. While the estimated errors for both TopDown and swapping algorithms are generally no greater than other sources of Census error, they can be relatively substantial for geographies with small total populations.

About this dataset

Context

The dataset tabulates the Fine Lakes township population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Fine Lakes township. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 90 (65.69% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Fine Lakes township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Fine Lakes township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Fine Lakes township is shown in the following column.

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 Fine Lakes township Population by Age. You can refer the same here

Public Law 94-171, enacted in 1975, requires the Census Bureau to provide redistricting data in a format requested by state governments. Within one year following the 1990 decennial Census (by April 1, 1991), the Census Bureau provided the governor and legislature of each state with the population data needed to redraw legislative districts. This collection contains the same substantive and geographic variables as the original Public Law 94-171 files [see CENSUS OF POPULATION AND HOUSING, 1990 [UNITED STATES]: PUBLIC LAW (P.L.) 94-171 DATA (ICPSR 9516)] but with the population counts adjusted for undernumeration. Adjusted Public Law 94-171 counts are supplied for a sample of one-half of blocks in the United States and a complete selection of areas with 1,000 or more persons. Each state file provides data for the state and its subareas in the following order: state, county, voting district, county subdivision, place, and block. Additionally, complete summaries are provided for the following geographic areas: county subdivision, place, consolidated city, state portion of American Indian and Alaska Native area, and county portion of American Indian and Alaska Native area. Area characteristics such as land area, water area, latitude, and longitude are provided. Summary statistics are provided for all persons, for persons 18 years old and over, and for housing units in the geographic areas. Counts by race and by Hispanic and non-Hispanic origin are also recorded.

The Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December of the census year, forms are sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies. This dataset presents company, establishments, value of shipments, value of product shipments, percentage of product shipments of the total value of shipments, and percentage of distribution of value of product shipments.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics will be released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for all Census Tracts in New Mexico. There are two data tables. One provides total counts by major race groups and by Hispanic ethnicity, while the other provides proportions of the total population for these same groups. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

About this dataset

Context

The dataset tabulates the population of Fine town by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Fine town across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 56.27% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Fine town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Fine town 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 Fine town Population by Race & Ethnicity. You can refer the same here

Public Law 94-171, enacted in 1975, directs the United States Census Bureau to make special preparations to provide redistricting data needed by the 50 states. It specifies that within one year following the Census Day (i.e., for Census 2000 by April 1, 2001), the Census Bureau must send the governor and legislature in each state the data they need to redraw districts for the United States Congress and state legislatures. This file contains a count of all persons and all households in New York State and its subareas, provided in hierarchical sequence down to the block level. They also provide a race count (five race categories) and a count of all persons of Hispanic origin. In addition, data are provided for all persons not of Hispanic origin and persons 18 years old and over not of Hispanic origin by race (five race categories).

The 2002 Census of Law Enforcement Training Academies (CLETA02) was the first effort by the Bureau of Justice Statistics (BJS) to collect information from law enforcement training academies across the United States. The CLETA02 included all currently operating academies that provided basic law enforcement training. Academies that provided only in-service training, corrections/detention training, or other special types of training were excluded. Data were collected on personnel, expenditures, facilities, equipment, trainees, training curricula, and a variety of special topic areas. As of year-end 2002, a total of 626 law enforcement academies operating in the United States offered basic law enforcement training to individuals recruited or seeking to become law enforcement officers.

This layer presents the 2020 U.S. Census Tract boundaries of the United States in the 50 states and the District of Columbia. This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

This dataset contains data from the P.L. 94-171 2020 Census Redistricting Program. The 2020 Census Redistricting Data Program provides states the opportunity to delineate voting districts and to suggest census block boundaries for use in the 2020 Census redistricting data tabulations (Public Law 94-171 Redistricting Data File). In addition, the Redistricting Data Program will periodically collect state legislative and congressional district boundaries if they are changed by the states. The program is also responsible for the effective delivery of the 2020 Census P.L. 94-171 Redistricting Data statutorily required by one year from Census Day. The program ensures continued dialogue with the states in regard to 2020 Census planning, thereby allowing states ample time for their planning, response, and participation. The U.S. Census Bureau will deliver the Public Law 94-171 redistricting data to all states by Sept. 30, 2021. COVID-19-related delays and prioritizing the delivery of the apportionment results delayed the Census Bureau’s original plan to deliver the redistricting data to the states by April 1, 2021.

Data in this dataset contains information on population, diversity, race, ethnicity, housing, household, vacancy rate for 2020 for various geographies (county, MCD, Philadelphia Planning Districts (referred to as county planning areas [CPAs] internally, Census designated places, tracts, block groups, and blocks)

For more information on the 2020 Census, visit https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html

PLEASE NOTE: 2020 Decennial Census data has had noise injected into it because of the Census's new Disclosure Avoidance System (DAS). This can mean that population counts and characteristics, especially when they are particularly small, may not exactly correspond to the data as collected. As such, caution should be exercised when examining areas with small counts. Ron Jarmin, acting director of the Census Bureau posted a discussion of the redistricting data, which outlines what to expect with the new DAS. For more details on accuracy you can read it here: https://www.census.gov/newsroom/blogs/director/2021/07/redistricting-data.html