United States - Population Level - Black or African American was 35892.00000 Thous. of Persons in September of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Black or African American reached a record high of 35892.00000 in September of 2025 and a record low of 14332.00000 in January of 1972. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Black or African American - last updated from the United States Federal Reserve on December of 2025.

There were almost 700 thousand slaves in the U.S. in 1790, which equated to approximately 18 percent of the total population, or roughly one in six people. By 1860, the final census taken before the American Civil War, there were four million slaves in the South, compared with less than 500,000 free Black Americans in all of the U.S.. Of the 4.4 million Blacks in the U.S. before the war, almost four million of these people were held as slaves; meaning that for all African Americans living in the US in 1860, there was an 89 percent* chance that they lived in slavery. A brief history Trans-Atlantic slavery began in the early 16th century, when the Portuguese and Spanish forcefully brought enslaved Africans to the New World. The British Empire introduced slavery to North America on a large scale, and the economy of the British colonies there depended on slave labor, particularly regarding cotton, sugar, and tobacco output. In the seventeenth and eighteenth century the number of slaves being brought to the Americas increased exponentially, and at the time of American independence it was legal in all thirteen colonies. Although slavery became increasingly prohibited in the north, the number of slaves remained high during this time as they were simply relocated or sold from the north to the south. It is also important to remember that the children of slaves were also viewed as property, and were overwhelmingly born into a life of slavery. Abolition and the American Civil War In the years that followed independence, the Northern States gradually prohibited slavery, it was officially abolished there by 1805, and the importation of slave labor was prohibited nationwide from 1808 (although both still existed in practice after this). Business owners in the Southern States however depended on slave labor in order to meet the demand of their rapidly expanding industries, and the issue of slavery continued to polarize American society in the decades to come. This culminated in the election of President Abraham Lincoln in 1860, who promised to prohibit slavery in the newly acquired territories to the west, leading to the American Civil War from 1861 to 1865. Although the Confederacy (south) took the upper hand in much of the early stages of the war, the strength in numbers of the northern states including many free, Black men, eventually resulted in a victory for the Union (north), and the nationwide abolishment of slavery with the Thirteenth Amendment in 1865. Legacy In total, an estimated twelve to thirteen million Africans were transported to the Americas as slaves, and this does not include the high number who did not survive the journey (which was as high as 23 percent in some years). In the 150 years since the abolition of slavery in the US, the African-American community have continuously campaigned for equal rights and opportunities that were not afforded to them along with freedom. The most prominent themes have been the Civil Rights Movement, voter suppression, mass incarceration, and the relationship between the police and the African-American community.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.

The leading causes of death among Black residents in the United States in 2023 included diseases of the heart, cancer, unintentional injuries, and stroke. The leading causes of death for African Americans generally reflect the leading causes of death for the entire United States population. However, a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans but is not among the ten leading causes for the general population. Homicide among African Americans The homicide rate among African Americans has been higher than that of other races and ethnicities for many years. In 2023, around 9,284 Black people were murdered in the United States, compared to 7,289 white people. A majority of these homicides are committed with firearms, which are easily accessible in the United States. In 2023, around 13,350 Black people died by firearms. Cancer disparities There are also major disparities in access to health care and the impact of various diseases. For example, the incidence rate of cancer among African American males is the greatest among all ethnicities and races. Furthermore, although the incidence rate of cancer is lower among African American women than it is among white women, cancer death rates are still higher among African American women.

Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Sep 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

The US Census Bureau defines Black or African American as "A person having origins in any of the Black racial groups of Africa. It includes people who indicate their race as "Black or African American," or report entries such as African American, Kenyan, Nigerian, or Haitian.". Black population percentage was calculated based upon total Black or African Americans within the census block group divided the total population of the same census block group. 2020 Census block groups for the Wichita / Sedgwick County area, clipped to the county line. Features were extracted from the 2020 State of Kansas Census Block Group shapefile provided by the State of Kansas GIS Data Access and Support Center (https://www.kansasgis.org/index.cfm).Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number. For example, blocks 3001, 3002, 3003… 3999 in census tract 1210.02 belong to Block Group 3. Due to boundary and feature changes that occur throughout the decade, current block groups do not always maintain these same block number to block group relationships. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the identification string (GEOID20) for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block identification strings are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain between 600 and 3,000 people. Local participants delineated most block groups as part of the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains at least one block group and block groups are uniquely numbered within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian, Alaska Native, and Native Hawaiian areas.Block groups have a valid range of 0 through 9. Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

Abstract

The 1970 South African Population Census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, migration, family and employment details.

Geographic coverage

National coverage of the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal, and the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, Kwandebele, Transkei and Bophuthatswana.

Analysis unit

The units of analysis for the South African Census 1970 were households and individuals

Universe

The South African population census of 1970 covered all de jure household members (usual residents) of South Africa and the "national states".

The Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 1970 Census was a full count for Whites, Coloureds and Asians, and a 5% sample for Blacks (Africans)

Sampling deviation

The country was divided into 400 census districts for the 1970 Census. In most cases the boundaries of the census districts corresponded with those of the magisterial districts. However, in some cases the boundaries did not correspond, particularly in the areas in and around the "National States". This was to facilitate the administration of the census and to make it easier to exclude figures of the "National states" from provincial totals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1970 Population Census of the Republic of South Africa questionnaires were: Form 01, to be completed by "Whites, Coloured and Asiatics" Form 02, to be completed by "Bantu" Form 03, for families, households and dwellings

Form 01 collected data on relationship to household head, population group, sex, age, marital status, place of birth, and citizenship, as well as usual place of residence, home language, religion, level of education and income. Employment data collected included occupation, employment status and industry type.

Form 02 collected data for African South Africans on relationship to household head, sex, age, marital status, fertility, place of birth, home language and literacy, religion and level of education. Employment data collected included occupation, employment status and industry type.

Form 03 collected household data, including data on dwelling type, building material of dwelling walls, number of rooms and age of the dwelling. Data on home ownership. Data was also collected on the number and sex of household members and their relationship to the household head. Data on household heads included their population group, age and marital status. Income data was also collected, for husbands and wives. Data on home ownership, household size and domestic workers was also collected, but for Urban households only.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources.

The Litchfield County Court African Americans and Native Americans Collection is an artificial collection consisting of photocopies of cases involving persons of African descent and indigenous people from the Files and Papers by Subject series of Litchfield County Court records. This collection was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers.

Collection Overview

The collection consists of records of 188 court cases involving either African Americans or Native Americans. A careful search of the Files for the Litchfield County Court discovered 165 on African Americans and 23 on Native Americans, about one third of the total that was found in Files for the New London County Court for the period up to the American Revolution. A couple of reasons exist for this vast difference in numbers. First, Litchfield County was organized much later than New London, one of Connecticut's four original counties. New London was the home of four of seven recognized tribes, was a trading center, and an area of much greater wealth. Second, minority population in the New London County region has been tracked and tabulated by Barbara Brown and James Rose in Black Roots of Southeastern Connecticut.¹ Although this valuable work does not include all of Negro or Indian background, it provides a wonderful starting point and it has proven to be of some assistance in tracking down minorities in Litchfield County. In most instances, however, identification is based upon language in the documents and knowledge of surnames or first names.² Neither surname nor first name provides an invariably reliable guide so it is possible that some minorities have been missed and some persons included that are erroneous.

In thirteen of 188 court cases, the person of African or Native American background cannot be identified even by first name. He or she is noted as "my Negro," a slave girl, or an Indian. In twenty-three lawsuits, a person with a first name is identified as a Negro, as an Indian in two other cases, and Mulatto in one. In the remaining 151 cases, a least one African American or Native American is identified by complete name.³ Thirteen surnames recur in three or more cases.⁴ A total of seventy surnames, some with more than one spelling, are represented in the records.

The Jacklin surname appears most frequently represented in the records. Seven different Jacklins are found in eighteen cases, two for debt and the remaining sixteen for more serious crimes like assault, breach of peace, keeping a bawdy house, and trespass.⁵ Ten cases concern Cuff Kingsbury of Canaan between 1808 and 1812, all involving debts against Kingsbury and the attempts of plaintiffs to secure writs of execution against him. Cyrus, Daniel, Ebenezer, Jude, Luke, Martin, Nathaniel, Pomp, Titus, and William Freeman are found in nine cases, some for debt, others for theft, and one concerning a petition to appoint a guardian for aged and incompetent Titus Freeman.⁶ Six persons with the surname Caesar are found in seven court cases.

Sixty-one of 188 cases concern debt.⁷ Litchfield County minorities were plaintiffs in only about ten of these lawsuits, half debt by book and half debt by note. The largest single category of court proceedings concern cases of crimes against person or property. They include assault (32 cases), theft (30), breach of peace (5), and breaking out of jail (1). In cases of assault, the Negro or Indian was the perpetrator in about two thirds of the cases and victim in one third. In State v. Alexander Kelson, the defendant was accused of assaulting Eunice Mawwee.⁸ Minority defendants in assault cases included Daniel K. Boham, William Cable, Prince Comyns, Adonijah Coxel, Homer Dolphin, Jack Jacklin, Pompey Lepean, John Mawwee, Zack Negro, and Jarvis Phillips. One breach of peace case, State v. Frederic Way, the defendant, "a transient Indian man," was accused of breach of the peace for threatening Jonathan Rossetter and the family of Samuel Wilson of Harwinton.⁹

In cases of theft, African Americans appeared as defendants in 27 of 30 cases, the only exceptions being two instances in which Negroes were illegally seized by whites and the case of State v. William Pratt of Salisbury. The State charged Pratt with stealing $35 from the house of George Ceasor.¹⁰ More typical, however, are such cases as State v. Prince Cummins for the theft of a dining room table and State v. Nathaniel Freeman for the theft of clothes.¹¹

Another major category of lawsuits revolves around the subject of slaves as property. The number and percentage of such cases is much lower than that for New London County due to the fact that the county was only organized one generation before the American Revolution and the weaker grip the institution of slavery had in that county. The cases may be characterized as conversion to own use (4), fraudulent contract (3), fraudulent sale (3), runaways (3), illegal enslavement (2), and trespass (2).¹² The Litchfield County Court in April 1765 heard George Catling v. Moses Willcocks, a case in which Willcocks was accused of converting a slave girl and household goods to his own use.¹³ In the 1774 fraudulent contract case of Josiah Willoughby v. Elisha Bigelow, the plaintiff accused Bigelow of lying about York Negro's age and condition. Willoughby stated that York Negro was twenty years older that he was reputed to be, was blind in one eye, and "very intemperate in the use of Speretuous Lickor." He sued to recover the purchase price of £45, the court agreed, and the defendant appealed.¹⁴ Cash Africa sued Deborah Marsh of Litchfield in 1777 for illegal enslavement. He claimed that he was unlawfully seized with force and arms and compelled to labor for the defendant for three years.¹⁵ In another case, David Buckingham v. Jonathan Prindle, the defendant was accused of persuading Jack Adolphus to run away from his master. The plaintiff claimed that Adolphus was about twenty years old and bound to service until age twenty-five, when he would be freed under terms of Connecticut's gradual emancipation law.¹⁶

Other subjects found in Litchfield County Minorities include defamation, gambling, keeping a bawdy house, and lascivious carriage. The defamation cases all included the charge of sexual intercourse with an Indian or Negro. In one such case, Henry S. Atwood v. Norman Atwood, both of Watertown, the defendant defamed and slandered the plaintiff by charging that he was "guilty of the crime of fornication or adultery with [a] Black or Negro woman," the wife of Peter Deming.¹⁷ Three cases, two from 1814 and one from 1821, accuse several Negroes accuse Harry Fitch, Polly Gorley, Violet Jacklin, Betsy Mead, and Jack Peck alias Jacklin, of running houses of ill repute.¹⁸

The records on African Americans and Native Americans from Litchfield County are relatively sparse, but they do provide some indication of the difficulties encountered by minorities in white society. They also provide some useful genealogical data on a handful of families in northwestern Connecticut.

1. Barbara W. Brown and James M. Rose, joint authors, Black Roots in Southeastern Connecticut, 1650-1900 (Detroit: Gale Research Co., 1980).
2. The court cases often identify minorities by the words Negro, mulatto, colored, or Indian.
3. Two or more African Americans or Native Americans are found in 27 lawsuits, but a maximum of two people are included in the Litchfield County Minorities database.
4. Surnames with spelling variations: Boston (3), Botsford (4), Caesar (7), Coxel (3), Freedom (3), Freeman (9), Gauson (5), Jacklin (17), Kingsbury (10), Leopen (4), Mawwee (5), Quomenor (4), and Smith (3).
5. George, Harvey, Isaac, Jack, Philip, Violet, and William Jacklin. Also included is Jack Peck, alias Jack Jacklin.
6. For the last case, see Conservators and Guardians, Box 2, folder 42.
7. Fifty-seven suits for debt, the vast majority of which a minority was plaintiff or defendant, and four concerning writs of execution to recover debt owed.
8. Dec. 1836, Box 3, folder 16.
9. Sep. 1796, Box 3, folder 6.
10. David King v. Stephen Walton, Mar. 1791, Box 1, folder 17;Simon Mitchel v. Edward Hinman, Dec. 1793, Box 1, folder 18; State v. William Pratt, Oct. 1848, Box 2, folder 37.
11. Apr. 1828, Box 2, folder 23; Oct. 1837, Box 2, folder 29.
12. Three additional conversion cases concern livestock and hay.
13. Apr. 1765, Box 1, folder 5.
14. Dec. 1774, Box 1, folder 9.
15. Sep. 1777, Box 1, folder 9.
16. Dec. 1813, Box 1, folder 49.
17. Dec. 1814, Box 2, folder 2.
18. Sep. 1814, Box 2, folder 3, Sep. 1814, Box 2, folder 4; Sep. 1821, Box 2, folder 15.

If a record of interest is found, and a reproduction of the original record is desired, you may submit a request via <a

Abstract

The 1970 South African Population Census collected data on dwellings and individuals' demographic, migration, family and employment details.

Geographic coverage

National coverage of the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal, and the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, Kwandebele, Transkei and Bophuthatswana.

Analysis unit

The units of analysis for the South African Census 1970 were households and individuals

Universe

The South African population census of 1970 covered all de jure household members (usual residents) of South Africa and the "national states".

The Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 1970 Census was a full count for Whites, Coloureds and Asians, and a 5% sample for Blacks (Africans)

Sampling deviation

The country was divided into 400 census districts for the 1970 Census. In most cases the boundaries of the census districts corresponded with those of the magisterial districts. However, in some cases the boundaries did not correspond, particularly in the areas in and around the "National States". This was to facilitate the administration of the census and to make it easier to exclude figures of the "National states" from provincial totals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1970 Population Census of the Republic of South Africa questionnaires were: Form 01, to be completed by "Whites, Coloured and Asiatics" Form 02, to be completed by "Bantu" Form 03, for families, households and dwellings

Form 01 collected data on relationship to household head, population group, sex, age, marital status, place of birth, and citizenship, as well as usual place of residence, home language, religion, level of education and income. Employment data collected included occupation, employment status and industry type.

Form 02 collected data for African South Africans on relationship to household head, sex, age, marital status, fertility, place of birth, home language and literacy, religion and level of education. Employment data collected included occupation, employment status and industry type.

Form 03 collected household data, including data on dwelling type, building material of dwelling walls, number of rooms and age of the dwelling. Data on home ownership. Data was also collected on the number and sex of household members and their relationship to the household head. Data on household heads included their population group, age and marital status. Income data was also collected, for husbands and wives. Data on home ownership, household size and domestic workers was also collected, but for Urban households only.

Biometric Attack Dataset, Black People

The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of black people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group.

The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.

The dataset contains images and videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

People in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F224e4e37b00a04546bbeaeded5fd3213%2FFrame%2095.png?generation=1712226592271540&alt=media" alt="">

Types of files in the dataset:

• photo - selfie of the person
• video - real video of the person

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• video_extension - video extensions in the dataset
• video_resolution - video resolution in the dataset
• video_duration - video duration in the dataset
• video_fps - frames per second for video in the dataset
• photo_extension - photo extensions in the dataset
• photo_resolution - photo resolution in the dataset

Statistics for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9682c567213f0e6e99fecc3c6b511a9d%2FFrame%2096.png?generation=1712832044284031&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset consists of: - files - includes 10 folders corresponding to each person and including 1 image and 1 video, - .csv file - contains information about the files and people in the dataset

File with the extension .csv

• id: id of the person,
• selfie_link: link to access the photo,
• video_link: link to access the video,
• age: age of the person,
• country: country of the person,
• gender: gender of the person,
• video_extension: video extension,
• video_resolution: video resolution,
• video_duration: video duration,
• video_fps: frames per second for video,
• photo_extension: photo extension,
• photo_resolution: photo resolution

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F77905aea23afb7f61167bc9ccd0d98cb%2F7-ezgif.com-optimize.gif?generation=1707303271936246&alt=media" alt="">

🚀 You can learn more about our high-quality unique datasets here

keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File.

BackgroundStudies on the barriers migrant women face when trying to access healthcare services in South Africa have emphasized economic factors, fear of deportation, lack of documentation, language barriers, xenophobia, and discrimination in society and in healthcare institutions as factors explaining migrants’ reluctance to seek healthcare. Our study aims to visualize some of the outcome effects of these barriers by analyzing data on maternal death and comparing the local population and black African migrant women from the South African Development Countries (SADC) living in South Africa. The heightened maternal mortality of black migrant women in South Africa can be associated with the hidden costs of barriers migrants face, including xenophobic attitudes experienced at public healthcare institutions.MethodsOur analysis is based on data on reported causes of death (COD) from the South African Department of Home Affairs (DHA). Statistics South Africa (Stats SA) processed the data further and coded the cause of death (COD) according to the WHO classification of disease, ICD10. The dataset is available on the StatsSA website (http://nesstar.statssa.gov.za:8282/webview/) for research and statistical purposes. The entire dataset consists of over 10 million records and about 50 variables of registered deaths that occurred in the country between 1997 and 2018. For our analysis, we have used data from 2002 to 2015, the years for which information on citizenship is reliably included on the death certificate. Corresponding benchmark data, in which nationality is recorded, exists only for a 10% sample from the population and housing census of 2011. Mid-year population estimates (MYPE) also exist but are not disaggregated by nationality. For this reason, certain estimates of death proportions by nationality will be relative and will not correspond to crude death rates.ResultsThe total number of female deaths recorded from the years 2002 to 2015 in the country was 3740.761. Of these, 99.09% (n = 3,707,003) were deaths of South Africans and 0.91% (n = 33,758) were deaths of SADC women citizens. For maternal mortality, we considered the total number of deaths recorded for women between the ages of 15 and 49 years of age and were 1,530,495 deaths. Of these, deaths due to pregnancy-related causes contributed to approximately 1% of deaths. South African women contributed to 17,228 maternal deaths and SADC women to 467 maternal deaths during the period under study. The odds ratio for this comparison was 2.02. In other words, our findings show the odds of a black migrant woman from a SADC country dying of a maternal death were more than twice that of a South African woman. This result is statistically significant as this odds ratio, 2.02, falls within the 95% confidence interval (1.82–2.22).ConclusionThe study is the first to examine and compare maternal death among two groups of women, women from SADC countries and South Africa, based on Stats SA data available for the years 2002–2015. This analysis allows for a better understanding of the differential impact that social determinants of health have on mortality among black migrant women in South Africa and considers access to healthcare as a determinant of health. As we examined maternal death, we inferred that the heightened mortality among black migrant women in South Africa was associated with various determinants of health, such as xenophobic attitudes of healthcare workers toward foreigners during the study period. The negative attitudes of healthcare workers toward migrants have been reported in the literature and the media. Yet, until now, its long-term impact on the health of the foreign population has not been gaged. While a direct association between the heightened death of migrant populations and xenophobia cannot be established in this study, we hope to offer evidence that supports the need to focus on the heightened vulnerability of black migrant women in South Africa. As we argued here, the heightened maternal mortality among migrant women can be considered hidden barriers in which health inequality and the pervasive effects of xenophobia perpetuate the health disparity of SADC migrants in South Africa.

Context

Percentage of resident persons who declared themselves black in relation to the total resident population, at the reference date of the Demographic Census. Source: IBGE, Demographic Census 2010 and Municipal fabric 2010. http://www.geoservicos.ibge.gov.br/geoserver/wms?service=WFS&version=1.0.0&request=GetFeature&typeName=CGEO:vw_per_black_people& om the dataset summary Population Census and Mesh ... License not specified spatial: "type": "Polygon", "coordinates": [[- [- 74.0046, -33.7411], [- 34.7929, -33.7411], [- 34.7929,5.2727], [- 74.0046,5.2727], [- 74.0046, -33.7411 ]]] http://dados.gov.br/dataset/cgeo_vw_per_pessoas_pretas

Content

Author and Maintainer: Geosciences Directorate - IBGE and Research Directorate - IBGE Last update: June 12, 2018 package id: 4565a7e3-9509-43dc-b074-433451ef7a47 Organ - Sphere: Federal. Organ - Power: Executive.

Acknowledgements

Geosciences Directorate - IBGE and Research Directorate - IBGE http://dados.gov.br

Photo by Anomaly on Unsplash

Inspiration

Nelson Mandela: was a South African anti-apartheid revolutionary, political leader, and philanthropist who served as President of South Africa from 1994 to 1999. He was the country's first black head of state and the first elected in a in a fully representative democratic election. His government focused on dismantling the legacy of apartheid by tackling institutionalized racism and fostering racial reconciliation. https://en.wikipedia.org/wiki/Nelson_Mandela

Martin Luther King Jr. (January 15, 1929 – April 4, 1968) was an American Christian minister and activist who became the most visible spokesperson and leader in the Civil Rights Movement from 1955 until his assassination in 1968. Born in Atlanta Georgia, King is best known for advancing civil rights through nonviolence and civil disobedience, inspired by his Christian beliefs and the nonviolent activism of Mahatma Gandhi. https://en.wikipedia.org/wiki/Martin_Luther_King_Jr.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

According to the 2021 Census, 81.7% of the population of England and Wales was white, 9.3% Asian, 4.0% black, 2.9% mixed and 2.1% from other ethnic groups.

Throughout the history of the transatlantic slave trade, approximately 5.7 million of the 12.5 million African slaves who embarked on slave ships did so in ports along the region of West Central Africa and St. Helena. Today, these regions are in the countries of Angola, Democratic Republic of the Congo, Gabon, and the Republic of the Congo. The majority of the rest were taken from West Africa, embarking in ports between the present-day countries of Senegal and Gabon, while a smaller number of slaves were captured in the southeast of Africa. Senegambia and off-shore Atlantic islands had the highest number of captives taken from that region in the 16th century, however West Central Africa and St. Helena was the region where most slaves embarked on their journey across the Atlantic in the following centuries. As Portuguese traders were responsible for transporting the largest volume of slaves to the Americas, it is unsurprising that many of the busiest ports in the transatlantic slave trade were in Portuguese-controlled enclaves along the African coast.