About this dataset

Context

The dataset tabulates the Sea Bright 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 Sea Bright 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 Sea Bright was 1,425, a 0.49% decrease year-by-year from 2022. Previously, in 2022, Sea Bright population was 1,432, a decline of 0.76% compared to a population of 1,443 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Sea Bright decreased by 374. In this period, the peak population was 1,799 in the year 2000. 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 Sea Bright is shown in this column.
• Year on Year Change: This column displays the change in Sea Bright 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 Sea Bright Population by Year. You can refer the same here

Towns in Time is a compilation of time series data for Victoria's towns covering the years 1981 to 2011. The data is based on Census data collected by the Australian Bureau of Statistics. Towns in Time presents 2011 data for the 2011 definition of each town, together with data under the 2006 definition for 2006 and earlier years. A map showing the difference in the town's boundaries between 2006 and 2011 is attached to each data sheet. It is recommended the user assess this concordance when using time series data.

About this dataset

Context

The dataset tabulates the Sea Bright population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Sea Bright. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,110 (66.99% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Sea Bright population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Sea Bright is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Sea Bright 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 Sea Bright Population by Age. You can refer the same here

U.S. Census Bureau QuickFacts statistics for Bright CDP, Indiana. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

This catalog presents new radial velocity determinations, based on 2nm/mm Coude plates, for 146 southern stars brighter than m(v)=8.3. Drawn from the catalog of uvby-{beta} photometry of southern A5-G0 stars by Olsen (1983, Cat. II/90), the program stars are certain or suspected Population II stars. One triple-lined and 10 double-lined binaries have been detected, including HD 210737, for which a preliminary orbit has been derived. Notes on spectral peculiarities are given. The catalog is in two files. The first file lists HD number, heliocentric Julian date of observation, radial velocity with error and number of lines observed for both the primary and secondary stars, rotation class, and remarks for each observation. The second file contains additional remarks to the data of the first file, sorted by HD number.

The 2014 Town and Community Profiles bring together information on more than 1000 Victorian communities from a wide variety of sources, both internal and external to the Department of Health and Department of Human Services. The Profiles include information on population, geography, services and facilities, and social, cultural and demographic characteristics of each suburb, town and rural catchment in Victoria.

This map shows where youth populations are found throughout the world. Areas with more than 33% youth are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data arefromMichael Bauer Researchwith the exception of the following countries:Australia:Esri AustraliaandMapData ServicesCanada:Esri CanadaandEnvironicsFrance:Esri FranceGermany:Esri GermanyandNexigaIndia:Esri IndiaandIndicusJapan:Esri JapanSouth Korea:Esri KoreaandOPENmateSpain:Esri EspaaandAISUnited States:Esri Demographics

About this dataset

Context

The dataset tabulates the data for the Sea Bright, NJ population pyramid, which represents the Sea Bright population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Sea Bright, NJ, is 14.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Sea Bright, NJ, is 34.9.
• Total dependency ratio for Sea Bright, NJ is 49.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Sea Bright, NJ is 2.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Sea Bright population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sea Bright for the selected age group is shown in the following column.
• Population (Female): The female population in the Sea Bright for the selected age group is shown in the following column.
• Total Population: The total population of the Sea Bright 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 Sea Bright Population by Age. You can refer the same here

We construct a catalogue of the optically bright post-AGB stars in the LMC. The sample forms an ideal testbed for stellar evolution theory predictions of the final phase of low- and intermediate-mass stars, because the distance and hence luminosity and also the current and initial mass of these objects is well constrained.

This paper describes the analysis of population data typed using the Promega PowerPlex 21 multiplex for the three major sub populations within Australia. Samples from 1427 declared Australian Aboriginal, 546 Pure Aboriginals from the Northern Territory, 990 Asian, and 1707 Caucasian individuals representing were analysed. Departures from Hardy–Weinberg equilibrium (HWE) and linkage equilibrium (LE) were assessed using exact tests. The Aboriginal populations were shown to display significant departures from equilibrium. All four subpopulation databases are of suitable size for the purpose of estimating allele frequencies.

This map shows where senior populations are found throughout the world. Areas with more than 10% seniors are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

The 2014 Town and Community Profiles bring together information on more than 1000 Victorian communities from a wide variety of sources, both internal and external to the Department of Health and Department of Human Services. The Profiles include information on population, geography, services and facilities, and social, cultural and demographic characteristics of each suburb, town and rural catchment in Victoria.

The 2014 Town and Community Profiles bring together information on more than 1000 Victorian communities from a wide variety of sources, both internal and external to the Department of Health and …Show full descriptionThe 2014 Town and Community Profiles bring together information on more than 1000 Victorian communities from a wide variety of sources, both internal and external to the Department of Health and Department of Human Services. The Profiles include information on population, geography, services and facilities, and social, cultural and demographic characteristics of each suburb, town and rural catchment in Victoria.

We have performed a census of the UV-bright population in 78 globular clusters using wide-field UV telescopes. This population includes a variety of phases of post-horizontal branch (HB) evolution, including hot post-asymptotic giant branch (AGB) stars, and post-early AGB stars. There are indications that old stellar systems like globular clusters produce fewer post-(early) AGB stars than currently predicted by evolutionary models, but observations are still scarce. We wish to derive effective temperatures, surface gravities, and helium abundances of the luminous hot UV-bright stars in these clusters to determine their evolutionary status and compare the observed numbers to predictions from evolutionary theory. We obtained FORS2 spectroscopy of 11 of these UV-selected objects (covering a range of -2.3<[Fe/H]<-1.0), which we (re-)analysed together with previously observed data. We used model atmospheres of different metallicities, including super-solar ones. Where possible, we verified our atmospheric parameters using UV spectrophotometry and searched for metal lines in the optical spectra. We calculated evolutionary sequences for four metallicity regimes and used them together with information about the HB morphology of the globular clusters to estimate the expected numbers of post-AGB stars. We find that metal-rich model spectra are required to analyse stars hotter than 40000 K. Seven of the eleven new luminous UV-bright stars are post-AGB or post-early AGB stars, two are evolving away from the HB, one is a foreground white dwarf, and another is a white dwarf merger. Taking into account published information on other hot UV-bright stars in globular clusters, we find that the number of observed hot post-AGB stars generally agrees with the predicted values, although the numbers are still low. Spectroscopy is clearly required to identify the evolutionary status of hot UV-bright stars. For hotter stars, metal-rich model spectra are required to reproduce their optical and UV spectra, which may affect the flux contribution of hot post-AGB stars to the UV spectra of evolved populations. Adding published information on other hot UV-bright stars in globular clusters, we find that the number of observed hot post-AGB stars generally agrees with the predicted values, although the numbers are still low.

This map shows whether an area has a predominant daytime or nighttime population. Areas in blue have more residential population (nighttime population) while the bright pink areas have more people who travel there in the daytime (daytime population). The size of the features is the sum of daytime and nighttime population. This shows the overall quantity of people who are in an area each 24-hour period. The transparency of the features displays the strength of the predominance. This means that features with stronger color are largely predominant. More transparent features show that the daytime and nighttime populations are similar, so the predominant population may not be predominant by much. The popup is configured to provide more information about the daytime/nighttime and resident/worker populations in an area.Data source: Esri 2016 Updated USA Demographics

VizieR Online Data Catalog: Radial Velocities of Bright Population II F Stars(Andersen J.+, 1985)

The coastal zones of Small Island States are hotspots of human habitation and economic endeavour. In the Pacific region, as elsewhere, there are large gaps in understandings of the exposure and vulnerability of people in coastal zones. The 22 Pacific Countries and Territories (PICTs) are poorly represented in global analyses of vulnerability to seaward risks. We combine several data sources to estimate populations to zones 1, 5 and 10 km from the coastline in each of the PICTs. Regional patterns in the proximity of Pacific people to the coast are dominated by Papua New Guinea. Overall, ca. half the population of the Pacific resides within 10 km of the coast but this jumps to 97% when Papua New Guinea is excluded. A quarter of Pacific people live within 1 km of the coast, but without PNG this increases to slightly more than half. Excluding PNG, 90% of Pacific Islanders live within 5 km of the coast. All of the population in the coral atoll nations of Tokelau and Tuvalu live within a km of the ocean. Results using two global datasets, the SEDAC-CIESIN Gridded Population of the World v4 (GPWv4) and the Oak Ridge National Laboratory Landscan differed: Landscan under-dispersed population, overestimating numbers in urban centres and underestimating population in rural areas and GPWv4 over-dispersed the population. In addition to errors introduced by the allocation models of the two methods, errors were introduced as artefacts of allocating households to 1 km x 1 km grid cell data (30 arc–seconds) to polygons. The limited utility of LandScan and GPWv4 in advancing this analysis may be overcome with more spatially resolved census data and the inclusion of elevation above sea level as an important dimension of vulnerability.

About this dataset

Context

The dataset tabulates the population of Sea Bright by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sea Bright. The dataset can be utilized to understand the population distribution of Sea Bright by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sea Bright. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Sea Bright.

Key observations

Largest age group (population): Male # 50-54 years (144) | Female # 55-59 years (110). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Sea Bright population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sea Bright is shown in the following column.
• Population (Female): The female population in the Sea Bright is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Sea Bright for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Using an innovative approach with Geographic Information Systems and Remote Sensing, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available and represents an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region.

Using an innovative approach that combines geospatial science, remote sensing technology, and machine learning algorithms, LandScan Global is a global population distribution data, at 30 arc seconds (roughly 1km at equator), representing an ambient (24 hour average) population. The LandScan Global algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.