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.

About the Socio-Demographic Data Dashboards The U.S. Department of Housing and Urban Development (HUD) has created interactive dashboards that display maps, tables, and charts of local housing, economic, and demographic conditions for each FY23 HUD Jurisdiction. These dashboards can help local governments, Public Housing Agencies (PHAs), states, and community organizations assess housing needs, prepare HUD reports, and advance local housing, and community development goals. The dashboards use data from the 2017-2021 5-Year American Community Survey, the Department of Transportation ETC Explorer, the Environmental Protection Agency Facility Registry Service, internal HUD administrative data, and other sources. For this dashboard, a jurisdiction is defined as any PHA or unit of local government (city, township, county, municipality, etc.) receiving HUD funding through the Community Development Block Grant (CDBG), Emergency Solutions Grant (ESG), Housing Opportunities for Persons with AIDS (HOPWA), or the HOME Investment Partnerships Program. HUD specific jurisdiction boundaries will likely differ from official U.S. Census geographic boundaries, so users should prioritize using HUD-defined jurisdictions in reports.

The Consumer Demographic database is comprised of over 80 sources and includes over 400 different data points for each individual in a household with complete PII. The fields provided include demographics, psychographic, lifestyle criteria, buying behavior, and real property identification.

Each record is ranked by confidence and only the highest quality data is used. The database is multi-sourced and contains both compiled and originated U.S. data. Additionally, the data goes through intensive cleansing including deceased processing and NCOA.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Please note this page provides neighborhood demographic data using 2010 Census tracts. For updated Neighborhood Demographics using 2020 Census tracts consistently across historical years, please refer to the Planning Department Research Division's Exploring Neighborhood Change Tool. The tool visualizes demographic, economic, and housing data for Boston's tracts and neighborhoods from 1950 to 2025 (with projections to 2035) using the most up-to-date 2020 Census tract-based Neighborhood boundaries.

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.

These are the data for displayed in the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2024, but display data from 2022 and 2023. Most data are from the 2022 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for: Total Population - City of Austin Planning (2023) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (2022) Occupied Housing Units - City of Austin Planning (2023) Median Home Closing Price - Austin Board of Realtors (2023) Average Monthly Rent - Austin Investor Interests (Q4 2023) Income Restricted Units - City of Austin Affordable Housing Inventory Housing Units-City of Austin Planning (2023) Population Density - Esri Updated Demographics Daytime Population Density - Esri Updated Demographics Selected Land Use Percentages - City of Austin Land Use Inventory Transit Stops - Capital Metro (2023) City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates. More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics. These profiles are updated annually. City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

• This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer.- Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA.- See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 52.97% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Parks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Parks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Parks Population by Gender. You can refer the same here

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.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

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Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

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• Standardized and unified demographic data structure

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Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

About this dataset

Context

The dataset tabulates the San Clemente population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of San Clemente across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of San Clemente was 62,313, a 0.99% decrease year-by-year from 2022. Previously, in 2022, San Clemente population was 62,933, a decline of 1.10% compared to a population of 63,634 in 2021. Over the last 20 plus years, between 2000 and 2023, population of San Clemente increased by 11,664. In this period, the peak population was 65,367 in the year 2016. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the San Clemente is shown in this column.
• Year on Year Change: This column displays the change in San Clemente population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 San Clemente Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Lake View population by year. The dataset can be utilized to understand the population trend of Lake View.

Content

The dataset constitues the following datasets

• Lake View, AR Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

Geolocet offers a rich repository of European demographic data, providing you with a robust foundation for data-driven decisions. Our datasets encompass a diverse range of attributes, but it's important to note that the attributes available may vary significantly from country to country. This variation reflects the unique demographic reporting standards and data availability in each region.

Attributes include essential demographic factors such as Age Bands, Gender, and Marital Status, as a minimum. In some countries, we provide cross-referenced attributes, such as Marital Status per Age Band, Marital Status per Gender, or even intricate combinations like Marital Status per Gender and Age. Additionally, for select countries, we offer insights into income, employment status, household composition, housing status, and many more.

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Our demographic data is derived exclusively from official census sources, ensuring the highest level of accuracy and reliability. We take pride in using data that is available under open licenses for commercial use. However, it's important to note that our data is not a direct representation of the original census data. Instead, we use this source data to create comprehensive demographic models that are tailored to your needs.

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Abstract This article intends to conduct a dialogue between Demography and History, combining the specific knowledge of these disciplines in order to better understand both the numbers collected for the population of Rio Grande do Norte and the historical context which would explain those numbers. The purpose of this article is to present socio-demographic characteristics of the Rio Grande do Norte’s population at the turn of the eighteenth century to the nineteenth, through the first population maps created in the second half of the eighteenth century, as part of the Pombal policy attempt to better understand the Portuguese empire. Furthermore, this paper seeks to present some demographic data, such as population composition by sex, color / ethnicity and legal status, of the Rio Grande do Norte captaincy, based on data produced in 1782, 1788, 1801 and 1805, using Demography methodologies which will be explained through direct dialogue with the area´s historiography.

Each year Eurostat collects demographic data at regional level from EU, EFTA and Candidate countries as part of the Population Statistics data collection. POPSTAT is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration at national and regional levels by various breakdowns (for the full overview see the Eurostat dedicated section). More specifically, POPSTAT collects data at regional levels on:

• population stocks;
• vital events (live births and deaths).

Each country must send the statistics for the reference year (T) to Eurostat by 31 December of the following calendar year (T+1). Eurostat then publishes the data in March of the calendar year after that (T+2).

Demographic data at regional level include statistics on the population at the end of the calendar year and on live births and deaths during that year, according to the official classification for statistics at regional level (NUTS - nomenclature of territorial units for statistics) in force in the year. These data are broken down by NUTS 2 and 3 levels for EU countries. For more information on the NUTS classification and its versions please refer to the Eurostat dedicated pages. For EFTA and Candidate countries the data are collected according to the agreed statistical regions that have been coded in a way that resembles NUTS.

The breakdown of demographic data collected at regional level varies depending on the NUTS/statistical region level. These breakdowns are summarised below, along with the link to the corresponding online table:

NUTS 2 level

• Population by sex, age and region of residence — demo_r_d2jan
• Population on 1 January by age group, sex and region of residence — demo_r_pjangroup
• Live births by mother's age, mother's year of birth and mother's region of residence — demo_r_fagec
• Deaths by sex, age, and region of residence — demo_r_magec

NUTS 3 level

• Population on 1 January by sex, age group and region of residence — demo_r_pjangrp3
• Population on 1 January by broad age group, sex and region of residence — demo_r_pjanaggr3
• Live births (total) by region of residence — demo_r_births
• Live births by five-year age group of the mothers and region of residence — demo_r_fagec3
• Deaths (total) by region of residence — demo_r_deaths
• Deaths by five-year age group, sex and region of residence — demo_r_magec3

This more detailed breakdown (by five-year age group) of the data collected at NUTS 3 level started with the reference year 2013 and is in accordance with the European laws on demographic statistics. In addition to the regional codes set out in the NUTS classification in force, these online tables include few additional codes that are meant to cover data on persons and events that cannot be allocated to any official NUTS region. These codes are denoted as CCX/CCXX/CCXXX (Not regionalised/Unknown level 1/2/3; CC stands for country code) and are available only for France, Hungary, North Macedonia and Albania, reflecting the raw data as transmitted to Eurostat.

For the reference years from 1990 to 2012 all countries sent to Eurostat all the data on a voluntary basis, therefore the completeness of the tables and the length of time series reflect the level of data received from the responsible National Statistical Institutes’ (NSIs) data provider. As a general remark, a lower data breakdown is available at NUTS 3 level as detailed:

• population data are broken down by sex and broad age groups (0-14, 15-64 and 65 or more). The data have this disaggregation since the reference year 2007 for all countries, and even longer for some — demo_r_pjanaggr3
• vital events (live births and deaths) data are available only as totals, without any further breakdown — demo_r_births and demo_r_deaths

Demographic indicators are calculated by Eurostat based on the above raw data using a common methodology for all countries and regions. The regional demographic indicators computed by NUTS level and the corresponding online tables are summarised below:

NUTS 2 level

• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind2
• Fertility indicators by region of residence — demo_r_find2
• Fertility rates by age and region of residence — demo_r_frate2
• Life table by age, sex and region of residence — demo_r_mlife
• Life expectancy by age, sex and region of residence — demo_r_mlifexp
• Infant mortality rates by region of residence — demo_r_minfind

NUTS 3 level

• Population change - Demographic balance and crude rates at regional level — demo_r_gind3
• Population density by region — demo_r_d3dens
• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind3
• Fertility indicators by region of residence (total fertility rate, mean age of woman at childbirth and median age of woman at childbirth) — demo_r_find3

Notes:

1) All the indicators are computed for all lower NUTS regions included in the tables (e.g. data included in a table at NUTS 3 level will include also the data for NUTS 2, 1 and country levels).

2) Demographic indicators computed by NUTS 2 and 3 levels are calculated using input data that have different age breakdown. Therefore, minor differences can be noted between the values corresponding to the same indicator of the same region classified as NUTS 2, 1 or country level.

3) Since the reference year 2015, Eurostat has stopped collecting data on area; therefore, the table 'Area by NUTS 3 region (demo_r_d3area)' includes data up to the year 2015 included.

4) Starting with the reference year 2016, the population density indicator is computed using the new data on area 'Area by NUTS 3 region (reg_area3).

Each year Eurostat collects demographic data at regional level from EU, EFTA and Candidate countries as part of the Population Statistics data collection. POPSTAT is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration at national and regional levels by various breakdowns (for the full overview see the Eurostat dedicated section). More specifically, POPSTAT collects data at regional levels on:

• population stocks;
• vital events (live births and deaths).

Each country must send the statistics for the reference year (T) to Eurostat by 31 December of the following calendar year (T+1). Eurostat then publishes the data in March of the calendar year after that (T+2).

Demographic data at regional level include statistics on the population at the end of the calendar year and on live births and deaths during that year, according to the official classification for statistics at regional level (NUTS - nomenclature of territorial units for statistics) in force in the year. These data are broken down by NUTS 2 and 3 levels for EU countries. For more information on the NUTS classification and its versions please refer to the Eurostat dedicated pages. For EFTA and Candidate countries the data are collected according to the agreed statistical regions that have been coded in a way that resembles NUTS.

The breakdown of demographic data collected at regional level varies depending on the NUTS/statistical region level. These breakdowns are summarised below, along with the link to the corresponding online table:

NUTS 2 level

• Population by sex, age and region of residence — demo_r_d2jan
• Population on 1 January by age group, sex and region of residence — demo_r_pjangroup
• Live births by mother's age, mother's year of birth and mother's region of residence — demo_r_fagec
• Deaths by sex, age, and region of residence — demo_r_magec

NUTS 3 level

• Population on 1 January by sex, age group and region of residence — demo_r_pjangrp3
• Population on 1 January by broad age group, sex and region of residence — demo_r_pjanaggr3
• Live births (total) by region of residence — demo_r_births
• Live births by five-year age group of the mothers and region of residence — demo_r_fagec3
• Deaths (total) by region of residence — demo_r_deaths
• Deaths by five-year age group, sex and region of residence — demo_r_magec3

This more detailed breakdown (by five-year age group) of the data collected at NUTS 3 level started with the reference year 2013 and is in accordance with the European laws on demographic statistics. In addition to the regional codes set out in the NUTS classification in force, these online tables include few additional codes that are meant to cover data on persons and events that cannot be allocated to any official NUTS region. These codes are denoted as CCX/CCXX/CCXXX (Not regionalised/Unknown level 1/2/3; CC stands for country code) and are available only for France, Hungary, North Macedonia and Albania, reflecting the raw data as transmitted to Eurostat.

For the reference years from 1990 to 2012 all countries sent to Eurostat all the data on a voluntary basis, therefore the completeness of the tables and the length of time series reflect the level of data received from the responsible National Statistical Institutes’ (NSIs) data provider. As a general remark, a lower data breakdown is available at NUTS 3 level as detailed:

• population data are broken down by sex and broad age groups (0-14, 15-64 and 65 or more). The data have this disaggregation since the reference year 2007 for all countries, and even longer for some — demo_r_pjanaggr3
• vital events (live births and deaths) data are available only as totals, without any further breakdown — demo_r_births and demo_r_deaths

Demographic indicators are calculated by Eurostat based on the above raw data using a common methodology for all countries and regions. The regional demographic indicators computed by NUTS level and the corresponding online tables are summarised below:

NUTS 2 level

• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind2
• Fertility indicators by region of residence — demo_r_find2
• Fertility rates by age and region of residence — demo_r_frate2
• Life table by age, sex and region of residence — demo_r_mlife
• Life expectancy by age, sex and region of residence — demo_r_mlifexp
• Infant mortality rates by region of residence — demo_r_minfind

NUTS 3 level

• Population change - Demographic balance and crude rates at regional level — demo_r_gind3
• Population density by region — demo_r_d3dens
• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind3
• Fertility indicators by region of residence (total fertility rate, mean age of woman at childbirth and median age of woman at childbirth) — demo_r_find3

Notes:

1) All the indicators are computed for all lower NUTS regions included in the tables (e.g. data included in a table at NUTS 3 level will include also the data for NUTS 2, 1 and country levels).

2) Demographic indicators computed by NUTS 2 and 3 levels are calculated using input data that have different age breakdown. Therefore, minor differences can be noted between the values corresponding to the same indicator of the same region classified as NUTS 2, 1 or country level.

3) Since the reference year 2015, Eurostat has stopped collecting data on area; therefore, the table 'Area by NUTS 3 region (demo_r_d3area)' includes data up to the year 2015 included.

4) Starting with the reference year 2016, the population density indicator is computed using the new data on area 'Area by NUTS 3 region (reg_area3).

In cooperative breeding systems, inclusive fitness theory predicts that non-breeding helpers more closely related to the breeders should be more willing to provide costly alloparental care, and thus have more impact on breeder fitness. In the red-cockaded woodpecker (Dryobates borealis), most helpers are the breeders’ earlier offspring, but helpers do vary within groups in both relatedness to the breeders (some even being unrelated) and sex, and it can be difficult to parse their separate impacts on breeder fitness. Moreover, most support for inclusive fitness theory has been positive associations between relatedness and behavior, rather than actual fitness consequences. We used functional linear models to evaluate the per capita effects of helpers of different relatedness on eight breeder fitness components measured for up to 41 years at three sites. In support of inclusive fitness theory, helpers more related to the breeding pair made greater contributions to six fitness components. However, male helpers made equal contributions to increasing pre-fledging survival regardless of relatedness. These findings suggest that both inclusive fitness benefits and other, direct benefits may underlie helping behaviors in the red-cockaded woodpecker. Our results also demonstrate the application of an underused statistical approach to disentangle a complex ecological phenomenon. Methods We used long-term demographic monitoring data collected over 28 to 41 consecutive years at three sites: the Sandhills region in south-central North Carolina (1980–2020), Marine Corps Base Camp Lejeune on the central coast of North Carolina (1986–2020), and Eglin Air Force Base in the western panhandle of Florida (1993–2020). Monitoring methods are described in detail by Walters et al. (1988) (see also Appendix A for more details on monitoring). See Walters and Garcia (2016) for how individuals are assigned breeder and helper status.

GapMaps premium demographic data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

Demographic Data attributes include:

Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for AGS Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

This feature layer provides Esri 2018 demographic estimates for popular variables including: 2018 Total Population, 2018 Household Population, 2018 Median Age, 2018 Median Household Income, 2018 Per Capita Income, 2018 Diversity Index and many more. Data is available from country, state, county, ZIP Code, tract, and block group level with adjustable scale visibility. It is intended as a beta sample feature service to demonstrate smart mapping capabilities with Esri's Demographic data.Example feature views and web maps built from this layer include:Predominant Generations in the United StatesUnemployment in the United StatesMedian Home Value and IncomePopulation Growth or Decline?For more information, visit the Updated Demographics documentation. For a full list of variables, click the Data tab.

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Eurostat’s annual data collections on population are structured as follows:

POPSTAT Population statistics data collection: The most in-depth annual national and regional demographic and migration data collection. The data relate to populations, births, deaths, immigrants, emigrants, marriages and divorces, and is broken down into several categories (Article 3 of Regulation (EU) No 1260/2013 and Article 3 of Regulation (EU) No 862/2007).

URESPOP Usually resident population: Usually resident population for the purpose of the qualified majority voting.
Formore specific information, please see the metadata on Usually resident population (Article 4 of Regulation (EU) No 1260/2013).

Member States send population data to Eurostat data as on of 31 December for the reference year under http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32013R1260&from=EN" target="_blank">Regulation 1260/2013 on European demographic statistics. The data are conventionally published by Eurostat as population on 1 January of the following year (reference year + 1).

The aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate — legal acts and other agreements’.

The completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes.

For more information on mandatory/voluntary data collection, see 6.1. Institutional mandate — legal acts and other agreements.

The following statistics are available.

Population on 1 January by sex and by:

• single age and educational attainment / marital status / broad group of citizenship / broad group of country of birth;
• five-year age group and citizenship / country of birth;
• citizenship and broad group of country of birth / country of birth and broad group of citizenship;
• broad age group and NUTS 3 (under regional data population folder);
• single age and NUTS 2 (under regional data population folder);
• five-year age group and NUTS 2 / NUTS 3 (under regional data population folder).

Population structure statistics: median age of population, proportion of population by various age groups, old age dependency ratio.