Comprehensive demographic dataset for Villages of Denver, Denver, NC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in The Village Of Indian Hill. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in The Village Of Indian Hill. 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 2023

In terms of income distribution across age cohorts, in The Village Of Indian Hill, the median household income stands at $250,001 for householders within the 25 to 44 years age group, followed by $250,001 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 $170,354.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-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 2023 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 The Village Of Indian Hill median household income by age. You can refer the same here

Growth data for housing units and employment for the growth areas, urban centers and villages, for the City of Seattle Comprehensive Plan. This is a stand alone table that includes non-spatial records.Housing unit growth is reported quarterly from the city's permitting system while employment change is reported annually from the State of Washington QCEW data.

Comprehensive demographic dataset for Villages of Woodland Springs, Keller, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

(Link to Metadata) The BNDHASH dataset depicts Vermont village, town, county, and Regional Planning Commission (RPC) boundaries. It is a composite of generally 'best available' boundaries from various data sources (refer to ARC_SRC and SRC_NOTES attributes). However, this dataset DOES NOT attempt to provide a legally definitive boundary. The layer was originally developed from TBHASH, which was the master VGIS town boundary layer prior to the development and release of BNDHASH. By integrating village, town, county, RPC, and state boundaries into a single layer, VCGI has assured vertical integration of these boundaries and simplified maintenance. BNDHASH also includes annotation text for town, county, and RPC names. BNDHASH includes the following feature classes: 1) BNDHASH_POLY_VILLAGES = Vermont villages 2) BNDHASH_POLY_TOWNS = Vermont towns 3) BNDHASH_POLY_COUNTIES = Vermont counties 4) BNDHASH_POLY_RPCS = Vermont's Regional Planning Commissions 5) BNDHASH_POLY_VTBND = Vermont's state boundary 6) BNDHASH_LINE = Lines on which all POLY feature classes are built The master BNDHASH data is managed as an ESRI geodatabase feature dataset by VCGI. The dataset stores village, town, county, RPC, and state boundaries as seperate feature classes with a set of topology rules which binds the features. This arrangement assures vertical integration of the various boundaries. VCGI will update this layer on an annual basis by reviewing records housed in the VT State Archives - Secretary of State's Office. VCGI also welcomes documented information from VGIS users which identify boundary errors. NOTE - VCGI has NOT attempted to create a legally definitive boundary layer. Instead the idea is to maintain an integrated village/town/county/RPC/state boundary layer which provides for a reasonably accurate representation of these boundaries (refer to ARC_SRC and SRC_NOTES). BNDHASH includes all counties, towns, and villages listed in "Population and Local Government - State of Vermont - 2000" published by the Secretary of State. BNDHASH may include changes endorsed by the Legislature since the publication of this document in 2000 (eg: villages merged with towns). Utlimately the Vermont Secratary of State's Office and the VT Legislature are responsible for maintaining information which accurately describes the locations of these boundaries. BNDHASH should be used for general mapping purposes only. * Users who wish to determine which boundaries are different from the original TBHASH boundaries should refer to the ORIG_ARC field in the BOUNDARY_BNDHASH_LINE (line feature with attributes). Also, updates to BNDHASH are tracked by version number (ex: 2003A). The UPDACT field is used to track changes between versions. The UPDACT field is flushed between versions.

About this dataset

Context

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

Key observations

Based on our analysis of the distribution of Patton Village population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 78.15% of the total residents in Patton Village. Notably, the median household income for White households is $78,750. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $78,750.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Patton Village median household income by race. You can refer the same here

Abstract

China Living Standards Survey (LSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The China LSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in North-eastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930's. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930's. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus, the intended sample size was 780 households, 130 from each county. Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list. In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY All responses obtained from the household interviews were recorded in the household questionnaires. These were then entered into the computer, in the field, using data entry programs written in BASIC. The data produced by the data entry program were in the form of household files, i.e. one data file for all of the data in one household/community questionnaire. Thus, for the household there were about 880 data files. These data files were processed at the University of Toronto and the World Bank to produce datasets in statistical software formats, each of which contained information for all households for a subset of variables. The subset of variables chosen corresponded to data entry screens, so these files are hereafter referred to as "screen files". For the household survey component 66 data files were created. Members of the survey team checked and corrected data by checking the questionnaires for original recorded information. We would like to emphasize that correction here refers to checking questionnaires, in case of errors in skip patterns, incorrect values, or outlying values, and changing values if and only if data in the computer were different from those in the questionnaires. The personnel in charge of data preparation were given specific instructions not to change data even if values in the questionnaires were clearly incorrect. We have no reason to believe that these instructions were not followed, and every reason to believe that the data resulting from these checks and corrections are accurate and of the highest quality possible.

(b) DATA EDITING The screen files were then brought to World Bank headquarters in Washington, D.C. and uploaded to a mainframe computer, where they were converted to "standard" LSMS formats by merging datasets to produce separate datasets for each section with variable names corresponding to the questionnaires. In some cases, this has meant a single dataset for a section, while in others it has meant retaining "screen" datasets with just the variable names changed. Linking Parts of the Household Survey Each household has a unique identification number which is contained in the variable HID. Values for this variable range from 10101 to 60520. The first number is the code for the six counties in which data were collected, the second and third digits are for the villages within each county. Finally, the last two digits of HID contain the household number within the village. Data for households from different parts of the survey can be merged by using the HID variable which appears in each dataset of the household survey. To link information for an individual use should be made of both the household identification number, HID, and the person identification number, PID. A child in the household can be linked to the parents, if the parents are household members, through the parents' id codes in Section 01B. For parents who are not in the household, information is collected on the parent's schooling, main occupation and whether he/she is currently alive. Household members can be linked with their non-resident children through the parents' id codes in Section 01C. Linking the Household to the Community Data The community data have a somewhat different set of identifying variables than the household data. Each community dataset has four identifying variables: province (code 7 for Hebei and code 8 for Liaoning); county (six two digit codes, of which the first digit represents province and the second digit represents the three counties in each province); township (3 digit code, first digit is county, second digit is county and third digit is township); and village (4 digit code, first digit is county, second digit is county, third digit is township, and third fourth digit is village). Constructed Data Set Researchers at the World Bank and the University of Toronto have created a data set with information on annual household expenditures, region codes, etc. This constructed data set is made available for general use with the understanding that the description below is the only documentation that will be provided. Any manipulation of the data requires assumptions to be made and, as much as possible, those assumptions are explained below. Except where noted, the data set has been created using only the original (raw) data sets. A researcher could construct a somewhat different data set by incorporating different assumptions. Aggregate Expenditure, TOTEXP. The dataset TOTEXP contains variables for total household annual expenditures (for the year 1994) and variables for the different components of total household expenditures: food expenditures, non-food expenditures, use value of consumer durables, etc. These, along with the algorithm used to calculate household expenditures are detailed in Appendix D. The dataset also contains the variable HID, which can be used to match this dataset to the household level data set. Note that all of the expenditure variables are totals for the household. That is, they are not in per capita terms. Researchers will have to divide these variables by household size to get per capita numbers. The household size variable is included in the data set.

Growth data for housing units and employment for the growth areas, urban centers and villages, for the City of Seattle Comprehensive Plan.

Housing unit growth is reported quarterly from the city's permitting system while employment change is reported annually from the State of Washington QCEW data.

This dataset is about book subjects. It has 1 row and is filtered where the books is Village home. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Villages help elders stay at home in their neighborhoods. A Village is neighborhood-based nonprofit membership organization supported by volunteers. A Village makes it easier for older neighbors to keep living safely, comfortably and actively in their own homes and connected with their neighbors.Members continue to live in their homes. The can get together for parties, picnics, happy hours, and visits to local theaters, music, and art venues. Volunteers offer free services that can range from rides to and from medical appointments, prescription pickups, yard clean-ups, and simple handyman repairs, assistance with grocery shopping, changing light bulbs in ceiling fixtures, and reading to the visually impaired. Villages also help their members find useful community resources and reliable professionals and licensed vendors. Villages do not provide medical services, but can connect seniors with these services. They typically offer some services not traditionally offered by the DC Lead Agencies.The Department of Aging and Community Living has a senior service directory of agencies providing a variety of services. Call (202) 724-5622.

About this dataset

Context

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

Key observations

Based on our analysis of the distribution of Village Of Four Seasons population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 96% of the total residents in Village Of Four Seasons. Notably, the median household income for White households is $92,835. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $92,835.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Village Of Four Seasons median household income by race. You can refer the same here

The Residential Communities of Loudoun County map let's you search by address or by residential community (subdivision project) name. Click on a community for more information, including percent complete, the number of units allowed and left to build, and the size. Zoom in further to see the parcels within a community. You can also search by Other Name to find incorporated towns, villages, or historic place names.

Census 2020 Redistricting Data obtained from U.S. Census Bureau. This polygon feature contains block-level data provided by the census, along with a field marked "CVT" designating city/village/township added by the Stark County Regional Planning Commission. NOTE: some of the records belong to outside of Stark County but were included in this dataset because they are a part of cities/villages/townships that are technically a part of Stark County: Magnolia, Minerva, and Alliance. See "County Name" (Field: County_Name).

This dataset provides information about the number of properties, residents, and average property values for Shelton Street cross streets in The Villages, FL.

Cities, Towns and Villages dataset current as of 2006. City Limits.

This dataset symbolizes city, county, town, parish, village, or other general-purpose political subdivisions of a State. The term "Unit of General Local Government" refers to a city, county, town, parish, village, or other general-purpose political subdivision of a State. Units of General Local Government (UGLG) are comprised of several Census geographies including: Summary Level 050 - State-County; Summary Level 060 - County Subdivision; Summary Level 070 - State-County-County Subdivision-Place/Remainder; Summary Level 160 - Place; Summary Level 170 - State-Consolidate City; Remainders of County Lands.

Cities, Towns and Villages dataset current as of 2007. Chattahoochee-Flint Region County and Municipality Location Map.

The dataset contains a hierarchal listing of New York State counties, cities, towns, and villages, as well as official locality websites

Cities, Towns and Villages dataset current as of 2008. This is a polygon dataset of the Municipalities located within Washburn County, Wisconsin. The boundaries are derived from a variety of source data including; section and quarter section corners. Deed descriptions and surveys of rec..

Cities, Towns and Villages dataset current as of 2013. Minor Civil Divisions. Include County, City, Village and Town boundaries..