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.

To ensure an accurate sampling frame for its Law Enforcement Management and Administrative Statistics (LEMAS) survey, the Bureau of Justice Statistics sponsors a census of the nation's state and local law enforcement agencies, known as the Directory Survey. This census, which is conducted every four years, includes all state and local law enforcement agencies operating nationwide 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 2004 census 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 September 30, 2004, was the reference date for all personnel data. 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. Previous 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]), 1996 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: [UNITED STATES] [ICPSR 2260]), and 2000 (Census of State and Local Law Enforcement Agencies (CSLLEA), 2000: [United States] [ICPSR 3484]).

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.

Overview This dataset re-shares cartographic and demographic data from the U.S. Census Bureau to provide an obvious supplement to Open Environments Block Group publications.These results do not reflect any proprietary or predictive model. Rather, they extract from Census Bureau results with some proportions and aggregation rules applied. For additional support or more detail, please see the Census Bureau citations below. Cartographics refer to shapefiles shared in the Census TIGER/Line publications. Block Group areas are updated annually, with major revisions accompanying the Decennial Census at the turn of each decade. These shapes are useful for visualizing estimates as a map and relating geographies based upon geo-operations like overlapping. This data is kept in a geodatabase file format and requires the geopandas package and its supporting fiona and DAL software. Demographics are taken from popular variables in the American Community Survey (ACS) including age, race, income, education and family structure. This data simply requires csv reader software or pythons pandas package. While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file and geometry in a gpd file needed an installation of geopandas, fiona and DAL software. More details on the ACS variables selected and derivation rules applied can be found in the commentary docstrings in the source code found here: https://github.com/OpenEnvironments/blockgroupdemographics. ## Files While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file named YYYYblcokgroupdemographics.csv. The cartographic column, 'geometry', is shared as file named YYYYblockgroupdemographics-geometry.pkl. This file needs an installation of geopandas, fiona and DAL software.

About this dataset

Context

The dataset tabulates the Rule population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Rule.

Content

The dataset constitues the following two datasets across these two themes

• Rule, TX Population Breakdown by Gender
• Rule, TX Population Breakdown by Gender and Age

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/.

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.

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 presents median household incomes for various household sizes in Rule, TX, 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, Rule did not include 4, 6, or 7-person households. Across the different household sizes in Rule the mean income is $59,524, and the standard deviation is $43,645. The coefficient of variation (CV) is 73.32%. 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 $19,747. It then further increased to $82,644 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/rule-tx-median-household-income-by-household-size.jpeg" alt="Rule, TX 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 Rule median household income. You can refer the same here

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.

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.

Census Designated Places are the statistical counterparts of incorporated places. CDPs are settled concentrations of population that are identifiable by name but not legally incorporated under the laws of the state in which the CDPs are located. The Census Bureau defines CDP boundaries in cooperation with local partners as part of the PSAP. CDP boundaries usually coincide with visible features or the boundary of an adjacent Incorporated Place or another legal entity boundary. CDPs have no legal status and do not have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. There are no population size requirements for CDPs. In the nine states of the Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont) as well as Michigan, Minnesota, and Wisconsin, a CDP may represent a densely settled concentration of population within a town or township; in other instances, a CDP represents an entire town or township.Additional resources to obtain Place geography is listed below.Consolidated City Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CONCITY/Place Shapefile (Includes Incorporated Place and Census Designated Place) – https://www2.census.gov/geo/tiger/TIGER2020/PLACE/

The life-cycle age groups are:

• under 15 years
• 15 to 29 years
• 30 to 64 years
• 65 years and over.

Map shows the percentage change in the census usually resident population count for life-cycle age groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.

Footnotes

Geographical boundaries
Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

Subnational census usually resident population
The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

Caution using time series
Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

About the 2023 Census dataset
For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

Data quality
The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

Quality rating of a variable
The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

Age concept quality rating
Age is rated as very high quality.
Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.

Using data for good
Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga".

Confidentiality
The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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).

2020 TIGER FilesTopologically Integrated Geographic Encoding and Referencing (TIGER) files are a product of the U.S. Census Bureau. These files include vector data on features such as transportation and hydrography, landmarks, Congressional Districts, and census blocks and tracts.Full technical documentation for TIGER/Line® Shapefiles can be found here.2020 Redistricting DataPublic Law (P.L.) 94-171, enacted by Congress in December 1975, requires the Census Bureau to provide states the opportunity to identify the small area geography for which they need data in order to conduct legislative redistricting. The law also requires the U.S. Census Bureau to furnish tabulations of population to each state, including for those small areas the states have identified, within one year of Census day.Since the first Census Redistricting Data Program, conducted as part of the 1980 census, the U.S. Census Bureau has included summaries for the major race groups specified by the Statistical Programs and Standards Office of the U.S. Office of Management and Budget (OMB) in Directive 15 (as issued in 1977 and revised in 1997). Originally, the tabulation groups included White, Black, American Indian/Alaska Native, and Asian/Pacific Islander, plus “some other race.” These race data were also cross-tabulated by Hispanic/Non-Hispanic origin. At the request of the state legislatures and the Department of Justice, for the 1990 Census Redistricting Data Program, voting age (18 years old and over) was added to the cross-tabulation of race and Hispanic origin. For the 2000 Census, these categories were revised to the current categories used today.To view the full technical documentation for the 2020 Census Redistricting Data, please click here.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Rule: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 62(19.38%) households with a householder aged between 25 and 44 years, 110(34.38%) households with a householder aged between 45 and 64 years, and 148(46.25%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the town of Rule, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• 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
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Rule median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Rule, TX population pyramid, which represents the Rule population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Rule, TX, is 26.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Rule, TX, is 47.6.
• Total dependency ratio for Rule, TX is 74.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Rule, TX is 2.1.

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 for the Rule population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Rule for the selected age group is shown in the following column.
• Population (Female): The female population in the Rule for the selected age group is shown in the following column.
• Total Population: The total population of the Rule for the selected age group 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 Rule Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Rule by race. It includes the distribution of the Non-Hispanic population of Rule across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Rule across relevant racial categories.

Key observations

Of the Non-Hispanic population in Rule, the largest racial group is White alone with a population of 434 (87.85% of the total Non-Hispanic population).

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: This column displays the racial categories (for Non-Hispanic) for the Rule
• Population: The population of the racial category (for Non-Hispanic) in the Rule is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Rule total Non-Hispanic 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 Rule Population by Race & Ethnicity. You can refer the same here

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.