Abstract

Collected data from the South India Community Health Study (SICHS), which covers a rural population of 1.1 million individuals residing in Vellore district in Tamil Nadu. The study includes a census of all 298,000 households drawn from 57 castes and a detailed survey of 5,000 representative households. The census, completed in 2014, provides a comprehensive demographic and socioeconomic profile of the study area. The household survey, conducted in 2016, collected information on marriage patterns, including within-caste marriage, close-kin marriage, arranged marriage, and migration of spouses between villages.

The SICHS was designed to examine a variety of socioeconomic phenomena and health problems, including the treatment of tuberculosis. The study area thus comprises three Tuberculosis Units (TU’s) within Vellore district that were purposefully selected to be representative of rural South India.

Geographic coverage

Vellore district, Tamil Nadu, India.

Analysis unit

Households

Universe

Rural population of 1.1 million individuals in Vellore district, Tamil Nadu.

Kind of data

Sample survey data [ssd]

Sampling procedure

South India Community Health Study (SICHS) has two components:

A census of all 298,000 households drawn from 57 castes residing on the study area, completed in 2014. A detailed survey of 5,000 representative households, completed in 2016.

The sampling frame for the household survey included all ever-married men aged 25-60 in the SICHS census plus (a small number of) divorced or widowed women with “missing” husbands who would have been aged 25-60, based on the average age-gap between husbands and wives. The sample was subsequently drawn to be representative of each caste in the study area, excluding castes with less than 100 households in the census.

Research instrument

• Female primary respondent (FPR) questionnaire.
• Male primary respondent (MPR) questionnaire.
• Household roster questionnaire.
• Spouse (SPO) os MPR questionnaire.
• NIRT census questionnaire.

In 2019, the mid-year population of India was approximately 1.34 billion people. Comparatively, the population of the Maldives was approximately five hundred thousand in mid-2019.

South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language–speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

Quality filtered GSA data of the individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.

Census: Population: West Bengal: Chak South Enayetnagar data was reported at 6,754.000 Person in 03-01-2011. This records an increase from the previous number of 5,664.000 Person for 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar data is updated decadal, averaging 6,209.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 6,754.000 Person in 03-01-2011 and a record low of 5,664.000 Person in 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC037: Census: Population: By Towns and Urban Agglomerations: West Bengal.

Census: Population: West Bengal: Chak South Enayetnagar: Male data was reported at 3,491.000 Person in 03-01-2011. This records an increase from the previous number of 2,862.000 Person for 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar: Male data is updated decadal, averaging 3,176.500 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 3,491.000 Person in 03-01-2011 and a record low of 2,862.000 Person in 03-01-2001. Census: Population: West Bengal: Chak South Enayetnagar: Male data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC037: Census: Population: By Towns and Urban Agglomerations: West Bengal.

Seven human-specific Alu markers were studied in 574 unrelated individuals from 10 endogamous groups and 2 hill tribes of Tamil Nadu and Kerala states. DNA was isolated, amplified by PCR-SSP, and subjected to agarose gel electrophoresis, and genotypes were assigned for various Alu loci. Average heterozygosity among caste populations was in the range of 0.292–0.468. Among tribes, the average heterozygosity was higher for Paliyan (0.3759) than for Kani (0.2915). Frequency differences were prominent in all loci studied except Alu CD4. For Alu CD4, the frequency was 0.0363 in Yadavas, a traditional pastoral and herd maintaining population, and 0.2439 in Narikuravars, a nomadic gypsy population. The overall genetic difference (Gst) of 12 populations (castes and tribes) studied was 3.6%, which corresponds to the Gst values of 3.6% recorded earlier for Western Asian populations. Thus, our study confirms the genetic similarities between West Asian populations and South Indian castes and tribes and supported the large scale coastal migrations from Africa into India through West Asia. However, the average genetic difference (Gst) of Kani and Paliyan tribes with other South Indian tribes studied earlier was 8.3%. The average Gst of combined South and North Indian Tribes (CSNIT) was 9.5%. Neighbor joining tree constructed showed close proximity of Kani and Paliyan tribal groups to the other two South Indian tribes, Toda and Irula of Nilgiri hills studied earlier. Further, the analysis revealed the affinities among populations and confirmed the presence of North and South India specific lineages. Our findings have documented the highly diverse (micro differentiated) nature of South Indian tribes, predominantly due to isolation, than the endogamous population groups of South India. Thus, our study firmly established the genetic relationship of South Indian castes and tribes and supported the proposed large scale ancestral migrations from Africa, particularly into South India through West Asian corridor.

In 2011, there were over 382 million individuals on average per square kilometer across the south east Asian country of India. Furthermore, there were over 3.6 thousand individuals per square kilometer in the urban regions and over 279 individuals per square kilometer in the rural regions of the country.

Comprehensive population and demographic data for White Gate South Village

Complete mitogenomes of the individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.

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. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – 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 Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

Comprehensive population and demographic data for Thethakudi South Village

In India, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was ** percent. Hyderabad topped the list with the highest share of middle-class and above category of consumers. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

South Asia is one of the most densely populated regions in the world. This dataset comprehensively collects historical materials related to the population of South Asia and previous research results (see data description documents and references for details), carefully examines and estimates the population of South Asia (now India, Pakistan, Nepal, Bangladesh) from 640 to 1801 AD, and connects it with the population census data of British India from 1871 to 1941 (Nepal's data comes from Nepal's census data) and the United Nations World Population Prospects data from 1950 to 2020, obtaining the population of South Asia for a total of 22 periods (640, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1595, 1750, 1801, 1871, 1901, 1921, 1941, 1960, 1980, 2000, 2010, 2020) from 640 to 2020. Next, based on geographic detectors, select the dominant environmental factors that affect the spatial distribution of population, collect historical data on the distribution of residential areas (see data description document and references for details), and use a random forest regression model to spatialize the population size. On the basis of excluding uninhabited areas such as water bodies, glaciers, and bare/unused land, and determining the maximum historical population distribution range, a 1km resolution population dataset for South Asia from 640 to 2020 was developed. The leave one method was used to test the model, and the variance explained was 0.81, indicating good model accuracy. Compared with the existing HYDE historical population dataset, this study incorporates more historical materials and the latest research results in estimating the historical population; In using random forest regression for historical population spatial simulation, this study considers the changes in South Asian settlements over the past millennium, while the HYDE dataset only considers natural elements and considers them stable and unchanged. Therefore, this dataset is more reliable than the HYDE dataset and can more reasonably reveal the spatiotemporal characteristics of population changes in South Asia during historical periods. It is the basic data for the long-term evolution of human land relations, climate change attribution, and ecological protection research in South Asia.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2021. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Institutional Ethics Committee vide No.1299.

Data in table tells us about the year-wise estimated population of wild elephants in different regions of India. Regions mentioned are- North East includes: Arunachal Pradesh, Assam, Meghalaya, Nagaland, Mizoram, Manipur, Tripura and West Bengal (North); East includes: West Bengal (South), Jharkhand, Odisha, Chhattisgarh: North includes: Uttarakhand, Uttar Pradesh; and South includes Tamil Nadu, Karnataka, Kerala, Andhra Pradesh and Maharashtra.

Note: 1) Meghalaya and Uttarakhand have not conducted elephant census after 2007.Therefore the figure of 2007 has been maintained for 2012 as well. 2) The figure for North and South Bengals are combined 3) Gap between 1993 and 1997- only 4 years.

Comprehensive population and demographic data for Salem South Tehsil

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

It was estimated that by 2050, India's Muslim population would grow by ** percent compared to 2010. For followers of the Hindu faith, this change stood at ** percent. According to this projection, the south Asian country would be home not just to the world's majority of Hindus, but also Muslims by this time period. Regardless, the latter would continue to remain a minority within the country at ** percent, with ** percent or *** billion Hindus at the forefront by 2050.