About this dataset

Context

The dataset tabulates the population of Town And Country by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Town And Country. The dataset can be utilized to understand the population distribution of Town And Country by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Town And Country. 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 Town And Country.

Key observations

Largest age group (population): Male # 60-64 years (538) | Female # 45-49 years (537). 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 Town And Country population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Town And Country is shown in the following column.
• Population (Female): The female population in the Town And Country 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 Town And Country 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 Town And Country Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Lost Nation by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lost Nation. The dataset can be utilized to understand the population distribution of Lost Nation by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lost Nation. 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 Lost Nation.

Key observations

Largest age group (population): Male # 50-54 years (27) | Female # 10-14 years (25). 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 Lost Nation population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lost Nation is shown in the following column.
• Population (Female): The female population in the Lost Nation 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 Lost Nation 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 Lost Nation Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Country Club Hills by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Club Hills. The dataset can be utilized to understand the population distribution of Country Club Hills by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Club Hills. 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 Country Club Hills.

Key observations

Largest age group (population): Male # 10-14 years (639) | Female # 15-19 years (1,080). 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 Country Club Hills population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Hills is shown in the following column.
• Population (Female): The female population in the Country Club Hills 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 Country Club Hills 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 Country Club Hills Population by Gender. You can refer the same here

BackgroundDelivery of preventive chemotherapy (PC) through mass drug administration (MDA) is used to control or eliminate five of the most common neglected tropical diseases (NTDs). The success of an MDA campaign relies on the ability of drug distributors and their supervisors—the NTD front-line workers—to reach populations at risk of NTDs. In the past, our understanding of the demographics of these workers has been limited, but with increased access to sex-disaggregated data, we begin to explore the implications of gender and sex for the success of NTD front-line workers.Methodology/Principal findingsWe reviewed data collected by USAID-supported NTD projects from national NTD programs from fiscal years (FY) 2012–2017 to assess availability of sex-disaggregated data on the workforce. What we found was sex-disaggregated data on 2,984,908 trainees trained with financial support from the project. We then analyzed the percentage of males and females trained by job category, country, and fiscal year. During FY12, 59% of these data were disaggregated by sex, which increased to nearly 100% by FY15 and was sustained through FY17. In FY17, 43% of trainees were female, with just four countries reporting more females than males trained as drug distributors and three countries reporting more females than males trained as trainers/supervisors. Except for two countries, there were no clear trends over time in changes to the percent of females trained.Conclusions/SignificanceThere has been a rapid increase in availability of sex-disaggregated data, but little increase in recruitment of female workers in countries included in this study. Women continue to be under-represented in the NTD workforce, and while there are often valid reasons for this distribution, we need to test this norm and better understand gender dynamics within NTD programs to increase equity.

Among 15-year-olds, boys tend to report higher life satisfaction than girls. Recent research has shown that this gender gap tends to be larger in more gender-egalitarian countries. We shed light on this apparent paradox by examining the mediating role of two psychological dispositions: competitiveness and fear of failure. Using data from the 2018 PISA study, we analyze the life satisfaction, competitiveness, and fear of failure of more than 400,000 15-year-old boys and girls in 63 countries with known levels of gender equality. We find that competitiveness and fear of failure together mediate more than 40 percent of the effects on life satisfaction of gender and its interaction with gender equality. Thus, interventions targeting competitiveness and fear of failure could potentially have an impact on the gender gap in life satisfaction among adolescents in gender equal countries.

About this dataset

Context

The dataset tabulates the population of Country Club Heights by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Club Heights. The dataset can be utilized to understand the population distribution of Country Club Heights by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Club Heights. 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 Country Club Heights.

Key observations

Largest age group (population): Male # 55-59 years (22) | Female # 45-49 years (15). 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 Country Club Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Heights is shown in the following column.
• Population (Female): The female population in the Country Club Heights 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 Country Club Heights 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 Country Club Heights Population by Gender. You can refer the same here

Office for National Statistics’ national and subnational total mid-year population estimates for England and Wales for a selection of administrative and census areas by sex for 2012 to 2020. The data is source is from ONS Population Estimates. Find out more about this dataset here.

This data is issued at (BGC) Generalised (20m) boundary type for:

Country, Region, Upper Tier Local Authority (2021), Lower Tier Local Authority (2021), Middle Super Output Area (2011), and Lower Super Output Area (2011).

If you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at dataenquiries@esriuk.com.

The Office for National Statistics (ONS) produces annual estimates of the resident population of England and Wales at 30 June every year. The most authoritative population estimates come from the census, which takes place every 10 years in the UK. Population estimates from a census are updated each year to produce mid-year population estimates (MYEs), which are broken down by local authority, sex and age. More detailed information on the methods used to generate the mid-year population estimates can be found here.

For further information on the usefulness of the data and guidance on small area geographies please see here.The currency of this data is 2021.

Methodology

The total and 5-year breakdown population counts are reproduced directly from the source data. The age range estimates have been calculated from the published estimates by single year of age. The percentages are calculated using the gender specific (total, female or male) total population count as a denominator except in the case of the male and female total population where the total population is used to give female and male proportions.

This dataset will be updated annually, in two releases.

Creator: Office for National Statistics. Aggregated age groupings and percentages calculated by Esri UK._The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Abstract

This data is from a quantitative survey administered in 2023 to 2,000 married Nepali women and men from 4 provinces in the country about their own beliefs regarding norms-related behaviors, their expectations of how common it is for others in their social group to engage in those behaviors, and the expected social consequences surrounding those behaviors. It is the primary dataset used to author the working paper titled "Women’s Labor Force Participation in Nepal: An Exploration of The Role of Social Norms" - which presents rigorous evidence on whether and the extent to which social norms matter for women's labor force participation in Nepal.

Geographic coverage

The survey data includes a representative sample of households from 4 out of 7 provinces in Nepal: 1. Bagmati Province 2. Sudurpashchim Province 3. Madhesh Province 4. Gandaki Province

Analysis unit

Individual

Universe

The sampling frame is a list of all wards within each selected province.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ward (cluster) selection: The sampling frame consisted of the list of all wards within each selected province. Each province comprises districts and within each district are municipalities (urban and rural municipalities) which are further broken down into wards – the smallest administrative units. The list of wards and their population figures were taken from the latest available 2021 Census. First, the universe of all districts was stratified by urban and rural to ensure greater statistical power for detecting differences between the 2 localities. The stratification by urban-rural proportionate to the population proportion of each group within a province resulted in a self-weighted sample, allowing for analysis of data at the province level and further at locality level within each province. To select the wards, a random start point was generated to negate any bias in the list and to provide an independent chance of selection from the list. The sampling method used here, probability proportionate to size (PPS), gives an independent chance of selection to each ward as per its population size, i.e., a higher chance of selection to wards with a higher population size.38 As a first step of random selection of wards, the cumulative frequency (CF) of the population of households in a ward was calculated. Since the unit of analysis for our study purpose was households having certain criteria and we expected the main outcome variables (social norms) to vary at household levels (as opposed to at an individual level), the household population figures served as the basis for sampling purpose (as opposed to the population size of individuals for a ward). Applying PPS, in the first step, the required number of wards were selected for Categories 1 and 2 households (households with working and non-working females respectively). Following this, the clusters allocated for Category 3 (households with migrant population) households were taken as a subset of the wards selected for Categories 1 and 2.

Selection of the random starting point within each ward during in-field random sampling of households: The selection of the random starting point within a PSU was done by the survey supervisors. For every ward, a predefined landmark for the starting point was chosen. The predefined landmark consisted of i) school, ii) health post, iii) central marketplace, or iv) ward office. The selection of a predefined landmark was the basis of the starting point which was made at the central office. The chosen landmark for every cluster was rotated to account for randomization and to avoid interviewer bias. Once the landmark was chosen, each enumerator used the spin-the-bottle method to randomize the direction in which the survey took place. After starting with a household, enumerators used a skip interval to survey every third household in rural and every fifth household in urban areas. Once the household was chosen, the interviewer used the screener to ascertain the eligibility as per the category quota set aside for them.

Respondent selection: The respondents were selected based on a screener instrument that surveyed the following factors: 1. Gender: Since the views about social norms and labor market outcomes vary by gender, both males and females within a household were interviewed. However, for households with migrant men, only the women were interviewed. 2. Age group: For all women, the screener was applied so as to ensure that only women within the economically active age range, i.e., between the ages of 18-59 years were interviewed. For spouses of female respondents, they had to be at least 18 years of age with no maximum age limit set. 3. Ethnicity: Nepal has more than a hundred ethnic groups residing across the country, and thus the major 8-10 groups are captured in the sample. The other objective of applying a screener for monitoring ethnic composition was to ensure that marginalized ethnic groups such as Dalits were sufficiently represented in the survey. 4. Marital Status: Only married men and women were interviewed since marriage and the responsibilities that come with are sown to impose greater social barriers and restrictions on mobility and work of females. 5. Location: The survey was carried out in both rural and urban locations in a total of 4 provinces. 6. General demographic factors include: • Perceived economic situation: Low to middle-income • It was ensured that both the respondents (male and female for Categories 1 and 2) and female respondent for Category 3 belonged to the second generation of the selected household (for example, not the in-laws residing in a household but their son and his wife.

Mode of data collection

Computer Assisted Personal Interview [capi]

Office for National Statistics’ national and subnational mid-year population estimates for England and Wales for a selection of administrative and census areas by age (in 5 year age brackets) for 2012 to 2020. The data is source is from ONS Population Estimates. Find out more about this dataset here.This data is issued at (BGC) Generalised (20m) boundary type for:Country,Region,Upper Tier Local Authority (2021),Lower Tier Local Authority (2021),Middle Super Output Area (2011), andLower Super Output Area (2011).If you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The Office for National Statistics (ONS) produces annual estimates of the resident population of England and Wales at 30 June every year. The most authoritative population estimates come from the census, which takes place every 10 years in the UK. Population estimates from a census are updated each year to produce mid-year population estimates (MYEs), which are broken down by local authority, sex and age. More detailed information on the methods used to generate the mid-year population estimates can be found here.For further information on the usefulness of the data and guidance on small area geographies please see here.The currency of this data is 2021.MethodologyThe total and 5-year breakdown population counts are reproduced directly from the source data. The age range estimates have been calculated from the published estimates by single year of age. The percentages are calculated using the gender specific (total, female or male) total population count as a denominator except in the case of the male and female total population where the total population is used to give female and male proportions.This dataset will be updated annually, in two releases.Creator: Office for National Statistics. Aggregated age groupings and percentages calculated by Esri UK._The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Abstract (en): These data are a collection of demographic statistics for the populations of 125 countries or areas throughout the world, prepared by the Statistical Office of the United Nations. The units of analysis are both country and data year. The primary source of data is a set of questionnaires sent monthly and annually to national statistical services and other appropriate government offices. Data include statistics on approximately 50 types of causes of death for the years 1966 through 1974 for males, females, and total populations. Causes of death in 125 countries or areas throughout the world between the years 1966 and 1974. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. The causes of death are classified according to the 6th, 7th, and 8th versions of an abbreviated list of the World Health Organization's INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Therefore, data for causes of death are not necessarily comparable across countries or data years. Users should refer to Variable 5 in the Variable List for full discussion of this problem. Users interested in comparing deaths for countries or years that use different versions of the Abbreviated list should consult two publications: A. Joan Klebba, and Alice B. Dolman. COMPARABILITY OF MORTALITY STATISTICS FOR THE SEVENTH AND EIGHTH REVISIONS OF THE INTERNATIONAL CLASSIFICATION OF DISEASES, UNITED STATES. Rockville, MD: United States Department of Health, Education, and Welfare. Public Health Service. Health Services and Mental Health Administration. National Center for Health Statistics, 1975, and World Health Organization. MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Geneva, Switzerland: World Health Organization, 1967.The user should note that countries have data covering a variety of time spans (the maximum span being 1965-1973), and the data have not been padded to supply missing data codes for those years for which a country does not have data. Thus, Egypt has data for years 1965 through 1972, while Kenya has data for only 1970. (See Appendix D in the codebook to determine the years for which a country has data.)It is important that any user of these data consult the United Nations' DEMOGRAPHIC YEARBOOK, 1976, for further explanation of the data's limitations. Certain countries have modified reporting procedures which are presented in both the footnotes and the technical notes accompanying the tables in the Yearbook. There is no way to identify these problems using only the machine-readable data.In order to eliminate unnecessary repetition of identifying information, data were merged so that each record now contains all the data for a country for one particular year. In this process, breakdowns of deaths by ethnic group and/or urban/rural classification were omitted since only a few countries provided such information. Each record now contains the data for the number of deaths from each cause of death for male, female, and total.While the data appear to be in a rectangular matrix, such is not the case. This occurs because different versions of the abbreviated list are referenced in different data years. The lack of a rectangular data matrix does little to restrict the manageability of the dataset. See codebook for examples.While the data have been reformatted and documented by ICPSR staff, there has been no attempt to verify the accuracy and consistency of the data received from the U.N. Statistical Office.

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Mozambique, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Latvia, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

About this dataset

Context

The dataset tabulates the population of Country Life Acres by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Country Life Acres. The dataset can be utilized to understand the population distribution of Country Life Acres by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Country Life Acres. 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 Country Life Acres.

Key observations

Largest age group (population): Male # 60-64 years (7) | Female # 60-64 years (6). 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 Country Life Acres population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Life Acres is shown in the following column.
• Population (Female): The female population in the Country Life Acres 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 Country Life Acres 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 Country Life Acres Population by Gender. You can refer the same here

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for El Salvador, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Road-kills are the greatest source of direct human-induced wildlife mortality, especially in amphibians. Country roads could act as the most important source of mortality when main roads act as strong barriers hampering the migration movements of some species. Mortality patterns of amphibians on country roads (1380 km) were studied in Salamanca (Spain) in order to quantify the mortality levels, to test the effects of sex and age factors on road-kills, to determine the spatial distribution patterns of road-kills, and to identify routes of migration through a friction map and hotspots of road-kills. From a total of 819 records of amphibians, 38.1% were road-killed and 61.9% were live. Fourteen amphibian species were recorded during the surveys (10 anurans and four urodeles). The species more affected by road-kills were the anurans Bufo calamita, Pelobates cultripes and B. bufo (38.5, 23.4 and 11.9%, respectively). Females had higher incidence of road-kills than males, due to the differential activity patterns of both sexes during the reproductive period. Adults were the most common age period and also the most road-killed. The spatial distribution patterns of live and road-killed records were clustered. On the sampled roads, there were 0.23 road-kills per kilometre and 52 hotspots of road-kills. The friction map showed that most of the road-killed and live specimens were located on migration routes crossing suitable habitats. Conservation measures should be implemented in these areas, as these mortality patterns may be causing significant negative impacts at the population level. Palabras clave: Amphibian, Mortality

LT: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 4.900 Ratio in 2016. This records an increase from the previous number of 4.700 Ratio for 2015. LT: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 5.500 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 13.200 Ratio in 1990 and a record low of 4.700 Ratio in 2015. LT: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lithuania – Table LT.World Bank: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Armenia, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Description

This dataset provides a comprehensive record of missing migrants and their tragic journeys towards international destinations , collected by the Missing Migrants Project, an initiative implemented by the International Organization for Migration (IOM) since 2014. The dataset documents deaths and disappearances, shedding light on the challenges migrants face during their journeys. Please note that due to the complexities of data collection, the figures presented are likely an undercount. The dataset serves as a tribute to the individuals who lost their lives, as well as the families and communities impacted by their absence.

Key Features:

• Incident Type: Type of migration incident
• Incident Year: Year when the incident occurred
• Reported Month: Month when the incident was reported
• Region of Origin: Geographical region where the migrants originated
• Region of Incident: Geographical region where the incident occurred
• Country of Origin: Country from which the migrants originated
• Number of Dead: Number of confirmed deceased migrants
• Minimum Estimated Number of Missing: Minimum estimated count of missing migrants
• Total Number of Dead and Missing: Total count of both deceased and missing migrants
• Number of Survivors: Number of migrants who survived the incident
• Number of Females: Number of female migrants involved
• Number of Males: Number of male migrants involved
• Number of Children: Number of children migrants involved
• Cause of Death: Cause of death for the migrants
• Migration Route: Route taken by migrants during their journey (if available)
• Location of Death: Approximate location where the incident occurred
• Information Source: Source of information about the incident
• Coordinates: Geographical coordinates of the incident location
• UNSD Geographical Grouping: Geographical grouping according to the United Nations Statistics Division

Potential Use Cases:

• Migration Patterns Analysis: Explore trends and patterns in migration incidents to understand the most affected regions and routes.
• Gender and Age Analysis: Investigate the demographics of migrants to identify gender and age-related vulnerabilities.
• Survival and Mortality Analysis: Analyze survival rates and causes of death to highlight risks and challenges migrants face.
• Temporal Analysis: Examine incidents over time to identify any temporal patterns or changes.
• Geospatial Analysis: Utilize geographical coordinates to map migration routes and incident locations.

If you find this dataset valuable, your support through votes is highly appreciated! ❤️ Thank you 🙂

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for United States Virgin Islands (USA), version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Lebanon, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)