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Demographic data are important to wildlife managers to gauge population health, to allow populations to be utilised sustainably, and to inform conservation efforts. We analysed published demographic data on the world’s wildfowl to examine taxonomic and geographic biases in study, and to identify gaps in knowledge. Wildfowl (order: Anseriformes) are a comparatively well studied bird group which includes 169 species of duck, goose and swan. In all, 1,586 wildfowl research papers published between 1911 and 2010 were found using Web of Knowledge (WoK) and Google Scholar. Over half of the research output involved just 15 species from seven genera. Research output was strongly biased towards ‘high income’ countries, common wildfowl species, and measures of productivity, rather than survival and movement patterns. There were significantly fewer demographic data for the world’s 31 threatened wildfowl species than for non-threatened species. Since 1994, the volume of demographic work on threatened species has increased more than for non-threatened species, but still makes up only 2.7% of total research output. As an aid to research prioritisation, a metric was created to reflect demographic knowledge gaps for each species related to research output for the species, its threat status, and availability of potentially useful surrogate data from congeneric species. According to the metric, the 25 highest priority species include thirteen threatened taxa and nine species each from Asia and South America, and six from Africa.
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TwitterThe table MT- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 677876 rows across 699 variables.
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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.
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
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.
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/.
This dataset is a part of the main dataset for Parks Population by Gender. You can refer the same here
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TwitterA 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)
Ad targeting
B2B Market Intelligence
Customer analytics
Real Estate Data Estimations
Marketing campaign analysis
Demand forecasting
Sales territory mapping
Retail site selection
Reporting
Audience targeting
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)
Changes in population density
Urbanization Patterns
Accurate at zip code and administrative level
Optimized for easy integration
Easy customization
Global coverage
Updated yearly
Standardized and reliable
Self-hosted delivery
Fully aggregated (ready to use)
Rich attributes
Why do companies choose our demographic databases
Standardized and unified demographic data structure
Seamless integration in your system
Dedicated location data expert
Note: Custom population data packages are available. Please submit a request via the above contact button for more details.
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TwitterThe table NE- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 1182076 rows across 699 variables.
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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.
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| BASE YEAR | 2024 |
| HISTORICAL DATA | 2019 - 2023 |
| REGIONS COVERED | North America, Europe, APAC, South America, MEA |
| REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
| MARKET SIZE 2024 | 7.05(USD Billion) |
| MARKET SIZE 2025 | 7.55(USD Billion) |
| MARKET SIZE 2035 | 15.0(USD Billion) |
| SEGMENTS COVERED | Data Type, Application, Source, End User, Regional |
| COUNTRIES COVERED | US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA |
| KEY MARKET DYNAMICS | data privacy concerns, demand for personalized services, growth of smart home technology, integration of AI analytics, increasing subscription models |
| MARKET FORECAST UNITS | USD Billion |
| KEY COMPANIES PROFILED | Avenue6, HouseCanary, CoreLogic, D.R. Horton, Verisk Analytics, RealPage, IHS Markit, Lennar Corporation, Toll Brothers, PulteGroup, KB Home, S&P Global, Zonda, CoStar Group, TRI Pointe Group, Owens Corning |
| MARKET FORECAST PERIOD | 2025 - 2035 |
| KEY MARKET OPPORTUNITIES | Rising demand for smart home analytics, Increased focus on personalized marketing strategies, Growth of IoT integration in homes, Expansion of online home service platforms, Enhanced data security solutions for homeowners |
| COMPOUND ANNUAL GROWTH RATE (CAGR) | 7.1% (2025 - 2035) |
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TwitterThe Ministry of Health and Social Welfare (MOHSW) initiated the 2004 Lesotho Demographic and Health Survey (LDHS) to collect population-based data to inform the Health Sector Reform Programme (2000-2009). The 2004 LDHS will assist in monitoring and evaluating the performance of the Health Sector Reform Programme since 2000 by providing data to be compared with data from the first baseline survey, which was conducted when the reform programme began. The LDHS survey will also provide crucial information to help define the targets for Phase II of the Health Sector Reform Programme (2005-2008). Additionally, the 2004 LDHS results will serve as the main source of key demographic indicators in Lesotho until the 2006 population census results are available.
The LDHS was conducted using a representative sample of women and men of reproductive age.
The specific objectives were to: - Provide data at national and district levels that allow the determination of demographic indicators, particularly fertility and childhood mortality rates; - Measure changes in fertility and contraceptive use and at the same time analyse the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding patterns, and important social and economic factors; - Examine the basic indicators of maternal and child health in Lesotho, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and immunisation coverage for children; - Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS, other sexually transmitted infections, and tuberculosis; - Estimate adult and maternal mortality ratios at the national level; - Estimate the prevalence of anaemia among children, women and men, and the prevalence of HIV among women and men at the national and district levels.
National
Sample survey data
The sample for the 2004 LDHS covered the household population. A representative probability sample of more than 9,000 households was selected for the 2004 LDHS sample. This sample was constructed to allow for separate estimates for key indicators in each of the ten districts in Lesotho, as well as for urban and rural areas separately.
The survey utilized a two-stage sample design. In the first stage, 405 clusters (109 in the urban and 296 in the rural areas) were selected from a list of enumeration areas from the 1996 Population Census frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey.
All women age 15-49 who were either permanent household residents in the 2004 LDHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, in every second household selected for the survey, all men age 15-59 years were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. In the households selected for the men's survey, height and weight measurements were taken for eligible women and children under five years of age. Additionally, eligible women, men, and children under age five were tested in the field for anaemia, and eligible women and men were asked for an additional blood sample for anonymous testing for HIV.
Note: See detailed sample implementation in the APPENDIX A of the final 2004 Lesotho Demographic and Health Survey Final Report.
Face-to-face
Three questionnaires were used for the 2004 LDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. To reflect relevant issues in population and health in Lesotho, the questionnaires were adapted during a series of technical meetings with various stakeholders from government ministries and agencies, nongovernmental organizations and international donors. The final draft of the questionnaire was discussed at a large meeting of the LDHS Technical Committee organized by the MOHSW and BOS. The adapted questionnaires were translated from English into Sesotho and pretested during June 2004.
The Household Questionnaire was used to list all of the usual members and visitors in the selected households. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Some basic information was also collected on the characteristics of each person listed, including age, sex, education, residence and emigration status, and relationship to the head of the household. For children under 18, survival status of the parents was determined. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and access to health facilities. For households selected for the male survey subsample, the questionnaire was used to record height, weight, and haemoglobin measurements of women, men and children, and the respondents’ decision about whether to volunteer to give blood samples for HIV.
The Women’s Questionnaire was used to collect information from all women age 15-49. The women were asked questions on the following topics: - Background characteristics (education, residential history, media exposure, etc.) - Birth history and childhood mortality - Knowledge and use of family planning methods - Fertility preferences - Antenatal and delivery care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Awareness and behaviour regarding AIDS, other sexually transmitted infections (STIs), and tuberculosis (TB) - Maternal mortality
The Men’s Questionnaire was administered to all men age 15-59 living in every other household in the 2004-05 LDHS sample. The Men’s Questionnaire collected much of the same information found in the Women’s Questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health, nutrition, and maternal mortality.
Geographic coordinates were collected for each EA in the 2004 LDHS.
The processing of the 2004 LDHS results began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to BOS headquarters, where they were entered and edited by data processing personnel who were specially trained for this task. The data processing personnel included two supervisors, two questionnaire administrators/office editors-who ensured that the expected number of questionnaires from each cluster was received-16 data entry operators, and two secondary editors. The concurrent processing of the data was an advantage because BOS was able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in May 2005.
Response rates are important because high non-response may affect the reliability of the results. A total of 9,903 households were selected for the sample, of which 9,025 were found to be occupied during data collection. Of the 9,025 existing households, 8,592 were successfully interviewed, yielding a household response rate of 95 percent.
In these households, 7,522 women were identified as eligible for the individual interview. Interviews were completed with 94 percent of these women. Of the 3,305 eligible men identified, 85 percent were successfully interviewed. The response rate for urban women and men is somewhat higher than for rural respondents (96 percent compared with 94 percent for women and 88 percent compared with 84 percent for men). The principal reason for non-response among eligible women and men was the failure to find individuals at home despite repeated visits to the household. The lower response rate for men reflects the more frequent and longer absences of men from the household, principally because of employment and life style.
Response rates for the HIV testing component were lower than those for the interviews.
See summarized response rates in Table 1.2 of the Final Report.
The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2004 Lesotho Demographic and Health Survey (LSDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2004 LSDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield
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Context
The dataset tabulates the Waco population by year. The dataset can be utilized to understand the population trend of Waco.
The dataset constitues the following datasets
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.
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/.
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The dataset tabulates the Azusa population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Azusa. The dataset can be utilized to understand the population distribution of Azusa by age. For example, using this dataset, we can identify the largest age group in Azusa.
Key observations
The largest age group in Azusa, CA was for the group of age 20 to 24 years years with a population of 4,973 (10.08%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Azusa, CA was the 85 years and over years with a population of 407 (0.83%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Azusa Population by Age. You can refer the same here
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TwitterA computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490
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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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for San Clemente Population by Year. You can refer the same here
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The dataset tabulates the United States 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 United States 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 2022, the population of United States was 333,287,557, a 0.38% increase year-by-year from 2021. Previously, in 2021, United States population was 332,031,554, an increase of 0.16% compared to a population of 331,511,512 in 2020. Over the last 20 plus years, between 2000 and 2022, population of United States increased by 51,125,146. In this period, the peak population was 333,287,557 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for United States Population by Year. You can refer the same here
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The dataset tabulates the Brownstown population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Brownstown. The dataset can be utilized to understand the population distribution of Brownstown by age. For example, using this dataset, we can identify the largest age group in Brownstown.
Key observations
The largest age group in Brownstown, IN was for the group of age 70-74 years with a population of 384 (12.77%), according to the 2021 American Community Survey. At the same time, the smallest age group in Brownstown, IN was the 80-84 years with a population of 82 (2.73%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Brownstown Population by Age. You can refer the same here
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The dataset tabulates the data for the Dana, IA population pyramid, which represents the Dana 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
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Dana Population by Age. You can refer the same here
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The dataset tabulates the population of Triangle town by race. It includes the population of Triangle town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Triangle town across relevant racial categories.
Key observations
The percent distribution of Triangle town population by race (across all racial categories recognized by the U.S. Census Bureau): 97.49% are white, 1.22% are Black or African American, 0.54% are some other race and 0.75% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Triangle town Population by Race & Ethnicity. You can refer the same here
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The dataset tabulates the Keedysville population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Keedysville. The dataset can be utilized to understand the population distribution of Keedysville by age. For example, using this dataset, we can identify the largest age group in Keedysville.
Key observations
The largest age group in Keedysville, MD was for the group of age 45 to 49 years years with a population of 140 (11.26%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Keedysville, MD was the 85 years and over years with a population of 3 (0.24%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Keedysville Population by Age. You can refer the same here
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The dataset tabulates the data for the Oak Grove, KY population pyramid, which represents the Oak Grove 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
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Oak Grove Population by Age. You can refer the same here
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Context
The dataset tabulates the data for the Fort Lauderdale, FL population pyramid, which represents the Fort Lauderdale 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
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Fort Lauderdale Population by Age. You can refer the same here
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the St. Clair, PA population pyramid, which represents the St. Clair 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
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for St. Clair Population by Age. You can refer the same here
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Demographic data are important to wildlife managers to gauge population health, to allow populations to be utilised sustainably, and to inform conservation efforts. We analysed published demographic data on the world’s wildfowl to examine taxonomic and geographic biases in study, and to identify gaps in knowledge. Wildfowl (order: Anseriformes) are a comparatively well studied bird group which includes 169 species of duck, goose and swan. In all, 1,586 wildfowl research papers published between 1911 and 2010 were found using Web of Knowledge (WoK) and Google Scholar. Over half of the research output involved just 15 species from seven genera. Research output was strongly biased towards ‘high income’ countries, common wildfowl species, and measures of productivity, rather than survival and movement patterns. There were significantly fewer demographic data for the world’s 31 threatened wildfowl species than for non-threatened species. Since 1994, the volume of demographic work on threatened species has increased more than for non-threatened species, but still makes up only 2.7% of total research output. As an aid to research prioritisation, a metric was created to reflect demographic knowledge gaps for each species related to research output for the species, its threat status, and availability of potentially useful surrogate data from congeneric species. According to the metric, the 25 highest priority species include thirteen threatened taxa and nine species each from Asia and South America, and six from Africa.