This feature layers contain demographics about age, gender, education, employment, assets & amenities as reported by Office of the Registrar General & Census Commissioner, India in the Census 2011. These attributes cover topics such as male and female population counts by age, literacy, occupation, and household characteristics.Census of India counts every resident in India at village level. It is mandated by The Census Act 1948 of the Constitution and takes place every 10 years.Other demographics layers are also available:Country DemographicsDistrict DemographicsSub-district DemographicsVillage DemographicsCombined DemographicsEach layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map has increasing detail as you zoom in to smaller areas.Data source: Explore Census DataAdmin boundary source (country, states, and districts): Survey of India, 2020For more information: 2011 Census Demographic ProfileFor feedback please contact: content@esri.inData Processing notes:Country, State and District boundaries are simplified representations offered from the Survey of India database.Sub-districts and village boundaries are developed based on the census provided maps.Field names and aliases are processed by Esri India as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.Disclaimer:The boundaries may not be perfectly align with AGOL imagery. The Census PDF maps are georeferenced using Survey of India boundaries and notice alignment issues with AGOL Imagery/ Maps. 33k villages are marked as point location on Census PDFs either because of low scale maps where small villages could not have been drawn or digitization has not been completed. These villages are marked as 100m circular polygons in the data.This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.
Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics
As of June 2024, there were around 3.09 million ethnic Chinese residents in Singapore. Singapore is a multi-ethnic society, with residents categorized into four main racial groups: Chinese, Malay, Indian, and Others. Each resident is assigned a racial category that follows the paternal side. This categorization would have an impact on both official as well as private matters. Modelling a peaceful, multi-ethnic society The racial categorization used in Singapore stemmed from its colonial past and continues to shape its social policies, from public housing quotas along the ethnic composition in the country to education policies pertaining second language, or ‘mother tongue’, instruction. Despite the emphasis on ethnicity and race, Singapore has managed to maintain a peaceful co-existence among its diverse population. Most Singaporeans across ethnic levels view the level of racial and religious harmony there to be moderately high. The level of acceptance and comfort with having people of other ethnicities in their social lives was also relatively high across the different ethnic groups. Are Singaporeans ready to move away from the CMIO model of ethnic classification? In recent times, however, there has been more open discussion on racism and the relevance of the CMIO (Chinese, Malay, Indian, Others) ethnic model for Singaporean society. The global discourse on racism has brought to attention the latent discrimination felt by the minority ethnic groups in Singapore, such as in the workplace. In 2010, Singapore introduced the option of having a ‘double-barreled’ race classification, reflecting the increasingly diverse and complicated ethnic background of its population. More than a decade later, there have been calls to do away from such racial classifications altogether. However, with social identity and policy deeply entrenched along these lines, it would be a challenge to move beyond race in Singapore.
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.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data at 150m grids across Asia / MENA. Understand who lives in a catchment, where they work and their spending potential to make more informed decisions.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
In 2011, 87.2 percent of the total population of the United Kingdom were white British. A positive net migration in recent years combined with the resultant international relationships following the wide-reaching former British Empire has contributed to an increasingly diverse population.
Varied ethnic backgrounds
Black British citizens, with African and/or African-Caribbean ancestry, are the largest ethnic minority population, at three percent of the total population. Indian Britons are one of the largest overseas communities of the Indian diaspora and make up 2.3 percent of the total UK population. Pakistani British citizens, who make up almost two percent of the UK population, have one of the highest levels of home ownership in Britain.
Racism in the United Kingdom
Though it has decreased in comparison to the previous century, the UK has seen an increase in racial prejudice during the first decade and a half of this century. Racism and discrimination continues to be part of daily life for Britain’s ethnic minorities, especially in terms of work, housing, and health issues. Moreover, the number of hate crimes motivated by race reported since 2012 has increased, and in 2017/18, there were 3,368 recorded offenses of racially or religiously aggravated assault with injury, almost a thousand more than in 2013/14.
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.
White – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.
Black or African American – A person having origins in any of the Black racial groups of Africa.
American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.
Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.
Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.
Some Other Race - this category is chosen by people who do not identify with any of the categories listed above.
People can identify with more than one race. These people are included in the Two or More Races
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Young people from the Chinese (4.5%) and Indian (7.3%) ethnic groups were less likely than the UK average (11.5%) to be not in employment, education or training.
The population of India is divided into several groups based on social, educational, and financial statuses. The formation of these groups is a result of the historical social structure of the country. Between 2019 and 2021, Other Backward Class (OBC) constituted the largest part of Indian households accounting for about 42 percent. On the other hand, Schedule Tribes formed about ten percent of households.
How prosperous is India’s caste-based society?
India suffers from extreme social and economic inequality. The combined share of Schedule Tribe and Schedule Caste in the affluent population of India was less than 30 percent. Contrary to this, economically and socially stronger groups constituted the major part of the affluent population. Hence, indicating a strong relationship between caste and prosperity.
India’s thoughts on caste-based reservation
The constitution of India provides reservations to the weaker sections of the society for their upliftment and growth. However, the need for reservation has increased with time, making the whole situation even more complicated. People are divided over the existence of a system that provides preference to certain castes or sects.
In a survey conducted in 2016 about providing employment reservation to young adults of Schedule Caste and Schedule Tribe, many people expressed opposition. More than 40 percent of opposition came from upper Hindu caste. Minimum opposition was observed from the people belonging to Schedule Tribe and Schedule Caste.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
It contains survey data collected in Kaski and Chitwan districts of Nepal between 2014 and 2016, related to education and skills, migration, caste relations, and cultural consumption. Survey data entails 837 variables and 1203 respondents. Field research were carried out for a year and half in these villages and in their migration satellites in the Tarai (the Gangetic strip of south Nepal abutting India), in urban centres, and international migrants also were included. Participant observation and interviews were combined with detailed surveys of both households and individuals in order to reveal changing attitudes to education, employment, and migration. The two next-biggest local ethnic groups, the Chhetris and Gurungs, who rank in between Bahuns and Dalits in the traditional caste hierarchy, were also included in the quantitative part of the study in order to bring out contrasts and comparisons. By producing an empirically sound, ethnographically sensitive, and quantitatively sophisticated study of the social history and migration of these two key Nepali groups, one of which is the most significant disadvantaged caste bloc, the research aimed having considerable potential policy impact in Nepal. The timing of research, coming as it did during the ongoing peace process and while disadvantage and exclusion are still very much part of the political debate, was appropriate and indeed advantageous.Nepal, like India, has traditionally been a caste society, with Bahuns (Brahmans) at the top, Chhetris (Kshatriyas) second, and Dalits (ex-Untouchables) at the bottom. Groups that used to be known as tribes and are now called Janajatis (the groups most commonly recruited to the Gurkha regiments) were slotted into the middle of the hierarchy. Between 1854 and 1951 this caste hierarchy was enforced in an authoritarian way by the state, and until 1963 regulated by law. In India, Dalits have, since 1947, if not before, benefited from positive discrimination in government employment and gradually in education. In Nepal there were till recently no such provisions. Comparing different groups in the country, Nepali Dalits today have the lowest life expectancy, the highest rates of illiteracy, the worst job prospects, the lowest incomes and wealth, and the worst rates of achievement in education. Of all groups of any size they are most disadvantaged and the most discriminated against. Bahuns, by contrast, do extremely well in education, have higher levels of educational attainment, and obtain more elite and professional jobs than any other group. They also provide the bulk of the political elite. Neither Dalits nor Bahuns have been studied as much they should have, given their importance in Nepali society, and this study aims to fill this gap. The political situation in Nepal is in flux. The Constituent Assembly, elected in April 2008 on the most inclusive franchise ever used in Nepal (surpassing even India's measures to ensure representation for marginal groups), failed ignominiously to produce a constitution, even after four years and four extensions of time, in May 2012. The Supreme Court refused to prolong the Assembly, leaving Nepal with a caretaker Prime Minister, no parliament, and an uncertain future. The key issue, over which the constitution-writing faltered, was that of ethnicity. In this context, it was essential to understand from the bottom up, the new process of ethnic identity formation among Bahuns and Dalits - a reaction to the much longer-standing and politically more assertive ethnicity formation among ex-tribal Janajati groups. This project aimed to examine in detail exactly how the patterns of disadvantage and exclusion, on the one hand, and achievement and success, on the other, are produced and reproduced. In doing so it focused on six neighbouring villages in west central Nepal where the two largest population groups are Bahuns and Dalits. The qualitative data was collected through semi-structured interviews with Dalit activists, scholars and politicians using purposive sampling. The quantitative data was collected through a survey of individuals. It was built upon an initial census-type household exploration representing different caste groups, including Bahuns and Dalits, from a set of six neighbouring villages. First, households were selected using stratified random sample (caste and class) taking 50% of the original households, and individuals 13 years or older were administered the survey questionnaire. Some temporarily migrated individuals, as well as some permanent migrants, were also interviewed at their migration destinations in Nepal. Altogether 1,203 respondents were covered in the face to face survey.
In 2022, the child abuse rate for children of Hispanic origin was at 7, indicating 7 out of every 1,000 Hispanic children in the United States suffered from some sort of abuse. This rate was highest among American Indian or Alaska Native children, with 14.3 children out of every 1,000 experiencing some form of abuse. Child abuse in the U.S. The child abuse rate in the United States is highest among American Indian or Alaska Native victims, followed by African-American victims. It is most common among children between two to five years of age. While child abuse cases are fairly evenly distributed between girls and boys, more boys than girls are victims of abuse resulting in death. The most common type of maltreatment is neglect, followed by physical abuse. Risk factors Child abuse is often reported by teachers, law enforcement officers, or social service providers. In the large majority of cases, the perpetrators of abuse were a parent of the victim. Risk factors, such as teen pregnancy, violent crime, and poverty that are associated with abuse and neglect have been found to be quite high in the United States in comparison to other countries.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.
In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).
Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.
This feature layers contain demographics about age, gender, education, employment, assets & amenities as reported by Office of the Registrar General & Census Commissioner, India in the Census 2011. These attributes cover topics such as male and female population counts by age, literacy, occupation, and household characteristics.Census of India counts every resident in India at village level. It is mandated by The Census Act 1948 of the Constitution and takes place every 10 years.Other demographics layers are also available:Country DemographicsDistrict DemographicsSub-district DemographicsVillage DemographicsCombined DemographicsEach layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map has increasing detail as you zoom in to smaller areas.Data source: Explore Census DataAdmin boundary source (country, states, and districts): Survey of India, 2020For more information: 2011 Census Demographic ProfileFor feedback please contact: content@esri.inData Processing notes:Country, State and District boundaries are simplified representations offered from the Survey of India database.Sub-districts and village boundaries are developed based on the census provided maps.Field names and aliases are processed by Esri India as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.Disclaimer:The boundaries may not be perfectly align with AGOL imagery. The Census PDF maps are georeferenced using Survey of India boundaries and notice alignment issues with AGOL Imagery/ Maps. 33k villages are marked as point location on Census PDFs either because of low scale maps where small villages could not have been drawn or digitization has not been completed. These villages are marked as 100m circular polygons in the data.This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.