This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This dataset describes birth outcomes (weight, gestational age, sex assigned at birth, presence of birth defects, etc.) and parental factors (age, address, health status, etc.) for people born in North Carolina between 2003 and 2015. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data come from the North Carolina Birth Defects Monitoring Program. These data are not publicly available, but more information can be obtained at https://schs.dph.ncdhhs.gov/units/bdmp/ (accessed 11/9/2021). Format: Data are stored as csv files and contain information on birth records in North Carolina from 2003 to 2015, including addresses of parents and medical information on parents and neonates. This dataset is associated with the following publication: Slawsky, E., A. Weaver, T. Luben, and K. Rappazzo. A Cross-sectional Study of Brownfields and Birth Defects. Birth Defects Research. John Wiley & Sons, Inc., Hoboken, NJ, USA, 114(5-6): 197-207, (2022).

Connecticut's Birth to Three System (B23) supports families with infants and toddlers that have developmental delays to learn new ways to make everyday activities enhance the child's development. Birth to Three is administered pursuant to Part C of the Individuals with Disabilities Education Act (IDEA). Once families with children below age 3 are referred, the child's development is evaluated for eligibility, and if eligible the family can receive supports until the child no longer has delays or until the child turns age 3. Because an infant can be referred within days of being born, a family may be enrolled for almost three full years. Connecticut's Birth to Three System publishes data annually by the fiscal and calendar year and longitudinally by birth cohort. CTData.org carries both sets of data, here and in 'Birth To Three Annual Data'. Birth cohort data looks at all children born in a particular year and tracks whether the family received B23 support. For example, the latest full year available in this dataset is for those children born in 2013 since they turned age 3 sometime in 2016. The 2013 data will tell you how many children there were whose families received support at some point during the first three years of the child's life. CTData calculates several indicators using total number of births in a town. This provides users with a general idea of the relative number of children in the community eligible for services. Using births is not perfect since families move in and out of town so it should not be used as an exact figure but as a general reference point. Below are how the indicators are calculated: % Referrals = Number referred divided by total number of births % Evaluations = Number evaluated divided by total number of births % Eligible = Number eligible divided by total number of births % Individual Family Service Plans (IFSP) = Number with IFSP divided by total number of births % Served = Number served divided by total number of births % Exited to Early Childhood Special Education = Number exited to early childhood special education divided by total number of births 'Referred that are Evaluated' represents the percent of children that were evaluated out of the total number of children referred to the Birth to Three System. 'Evaluated that are Eligible' represents the percent of children who were deemed eligible out of the total number of children that were evaluated. 'Eligible that Recieve IFSP' represents the percent of children whose family recieved an Individual Family Service Plan out of the total number of eligible children.

Context

Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

Content

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

https://cloud.google.com/bigquery/public-data/usa-names

Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @dcp from Unplash.

Inspiration

What are the most common names?

What are the most common female names?

Are there more female or male names?

Female names by a wide margin?

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20–3000 people/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area situated in the south-east portion of the uMkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the South by the Umfolozi river, on the East by the N2 highway (except form portions where the Kwamsane township strandles the highway) and in the North by the Inyalazi river for portions of the boundary. The area is 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (11.65 million) | Female # 30-34 years (11.41 million). 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 United States population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the United States is shown in the following column.
• Population (Female): The female population in the United States 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 United States 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 United States Population by Gender. You can refer the same here

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset contains the annual number of births and crude birth rate (births per 1,000 residents) with corresponding 95% confidence intervals, by Chicago community area, for the years 1999 – 2009. See the full dataset description for more information: https://data.cityofchicago.org/api/assets/8C4E8E51-6162-4DF3-9C29-D3F205FA2FB4

We conducted an unmatched case-control study of 1,225,285 infants from a North Carolina Birth Cohort (2003-2015). Ozone and PM2.5 during critical exposure periods (gestational weeks 3-8) were estimated using residential address and a national spatiotemporal model at census tract centroid. Here we describe data sources for outcome (i.e., congenital heart defects) and exposure (i.e., ozone and PM2.5) data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The North Carolina Birth Cohort data are not publicly available as it contains personal identifiable information. Data may be requested through the NCDHHS, Division of Public Health with proper approvals. Air pollutant concentrations for ozone and PM2.5 from the national spatiotemporal model are publicly available from EPA's website. Format: Birth certificate data from the State Center for Health Statistics of the NC Department of Health and Human Services linked with data from the Birth Defects Monitoring Program (NC BDMP) to create a birth cohort of all infants born in NC between 2003-2015. The NC BDMP is an active surveillance system that follows NC births to obtain birth defect diagnoses up to 1 year after the date of birth as well as identify infant deaths during the first year of life and include relevant information from the death certificate. A national spatiotemporal model provided data on predicted ozone PM2.5 concentrations over critical prenatal and time periods. The prediction model used data from research and regulatory monitors as well as a large (>200) array of geographic covariates to create fine scale spatial and temporal predictions. The model has a cross-validated R2 of 0.89 for PM2.5. Concentrations were predicted for daily throughout the study period at the centroid of each 2010 census tract in NC. This dataset is associated with the following publication: Arogbokun, O., T. Luben, J. Stingone, L. Engel, C. Martin, and A. Olshan. Racial disparities in maternal exposure to ambient air pollution during pregnancy and prevalence of congenital heart defects. AMERICAN JOURNAL OF EPIDEMIOLOGY. Johns Hopkins Bloomberg School of Public Health, 194(3): 709-721, (2025).

About this dataset

Context

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

Key observations

Largest age group (population): Male # 20-24 years (347) | Female # 50-54 years (433). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

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 Globe population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Globe is shown in the following column.
• Population (Female): The female population in the Globe 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 Globe 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 Globe Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 35-39 years (21) | Female # 30-34 years (51). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

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 Wheeler population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Wheeler is shown in the following column.
• Population (Female): The female population in the Wheeler 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 Wheeler 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 Wheeler Population by Gender. You can refer the same here

Analysis of ‘Places of birth of the Moerser Population 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/51e9e819-dfb7-4bb0-ab44-9ef00a9896c6 on 15 January 2022.

--- Dataset description provided by original source is as follows ---

The data set contains the birth places of moersers with their main residence in Moers (population as at 31/12/2015).

As there may be inaccuracies in the data (e.g. incorrect or outdated spelling of place names), birth places with fewer than 10 people living in moers have not been taken into account. As a result, 21 200 moors are not included in the statistics.

Of some 14 000 (!) birth places of people with their main residence in Moers, 634 are listed in the statitstik.

When processing the data, account shall be taken of the following points:

• Outdated spelling such as ‘Prussisch Stargard’ (= today ‘Starogard Gdański’) should be taken into account.
• There are many entries from previous locations, which have belonged to another location since the territorial reform in 1975 (e.g. “Capellens now Moers”). These entries would need to be recoded accordingly.

The suggestion for this dataset provided this beautiful visualisation of Berlin’s morning mail: http://interaktiv.morgenpost.de/berliner-zugezogenen-atlas/

--- Original source retains full ownership of the source dataset ---

This table contains 30 series, with data for years 1961 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Children born to ever-married women (10 items: Number of children born to ever-married women 15 years of age and over; total; Number of children born to ever-married women aged 15-19 years; Number of children born to ever-married women aged 20-24 years; Number of children born to ever-married women aged 25-29 years ...) Type of area (3 items: Total urban and rural areas; Rural; Urban ...).

About this dataset

Context

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

Key observations

Largest age group (population): Male # 65-69 years (44) | Female # 65-69 years (52). 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 Juniata population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Juniata is shown in the following column.
• Population (Female): The female population in the Juniata 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 Juniata 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 Juniata Population by Gender. You can refer the same here

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6150f21b0892b3fdde546d2a1af2af82/view

About this dataset

Context

The dataset tabulates the data for the Houston, TX population pyramid, which represents the Houston population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Houston, TX, is 29.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Houston, TX, is 16.9.
• Total dependency ratio for Houston, TX is 46.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Houston, TX is 5.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Houston population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Houston for the selected age group is shown in the following column.
• Population (Female): The female population in the Houston for the selected age group is shown in the following column.
• Total Population: The total population of the Houston 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 Houston Population by Age. You can refer the same here

Context

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050.

Content

The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_international

https://cloud.google.com/bigquery/public-data/international-census

Dataset Source: www.census.gov

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source -http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What countries have the longest life expectancy?

Which countries have the largest proportion of their population under 25?

Which countries are seeing the largest net migration?

Abstract

The Ouagadougou Health and Demographic Surveillance System (Ouagadougou HDSS), located in five neighborhoods at the northern periphery of the capital of Burkina Faso, was established in 2008. Data on vital events (births, deaths, unions, migration events) are collected during household visits that have taken place every 10 months.

The areas were selected to contrast informal neighborhoods (40,000 residents) with formal areas (40,000 residents), with the aims of understanding the problems of the urban poor, and testing innovative programs that promote the well-being of this population. People living in informal areas tend to be marginalized in several ways: they are younger, poorer, less educated, farther from public services and more often migrants. Half of the residents live in the Sanitary District of Kossodo and the other half in the District of Sig-Nonghin.

The Ouaga HDSS has been used to study health inequalities, conduct a surveillance of typhoid fever, measure water quality in informal areas, study the link between fertility and school investments, test a non-governmental organization (NGO)-led program of poverty alleviation and test a community-led targeting of the poor eligible for benefits in the urban context. Key informants help maintain a good rapport with the community.

The areas researchers follow consist of 55 census tracks divided into 494 blocks. Researchers mapped all the census tracks and blocks using fieldworkers with handheld global positioning system (GPS) receivers and ArcGIS. During a first census (October 2008 to March 2009), the demographic surveillance system was explained to every head of household and a consent form was signed; during subsequent censuses, new households were enrolled in the same way.

Geographic coverage

Ouagadougou is the capital city of Burkina Faso and lies at the centre of this country, located in the middle of West Africa (128 North of the Equator and 18 West of the Prime Meridian).

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants (visitors) are defined by intention to become resident, but actual residence episodes of less than six months (180 days) are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than six months (180 days) are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever residents during the study period (03 Oct. 2009 to 31 Dec. 2014).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 0 to 7 of demographic surveillance data covering the period from 07 Oct. 2008 to 31 December 2014.

Sampling procedure

This dataset is not based on a sample, it contains information from the complete demographic surveillance area of Ouagadougou in Burkina Faso.

Reponse units (households) by Round: Round Households
2008 4941
2009 19159 2010 21168
2011 12548 2012 24174 2013 22326

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Collective Housing Unit (UCH) Survey Form - Used to register characteristics of the house - Use to register Sanitation installations - All registered house as at previous round are uploaded behind the PDA or tablet.

Household registration (HHR) or update (HHU) Form - Used to register characteristics of the HH - Used to update information about the composition of the household - All registered households as at previous rounds are uploaded behind the PDA or tablet.

Household Membership Registration (HMR) or update (HMU) - Used to link individuals to households. - Used to update information about the household memberships and member status observations - All member status observations as at previous rounds are uploaded behind the PDA or tablet.

Presences registration form (PDR) - Used to uniquely identify the presence of each individual in the household and to identify the new individual in the household - Mainly to ensure members with multiple household memberships are appropriately captured - All presences observations as at previous rounds are uploaded behind the PDA or tablet.

Visitor registration form (VDR) - Used register the characteristics of the new individual in the household - Used to capt the internal migration - Use matching form to facilitate pairing migration

Out Migration notification form (MGN) - Used to record change in the status of residency of individuals or households - Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON) - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH - All member pregnancy without pregnancy outcome as at previous rounds are uploaded behind the PDA or tablet.

Death notification form (DTN) - Records all deaths that have recently occurred - Includes information about time, place, circumstances and possible cause of death

Updated Basic information Form (UBIF) - Use to change the individual basic information

Health questionnaire (adults, women, child, elder) - Family planning - Chronic illnesses - Violence and accident - Mental health - Nutrition, alcohol, tobacco - Access to health services - Anthropometric measures - Physical limitations - Self-rated health - Food security

Variability of climate and water accessibility - accessibility to water - child health outcomes - gender outcomes - data on rainfall, temperatures, water quality

Cleaning operations

The data collection system is composed by two databases: - A temporary database, which contains data collected and transferred each day during the round. - A reference database, which contains all data of Ouagadougou Health and Demographic Surveillance System, in which is transferred the data of the temporary database to the end of each round. The temporary database is emptied at the end of the round for a new round.

The data processing takes place in two ways:

1) When collecting data with PDAs or tablets and theirs transfers by Wi-Fi, data consistency and plausibility are controlled by verification rules in the mobile application and in the database. In addition to these verifications, the data from the temporary database undergo validation. This validation is performed each week and produces a validation report for the data collection team. After the validation, if the error is due to an error in the data collection, the field worker equipped with his PDA or tablet go back to the field to revisit and correct this error. At the end of this correction, the field worker makes again the transfer of data through the wireless access points on the server. If the error is due to data inconsistencies that might not be directly related to an error in data collection, the case is remanded to the scientific team of the main database that could resolve the inconsistency directly in the database or could with supervisors perform a thorough investigation in order to correct the error.

2) At the end of the round, the data from the temporary database are automatically transferred into the reference database by a transfer program. After the success of this transfer, further validation is performed on the data in the database to ensure data consistency and plausibility. This still produces a validation report for the data collection team. And the same process of error correction is taken.

Response rate

Household response rates are as follows (assuming that if a household has not responded for 2 years following the last recorded visit to that household, that the household is lost to follow-up and no longer part of the response rate denominator):

Year Response Rate
2008 100%
2009 100%
2010 100%
2011 98% 2012 100% 2013 95%

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate BF041 MicroDataCleaned Starts 151624 2017-05-16 13:36
BF041 MicroDataCleaned Transitions 0 314778 314778 0 2017-05-16 13:36
BF041 MicroDataCleaned Ends 151624 2017-05-16 13:36
BF041 MicroDataCleaned SexValues 314778 2017-05-16 13:36
BF041 MicroDataCleaned DoBValues 314778 2017-05-16 13:36

Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Input data can be obtained from Applications from owners of the data (Children's Hospital and Ohio Department of Health). The tabulated output data is found in the manuscript. Format: Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript (e.g., results of regression models, measures of central tendency, population characteristics, etc.).

This dataset is associated with the following publication: Kaufman, J., M. Wright, G. Rice, N. Connolly, K. Bowers, and J. Anixt. AMBIENT OZONE AND FINE PARTICULATE MATTER EXPOSURES AND AUTISM SPECTRUM DISORDER IN METROPOLITAN CINCINNATI, OHIO. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 171: 218-227, (2019).