Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Table legend: SD = standard deviation; FSIQ = Full Scale Intelligence Quotient.

The table MT- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 677876 rows across 699 variables.

This table concerns jobs of foreign-born employees within the age range of 18 up to 74 years. A distinction is made between employees who are registered as a resident in the Dutch population register (BRP; formerly known as the GBA) and those not registered as a resident in the BRP. Furthermore, the table can be broken down into origin background, gender, age, hourly wage class, employment contract type, and the Dutch standard industrial classification (SBI 2008). All employees registered as resident were at least 18 years old when they immigrated to the Netherlands. Likewise, the non-resident employees were at least 18 years old at the start of their stay in the Netherlands. The variable ‘country of origin’ is included as a background variable. Because the target population consists of both resident and non-resident employees, it is not always possible to directly derive the origin background. Missing data in this respect are imputed using information on someone’s country of permanent residence or someone’s nationality. Data available from: 2010. Status of the figures: Data from 2010 up to and including 2023 are final. Changes as of 28 March 2025: The figures for 2023 are adjusted. The method for determining the population has been improved for the reference period 2023. This means that approximately 1% of the total number of jobs held by foreign-born employees are now included. When will new figures be published? New figures for 2024 will be published in the fourth quarter of 2025.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. See methodologyhttps://www.census.gov/programs-surveys/international-programs/about/idb.html

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the county level..The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age;

B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by County Date of Coverage: 2016-2020

The table OH- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 7596373 rows across 699 variables.

aOral benign conditions.bOral HK/EH+OED cases.cOral SCCA.

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: 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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Distribution of demographic variables and health measures by frailty status.

Descriptive statistics of dependent, main independent, and extraneous (socio-demographic) variables.

Absolute Mobility, Air Pollution, and Demographic Characteristics of 70,185 US Census Tracts. Absolute Mobility from the Opportunity Atlas dataset. Demographic variables from the Census and ACS. Air pollution data from Colmer et al. 2023. Meterological variables from Daymet.

This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains aggregated household and individual sociodemographic data at the block-level from the 2005 and 2018 National Demographic Census. The data were downloaded from the National Administrative Department of Statistics (DANE). The data were not validated independently. Census data were provided at the level of persons, households, dwellings and spatial data (city blocks). To relate non-spatial and spatial data, city block codes (22 characters) were generated by concatenating the department code (2 characters), municipality (3 characters), class (1 character), rural sector (3 characters),rural section (2 characters), population center (3 characters), urban sector (4 characters), urban section (2 characters) and city block (2 characters).These codes were linked to the persons database. The 2005 and 2018 educational level and employment status census data had two additional categories with no clear definition in the census documentation ("Not applicable" and "Not reported"). Those categories were merged into the "Not reported" category. The 2005 and 2018 census data were merged into one dataset with the following attributes: city block code, census year, sex, educational level, and employment status, combining the multiple categories of socioeconomic variables.

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This dataset contains measures of socioeconomic and demographic characteristics by US census tract 1990-2010. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.-9An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.-8An '-8' means that the estimate is not applicable or not available.-6A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.-5A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate.-3A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.-2A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

The table NE- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 1182076 rows across 699 variables.

Births by Maternal Demographic Characteristics - 5-Year Aggregations reports the 5-year average number and percentage of births in certain categories by maternal demographic characteristics (mother's age, race, and ethnicity). Frequency Annual Full Description This dataset provides select birth outcomes (birthweight, gestational age) for Connecticut resident births along with select demographic characteristics of the mother (mother's age, race, and ethnicity). Low birth weight is less than 2500 grams, or about 5.5 pounds. Very low birth weight is less than 1500 grams, or about 3.3 pounds. A birth is premature if it occurs before the gestational age of 37 completed weeks. Department of Public Health collects and publishes annual vital statistics reports (www.ct.gov/dph/RegistrationReport) and CTdata.org carries annual data, 3-year and 5-year averages. More dimensions can be found in the raw data such as birth pluralities.

*p-value associated to differences among sets using χ2 test (categorical variables) or ANOVA test (quantitative variables).