BackgroundThe Planetary Health Diet (PHD) is a novel dietary pattern proposed by the EAT-Lancet Commission in 2019, yet a limited study has investigated the anti-aging effects of PHD to date.ObjectivesThis study aimed to explore the association between adherence to PHD, as quantified by the Planetary Health Diet Index (PHDI), and biological aging in American populations.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES) for 1999–2018. Food consumption information was relied on two 24-h diet recall questionnaires. The biological aging condition was comprehensively assessed by four biological markers, including phenotypic age, biological age, telomere length, and klotho concentration. Weighted multivariate linear models, restricted cubic spline (RCS), and subgroup analysis were subsequently carried out to evaluate the influence of PHDI on biological aging.Results44,925 participants with complete data were finally enrolled in our study. The fully adjusted models showed decreased 0.20 years in phenotypic age [−0.20 (−0.31, −0.10)] and declined 0.54 years in biological age [−0.54 (−0.69, −0.38)] correlated with PHDI per 10 scores increment. Klotho concentration [6.2 (1.0, 11.0)] was positively related to PHDI. In Model 2, telomere length increased by 0.02 bp for every 10-point rise in PHDI. Besides, the RCS analysis results exhibited a curvilinear relationship between PHDI and four indicators.ConclusionOur study explored a significant correlation between PHDI and biological aging, indicating that adherence to PHD may prevent biological aging.

Vegetation patterns are strongly influenced by sand mobility in desert ecosystems. However, little is known about the spatial patterns of Artemisia ordosica, a dominant shrub in the Mu Us desert of Northwest China, in relation to sand fixation. The aim of this study was to investigate and contrast the effects of sand dune stabilization on the population and spatial distribution of this desert shrub. Spatial autocorrelation, semi-variance analysis, and point-pattern analysis were used jointly in this study to investigate the spatial patterns of A. ordosica populations on dunes in Yanchi County of Ningxia, China. The results showed that the spatial autocorrelation and spatial heterogeneity declined gradually, and the distance between the clustered individuals shortened following sand dune fixation. Seedlings were more aggregated than adults in all stage of dune stabilization, and both were more aggregated on shifting sand dunes separately. Spatial associations of the seedlings with the adults were mostly positive at distances of 0–5 m in shifting sand dunes, and the spatial association changed from positive to neutral in semi-fixed sand dunes. The seedlings were spaced in an almost random pattern around the adults, and their distances from the adults did not seem to affect their locations in semi-fixed sand dunes. Furthermore, spatial associations of the seedlings with the adults were negative in the fixed sand dune. These findings demonstrate that sand stabilization is an important factor affecting the spatial patterns of A. ordosica populations in the Mu Us desert. These findings suggest that, strong association between individuals may be the mechanism to explain the spatial pattern formation at preliminary stage of dune fixation. Sand dune stabilization can change the spatial pattern of shrub population by weakening the spatial association between native shrub individuals, which may affect the development direction of desert shrubs.

This table contains 324 series, with data for years 2013 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Measures (3 items: Low-frequency hearing loss; High-frequency hearing loss; Speech-frequency hearing loss) Sex (3 items: Both sexes; Males; Females) Age group (6 items: Ages 6 to 79; Ages 6 to 11; Ages 12 to 19; Ages 20 to 39; ...) Categories (2 items: Hearing loss; No hearing loss) Characteristics (3 items: Estimate; Low 95% confidence interval, estimate; High 95% confidence interval, estimate)

This study, designed and carried out by the "http://www.asarb.org/" Target="_blank">Association of Statisticians of American Religious Bodies (ASARB), compiled data on 372 religious bodies by county in the United States. Of these, the ASARB was able to gather data on congregations and adherents for 217 religious bodies and on congregations only for 155. Participating bodies included 354 Christian denominations, associations, or communions (including Latter-day Saints, Messianic Jews, and Unitarian/Universalist groups); counts of Jain, Shinto, Sikh, Tao, Zoroastrian, American Ethical Union, and National Spiritualist Association congregations, and counts of congregations and adherents from Baha'i, three Buddhist groupings, two Hindu groupings, four Jewish groupings, and Muslims. The 372 groups reported a total of 356,642 congregations with 161,224,088 adherents, comprising 48.6 percent of the total U.S. population of 331,449,281. Membership totals were estimated for some religious groups.

In January 2024, the ARDA added 21 religious tradition (RELTRAD) variables to this dataset. These variables start at variable #12 (TOTCNG_2020). Categories were assigned based on pages 88-94 in the original "https://www.usreligioncensus.org/index.php/node/1638" Target="_blank">2020 U.S. Religion Census Report.

Visit the "https://www.thearda.com/us-religion/sources-for-religious-congregations-membership-data" Target="_blank">frequently asked questions page for more information about the ARDA's religious congregation and membership data sources.

City of Mesa population provided by Census Bureau Population Estimates Program (PEP) updated annually as of July 1. See Population and Housing Unit Estimates. Census PEP estimates are used for state revenue sharing per AZ statute (42-5033.01). This dataset is the authoritative source for all city metrics such as Crimes or Traffic Collisions per 1,000 residents.

2025-2040 population projections provided by Maricopa County Association of Governments (MAG) and adopted June 2023. MAG's planning area and incorporated jurisdiction projections are published at 2023 MAG Socioeconomic Projections

Other sources of population estimates include US Census American Community Survey 1-year and 5-year Estimates at https://citydata.mesaaz.gov/d/n5gn-m5c3 and https://citydata.mesaaz.gov/Economic-Development/d/9nqf-ygw6, Arizona Office of Economic Opportunity (OEO) at https://www.azcommerce.com/oeo/population/population-estimates/ (see link for OEO methodology which differs slightly from official US Census Estimates) and City of Mesa Office of Economic Development at https://www.selectmesa.com/business-environment/demographics (ESRI Community Analyst).

These data represent Socioeconomic Projections for the MAG Region by municipal planning area (MPA)*,adopted June 28, 2023, by the MAG Regional Council. An official set of projections is required to be used in transportation, air quality, and water quality management plans, as well as providing the base for all other regional planning activities. Current projections, therefore, are integral for managing future growth. The development of socioeconomic projections requires the collection and merging of a substantial amount of data from varying sources with differing data quality and resolution. These data include the following:Population and Housing: American Community Survey 5-year data (2017-2021),MAG Residential Completions database, County Property Assessment data, MAG/Arizona Department of Administration (ADOA) Annual Population Estimates.Group Quarters (Institutional and Non-institutional): MAG group quarters inventory.Detailed Population Characteristics: American Community Survey (ACS) Public Use Microdata Sample (PUMS) - 5-year data (2017-2021).Employment: MAG Employer Database, county level control totals developed from the Quarterly Census of Employment and Wages and Bureau of Labor Statistics (QCEW/BLS) data.Residential Completions: Current through 2022Q4, submitted and reviewed by MAG member agencies.Existing Land Use: Land use current as of December 2022, reviewed by MAG Population Technical Advisory Committee (POPTAC).Built Space: Maricopa County Assessor’s data current as of July 2022.Future Plans: General Plans current as of December 2022 or later, reviewed by MAG POPTAC.Development Data: data current as of 2023Q2 or later, reviewed by MAG POPTAC.TAZ system: TAZ2021b supplied by MAG Transportation Division.Educational institutions: Inventory of schools from Arizona Department of Education and post high school institutions.Mobile Home and RV Parks: Inventory of mobile home and RV parks.Retirement Areas: Age restricted communities reviewed by MAG POPTAC.Hotels/Motels/Resorts: Inventory of hotels/motels.For full documentation on the model process, please consult the Socioeconomic Projections Documentation: Data, Models, Methods, and Assumptions in the MAG Socioeconomic Projections 2023 on the MAG website at https://www.azmag.gov.These projections were adopted by the MAG Regional Council on June 28, 2023 for the MAG planning area. Areas outside of the MAG planning area are not adopted by the MAG Regional Council, but are prepared on behalf of Central Arizona Governments (CAG) and adopted separately.*Municipal planning areas are determined by the MAG member agencies in consultation with MAG staff. The MPAs identify the anticipated future corporate limits of a city or town.

Note: Sill: The semivariance value at which the variogram levels off. Range: The lag distance at which the semivariogram (or semivariogram component) reaches the sill value. Presumably, autocorrelation is essentially zero beyond the range. Nugget: The nugget represents variability at distances smaller than the typical sample spacing, including measurement error. The spherical model actually reaches the specified sill value, at the specified range. The exponential and Gaussian approach the sill asymptotically, with the practical range, the distance at which the semivariance reaches 95% of the sill value.Semi-variance analysis of A. ordosica populations in the sample plots.

The Population Research Laboratory (PRL), a member of the Association of Academic Survey Research Organizations (AASRO), seeks to advance the research, education and service goals of the University of Alberta by helping academic researchers and policy makers design and implement applied social science research projects. The PRL specializes in the gathering, analysis, and presentation of data about demographic, social and public issues. The PRL research team provides expert consultation and implementation of quantitative and qualitative research methods, project design, sample design, web-based, paper-based and telephone surveys, field site testing, data analysis and report writing. The PRL follows scientifically rigorous and transparent methods in each phase of a research project. Research Coordinators are members of the American Association for Public Opinion Research (AAPOR) and use best practices when conducting all types of research. The PRL has particular expertise in conducting computer-assisted telephone interviews (referred to as CATI surveys). When conducting telephone surveys, all calls are displayed as being from the "U of A PRL", a procedure that assures recipients that the call is not from a telemarketer, and thus helps increase response rates. The PRL maintains a complement of highly skilled telephone interviewers and supervisors who are thoroughly trained in FOIPP requirements, respondent selection procedures, questionnaire instructions, and neutral probing. A subset of interviewers are specially trained to convince otherwise reluctant respondents to participate in the study, a practice that increases response rates and lowers selection bias. PRL staff monitors data collection on a daily basis to allow any necessary adjustments to the volume and timing of calls and respondent selection criteria. The Population Research Laboratory (PRL) administered the 2012 Alberta Survey B. This survey of households across the province of Alberta continues to enable academic researchers, government departments, and non-profit organizations to explore a wide range of topics in a structured research framework and environment. Sponsors' research questions are asked together with demographic questions in a telephone interview of Alberta households. This data consists of the information from 1207 Alberta residence, interviewed between June 5, 2012 and June 27, 2012. The amount of responses indicates that the response rate, as calculated percentages representing the number of people who participated in the survey divided by the number selected in the eligible sample, was 27.6% for survey B. The subject ares included in the 2012 Alberta Survey B includes socio-demographic and background variables such as: household composition, age, gender, marital status, highest level of education, household income, religion, ethnic background, place of birth, employment status, home ownership, political party support and perceptions of financial status. In addition, the topics of public health and injury control, tobacco reduction, activity limitations and personal directives, unions, politics and health.

About this dataset

Context

The dataset tabulates the population of League City by race. It includes the population of League City across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of League City across relevant racial categories.

Key observations

The percent distribution of League City population by race (across all racial categories recognized by the U.S. Census Bureau): 76.94% are white, 8.23% are Black or African American, 0.18% are American Indian and Alaska Native, 6.28% are Asian, 1.44% are some other race and 6.93% are multiracial.

https://i.neilsberg.com/ch/league-city-tx-population-by-race.jpeg" alt="League City population by race">

Content

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

Racial categories include:

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

Variables / Data Columns

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

Notes: S is shifting sand dune, SF is semi-fixed sand dune and F is fixed sand dune.Basic information of the sample plots.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Comprehensive demographic dataset for Mid-Lakewood Civic Association, Lakewood, CO, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

BackgroundMultimorbidity is common, but the prevalence and burden of the specific combinations of coexisting disease has not been systematically examined in the general U.S. adult population.ObjectiveTo identify and estimate the burden of highly prevalent combinations of chronic conditions that are treated among one million or more adults in the United States.MethodsCross-sectional analysis of U.S. households in the Medical Expenditure Panel Survey (MEPS), 2016–2019, a large nationally-representative sample of the community-dwelling population. Association rule mining was used to identify the most common combinations of 20 chronic conditions that have high relevance, impact, and prevalence in primary care. The main measures and outcomes were annual treated prevalence, total medical expenditures, and perceived poor health. Logistic regression models with poor health as the outcome and each multimorbidity combination as the exposure were used to calculate adjusted odds ratios and 95% confidence intervals.ResultsFrequent pattern mining yielded 223 unique combinations of chronic disease, including 74 two-way (dyad), 115 three-way (triad), and 34 four-way combinations that are treated in one million or more U.S. adults. Hypertension-hyperlipidemia was the most common two-way combination occurring in 30.8 million adults. The combination of diabetes-arthritis-cardiovascular disease was associated with the highest median annual medical expenditures ($23,850, interquartile range: $11,593–$44,616), and the combination of diabetes-arthritis-asthma/COPD had the highest age-race-sex adjusted odds ratio of poor self-rated health (adjusted odd ratio: 6.9, 95%CI: 5.4–8.8).ConclusionThis study demonstrates that many multimorbidity combinations are highly prevalent among U.S. adults, yet most research and practice-guidelines remain single disease focused. Highly prevalent and burdensome multimorbidity combinations could be prioritized for evidence-based research on optimal prevention and treatment strategies.

Comprehensive demographic dataset for Crestmont Tenants Association, Bloomington, IN, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for League City, TX, is 33.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for League City, TX, is 16.4.
• Total dependency ratio for League City, TX is 50.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for League City, TX is 6.1.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the League City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the League City for the selected age group is shown in the following column.
• Population (Female): The female population in the League City for the selected age group is shown in the following column.
• Total Population: The total population of the League City for the selected age group is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for League City Population by Age. You can refer the same here

This dataset, named "state_trends.csv," contains information about different U.S. states. Let's break down the attributes and understand what each column represents:

1. state: The name of the U.S. state.
2. state_code: The two-letter postal code abbreviation for the state.
3. population: The population of the state.
4. sq_miles: The total land area of the state in square miles.
5. pop_density: Population density, which is the number of people per square mile.
6. region: The geographical region of the United States to which the state belongs (e.g., South, West).
7. psych_region: A description of the psychological region based on personality traits.
8. psy_reg: A shortened version of the psychological region.
9. extraversion: A measure of the state's population tendency toward extraversion.
10. agreeableness: A measure of the state's population tendency toward agreeableness.
11. conscientiousness: A measure of the state's population tendency toward conscientiousness.
12. neuroticism: A measure of the state's population tendency toward neuroticism.
13. openness: A measure of the state's population tendency toward openness.
14. data_science: A score related to the state's interest or proficiency in the field of data science.
15. artificial_intelligence: A score related to the state's interest or proficiency in artificial intelligence.
16. machine_learning: A score related to the state's interest or proficiency in machine learning.
17. data_analysis: A score related to the state's interest or proficiency in data analysis.
18. business_intelligence: A score related to the state's interest or proficiency in business intelligence.
19. spreadsheet: A score related to the state's interest or proficiency in spreadsheet usage.
20. statistics: A score related to the state's interest or proficiency in statistics.
21. art: A score related to the state's interest or involvement in the field of art.
22. dance: A score related to the state's interest or involvement in dance.
23. museum: A score related to the state's interest or presence of museums.
24. basketball: A score related to the state's interest or involvement in basketball.
25. football: A score related to the state's interest or involvement in football.
26. baseball: A score related to the state's interest or involvement in baseball.
27. soccer: A score related to the state's interest or involvement in soccer.
28. hockey: A score related to the state's interest or involvement in hockey.
29. has_nba: Indicates whether the state has a National Basketball Association (NBA) team (Yes/No).
30. has_nfl: Indicates whether the state has a National Football League (NFL) team (Yes/No).
31. has_mlb: Indicates whether the state has a Major League Baseball (MLB) team (Yes/No).
32. has_mls: Indicates whether the state has a Major League Soccer (MLS) team (Yes/No).
33. has_nhl: Indicates whether the state has a National Hockey League (NHL) team (Yes/No).
34. has_any: Indicates whether the state has any of the mentioned professional sports teams (Yes/No).

In summary, this dataset provides a variety of information about U.S. states, including demographic data, geographical region, psychological region, personality traits, and scores related to interests or proficiencies in various fields such as data science, art, and sports.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about