This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

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80% of parents say that their children under the age of 11 watch YouTube regularly.

This API returns a search for the demographic information for a particular geography type and geography name.

This website provides demographic information about laboratories, including CLIA number, facility name and address, where the laboratory testing is performed, the type of CLIA certificate, and the date the certificate expires. This list is updated monthly and represents the information in the system at the time of update. For additional information about a particular laboratory, contact the appropriate State Agency or Regional Office CLIA contact (refer to State Agency or Regional Office CLIA link found on the left-hand navigation pane).

This is an extract of the decennial Public Use Microdata Sample (PUMS) released by the Bureau of the Census. Because the complete PUMS files contain several hundred thousand records, ICPSR has constructed this subset to allow for easier and less costly analysis. The collection of data at ten year increments allows the user to follow various age cohorts through the life-cycle. Data include information on the household and its occupants such as size and value of dwelling, utility costs, number of people in the household, and their relationship to the respondent. More detailed information was collected on the respondent, the head of household, and the spouse, if present. Variables include education, marital status, occupation and income.

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

Demographic Data for Boston’s Neighborhoods, 1950-2019

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

The vast majority of TikTok users are below 30 years old. Approximately 37 million Gen-Zers used TikTok in the US.

Abstract (en): This data collection supplies standard monthly labor force data and also provides supplemental data on work experience, income, noncash benefits, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 15 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full time, total income and income components, and residence on March 1, 1990. This file also contains data covering nine noncash income sources: food stamps, school lunch programs, employer-provided group health insurance plans, employer-provided pension plans, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Information on demographic characteristics, such as age, sex, race, household relationship, and Spanish origin, are available for each person in the household enumerated. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.. Civilian noninstitutional population of the United States living in housing units and male members of the Armed Forces living in civilian housing units on military bases or in households not on military bases. A national probability sample was used in selecting housing units. About 57,000 housing units were contacted with an additional sample of 2,500 Spanish households added to the March survey sample. The sample was located in 729 sample areas comprising 1,973 counties and independent cities with coverage in every state and in the District of Columbia. The 300,012 cases in this hierarchical file include household-level, family-level, and person-level records. There are approximately 120 variables for the household records, approximately 65 variables for the family records, and approximately 350 variables for the person records. Data on employment and income refer to the preceding year, although demographic data refer to the time of the survey.

The most significant cohorts of users on Instagram are aged 18 – 24.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020). More detailed can be found on the page of the original version (https://doi.org/10.5281/zenodo.3768003).

This release increases the resolution to 0.25˚ and is explicitly designed to match with the grid definition of the ERA5 climate reanalysis dataset. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution.

The synthetic population was generated from the 2010-2014 ACS PUMS housing and person files.

United States Department of Commerce. Bureau of the Census. (2017-03-06).
American Community Survey 2010-2014 ACS 5-Year PUMS File [Data set].
Ann Arbor, MI: Inter-university Consortium of Political and Social
Research [distributor]. http://doi.org/10.3886/E100486V1

Outputs

There are 17 housing files
- repHus0.csv, repHus1.csv, ... repHus16.csv
and 32 person files
- rep_recode_ACSpus0.csv, rep_recode_ACSpus1.csv, ... rep_recode_ACSpus31.csv.

Files are split to be roughly equal in size. The files contain data for the entire country. Files are not split along any demographic characteristic. The person files and housing files must be concatenated to form a complete person file and a complete housing file, respectively.

If desired, person and housing records should be merged on 'id'. Variable description is below.

Data Dictionary
See [2010-2014 ACS PUMS data dictionary](http://doi.org/10.3886/E100486V1). All variables from the ACS PUMS housing files are present in the synthetic housing files and all variables from the ACS PUMS person files are present in the synthetic person files. Variables have not been modified in any way. Theoretically, variables like `person weight` no longer have any use in the synthetic population.

See README.md for more details.

This is a hybrid gridded dataset of demographic data for China from 1979 to 2100, given as 21 five-year age groups of population divided by gender every year at a 0.5-degree grid resolution.

The historical period (1979-2020) part of this dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4, UN WPP-Adjusted Population Count) with gridded population from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, Histsoc gridded population data).

The projection (2010-2100) part of this dataset is resampled directly from Chen et al.’s data published in Scientific Data.

This dataset includes 31 provincial administrative districts of China, including 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government (Taiwan, Hong Kong, and Macao were excluded due to missing data).

Method - demographic fractions by age and gender in 1979-2020

Age- and gender-specific demographic data by grid cell for each province in China are derived by combining historical demographic data in 1979-2020 with the national population census data provided by the National Statistics Bureau of China.

To combine the national population census data with the historical demographics, we constructed the provincial fractions of demographic in each age groups and each gender according to the fourth, fifth and sixth national population census, which cover the year of 1979-1990, 1991-2000 and 2001-2020, respectively. The provincial fractions can be computed as:

\(\begin{align*} \begin{split} f_{year,province,age,gender}= \left \{ \begin{array}{lr} POP_{1990,province,age,gender}^{4^{th}census}/POP_{1990,province}^{4^{th}census} & 1979\le\mathrm{year}\le1990\\ POP_{2000,province,age,gender}^{5^{th}census}/POP_{2000,province}^{5^{th}census} & 1991\le\mathrm{year}\le2000\\ POP_{2010,province,age,gender}^{6^{th}census}/POP_{2010,province}^{6^{th}census}, & 2001\le\mathrm{year}\le2020 \end{array} \right. \end{split} \end{align*}\)

Where:

- \( f_{\mathrm{year,province,age,gender}}\)is the fraction of population for a given age, a given gender in each province from the national census from 1979-2020.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province,age,gender}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for a given age, a given gender in each province from the X^th national census.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for all ages and both genders in each province from the X^th national census.

Method - demographic totals by age and gender in 1979-2020

The yearly grid population for 1979-1999 are from ISIMIP Histsoc gridded population data, and for 2000-2020 are from the GPWv4 demographic data adjusted by the UN WPP (UN WPP-Adjusted Population Count, v4.11, https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11), which combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP to improve accuracy. These two gridded time series are simply joined at the cut-over date to give a single dataset - historical demographic data covering 1979-2020.

Next, historical demographic data are mapped onto the grid scale to obtain provincial data by using gridded provincial code lookup data and name lookup table. The age- and gender-specific fraction were multiplied by the historical demographic data at the provincial level to obtain the total population by age and gender for per grid cell for china in 1979-2020.

Method - demographic totals and fractions by age and gender in 2010-2100

The grid population count data in 2010-2100 under different shared socioeconomic pathway (SSP) scenarios are drawn from Chen et al. published in Scientific Data with a resolution of 1km (~ 0.008333 degree). We resampled the data to 0.5 degree by aggregating the population count together to obtain the future population data per cell.

This previously published dataset also provided age- and gender-specific population of each provinces, so we calculated the fraction of each age and gender group at provincial level. Then, we multiply the fractions with grid population count to get the total population per age group per cell for each gender.

Note that the projected population data from Chen’s dataset covers 2010-2020, while the historical population in our dataset also covers 2010-2020. The two datasets of that same period may vary because the original population data come from different sources and are calculated based on different methods.

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. Spatial or temporal consistency across dataset boundaries cannot be guaranteed.

This API returns a search for the demographic information for the whole nation.

The list includes 4,250 first names and information on their respective count and proportions across six mutually exclusive racial and Hispanic origin groups. These six categories are consistent with the categories used in the Census Bureau's surname list.

Designed to facilitate analysis of the status of Blacks around the turn of the century, this oversample of Black-headed households in the United States was drawn from the 1910 manuscript census schedules. The sample complements the 1/250 Public Use Sample of the 1910 census manuscripts collected by Samuel H. Preston at the University of Pennsylvania: CENSUS OF POPULATION, 1910 [UNITED STATES]: PUBLIC USE SAMPLE (ICPSR 9166). Part 1, Household Records, contains a record for each household selected in the sample and supplies variables describing the location, type, and composition of the households. Part 2, Individual Records, contains a record for each individual residing in the sampled households and includes information on demographic characteristics, occupation, literacy, nativity, ethnicity, and fertility.

Professional organizations in STEM (science, technology, engineering, and mathematics) can use demographic data to quantify recruitment and retention (R&R) of underrepresented groups within their memberships. However, variation in the types of demographic data collected can influence the targeting and perceived impacts of R&R efforts - e.g., giving false signals of R&R for some groups. We obtained demographic surveys from 73 U.S.-affiliated STEM organizations, collectively representing 712,000 members and conference-attendees. We found large differences in the demographic categories surveyed (e.g., disability status, sexual orientation) and the available response options. These discrepancies indicate a lack of consensus regarding the demographic groups that should be recognized and, for groups that are omitted from surveys, an inability of organizations to prioritize and evaluate R&R initiatives. Aligning inclusive demographic surveys across organizations will provide baseline data that can be used to target and evaluate R&R initiatives to better serve underrepresented groups throughout STEM. Methods We surveyed 164 STEM organizations (73 responses, rate = 44.5%) between December 2020 and July 2021 with the goal of understanding what demographic data each organization collects from its constituents (i.e., members and conference-attendees) and how the data are used. Organizations were sourced from a list of professional societies affiliated with the American Association for the Advancement of Science, AAAS, (n = 156) or from social media (n = 8). The survey was sent to the elected leadership and management firms for each organization, and follow-up reminders were sent after one month. The responding organizations represented a wide range of fields: 31 life science organizations (157,000 constituents), 5 mathematics organizations (93,000 constituents), 16 physical science organizations (207,000 constituents), 7 technology organizations (124,000 constituents), and 14 multi-disciplinary organizations spanning multiple branches of STEM (131,000 constituents). A list of the responding organizations is available in the Supplementary Materials. Based on the AAAS-affiliated recruitment of the organizations and the similar distribution of constituencies across STEM fields, we conclude that the responding organizations are a representative cross-section of the most prominent STEM organizations in the U.S. Each organization was asked about the demographic information they collect from their constituents, the response rates to their surveys, and how the data were used. Survey description The following questions are written as presented to the participating organizations. Question 1: What is the name of your STEM organization? Question 2: Does your organization collect demographic data from your membership and/or meeting attendees? Question 3: When was your organization’s most recent demographic survey (approximate year)? Question 4: We would like to know the categories of demographic information collected by your organization. You may answer this question by either uploading a blank copy of your organization’s survey (linked provided in online version of this survey) OR by completing a short series of questions. Question 5: On the most recent demographic survey or questionnaire, what categories of information were collected? (Please select all that apply)

Disability status Gender identity (e.g., male, female, non-binary) Marital/Family status Racial and ethnic group Religion Sex Sexual orientation Veteran status Other (please provide)

Question 6: For each of the categories selected in Question 5, what options were provided for survey participants to select? Question 7: Did the most recent demographic survey provide a statement about data privacy and confidentiality? If yes, please provide the statement. Question 8: Did the most recent demographic survey provide a statement about intended data use? If yes, please provide the statement. Question 9: Who maintains the demographic data collected by your organization? (e.g., contracted third party, organization executives) Question 10: How has your organization used members’ demographic data in the last five years? Examples: monitoring temporal changes in demographic diversity, publishing diversity data products, planning conferences, contributing to third-party researchers. Question 11: What is the size of your organization (number of members or number of attendees at recent meetings)? Question 12: What was the response rate (%) for your organization’s most recent demographic survey? *Organizations were also able to upload a copy of their demographics survey instead of responding to Questions 5-8. If so, the uploaded survey was used (by the study authors) to evaluate Questions 5-8.

Emergency medical services (EMS) workforce demographics in the United States do not reflect the diversity of the population served. Despite some efforts by professional organizations to create a more representative workforce, little has changed in the last decade. This scoping review aims to summarize existing literature on the demographic composition, recruitment, retention, and workplace experience of underrepresented groups within EMS. Peer-reviewed studies were obtained from a search of PubMed, CINAHL, Web of Science, ProQuest Thesis and Dissertations, and non-peer-reviewed (“gray”) literature from 1960 to present. Abstracts and included full-text articles were screened by two independent reviewers trained on inclusion/exclusion criteria. Studies were included if they pertained to the demographics, training, hiring, retention, promotion, compensation, or workplace experience of underrepresented groups in United States EMS by race, ethnicity, sexual orientation, or gender. Studies of non-EMS fire department activities were excluded. Disputes were resolved by two authors. A single reviewer screened the gray literature. Data extraction was performed using a standardized electronic form. Results were summarized qualitatively. We identified 87 relevant full-text articles from the peer-reviewed literature and 250 items of gray literature. Primary themes emerging from peer-reviewed literature included workplace experience (n = 48), demographics (n = 12), workforce entry and exit (n = 8), education and testing (n = 7), compensation and benefits (n = 5), and leadership, mentorship, and promotion (n = 4). Most articles focused on sex/gender comparisons (65/87, 75%), followed by race/ethnicity comparisons (42/87, 48%). Few articles examined sexual orientation (3/87, 3%). One study focused on telecommunicators and three included EMS physicians. Most studies (n = 60, 69%) were published in the last decade. In the gray literature, media articles (216/250, 86%) demonstrated significant industry discourse surrounding these primary themes. Existing EMS workforce research demonstrates continued underrepresentation of women and nonwhite personnel. Additionally, these studies raise concerns for pervasive negative workplace experiences including sexual harassment and factors that negatively affect recruitment and retention, including bias in candidate testing, a gender pay gap, and unequal promotion opportunities. Additional research is needed to elucidate recruitment and retention program efficacy, the demographic composition of EMS leadership, and the prevalence of racial harassment and discrimination in this workforce.

There are more male LinkedIn users than females – although it is pretty balanced.