70 datasets found
  1. T

    South Asia - Individuals Using The Internet (% Of Population)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jun 9, 2017
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    TRADING ECONOMICS (2017). South Asia - Individuals Using The Internet (% Of Population) [Dataset]. https://tradingeconomics.com/south-asia/individuals-using-the-internet-percent-of-population-wb-data.html
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    Jun 9, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Asia, South Asia
    Description

    Individuals using the Internet (% of population) in South Asia was reported at 42.85 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Individuals using the Internet (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

  2. T

    South Asia - Population Density (people Per Sq. Km)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jul 22, 2013
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    TRADING ECONOMICS (2013). South Asia - Population Density (people Per Sq. Km) [Dataset]. https://tradingeconomics.com/south-asia/population-density-people-per-sq-km-wb-data.html
    Explore at:
    json, xml, excel, csvAvailable download formats
    Dataset updated
    Jul 22, 2013
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Asia, South Asia
    Description

    Population density (people per sq. km of land area) in South Asia was reported at 492 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  3. Total population of the ASEAN countries from 2020 to 2030

    • statista.com
    • ai-chatbox.pro
    Updated May 27, 2025
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    Statista (2025). Total population of the ASEAN countries from 2020 to 2030 [Dataset]. https://www.statista.com/statistics/796222/total-population-of-the-asean-countries/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In 2023, the total population of all ASEAN states amounted to an estimated 619.02 million inhabitants. The ASEAN (Association of Southeast Asian Nations) member countries are Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. ASEAN opportunity The Association of Southeast Asian Nations was founded by five states (Thailand, Indonesia, the Philippines, Malaysia, and Singapore) in 1967 to improve economic and political stability and social progress among the member states. It was originally modelled after the European Union. Nowadays, after accepting more members, their agenda also includes an improvement of cultural and environmental conditions. ASEAN is now an important player on the global stage with numerous alliances and business partners, as well as more contenders wanting to join. The major player in the SouthIndonesia is not only a founding member of ASEAN, it is also its biggest contributor in terms of gross domestic product and is also one of the member states with a positive trade balance. In addition, it has the highest number of inhabitants by far. About a third of all people in the ASEAN live in Indonesia – and it is also one of the most populous countries worldwide. Among the ASEAN members, it is certainly the most powerful one, not just in numbers, but mostly due to its stable and thriving economy.

  4. T

    South Asia - Rural Population

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jul 23, 2013
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    TRADING ECONOMICS (2013). South Asia - Rural Population [Dataset]. https://tradingeconomics.com/south-asia/rural-population-percent-of-total-population-wb-data.html
    Explore at:
    json, excel, xml, csvAvailable download formats
    Dataset updated
    Jul 23, 2013
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Asia, South Asia
    Description

    Rural population (% of total population) in South Asia was reported at 63.67 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  5. d

    Loudoun County 2020 Census Population Patterns by Race and Hispanic or...

    • catalog.data.gov
    • data.virginia.gov
    • +1more
    Updated Jan 31, 2025
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    Loudoun County GIS (2025). Loudoun County 2020 Census Population Patterns by Race and Hispanic or Latino Ethnicity [Dataset]. https://catalog.data.gov/dataset/loudoun-county-2020-census-population-patterns-by-race-and-hispanic-or-latino-ethnicity
    Explore at:
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Loudoun County GIS
    Area covered
    Loudoun County
    Description

    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.

  6. N

    South San Francisco, CA Non-Hispanic Population Breakdown By Race Dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 21, 2025
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    Neilsberg Research (2025). South San Francisco, CA Non-Hispanic Population Breakdown By Race Dataset: Non-Hispanic Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/9a0a7c0f-ef82-11ef-9e71-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 21, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South San Francisco, California
    Variables measured
    Non-Hispanic Asian Population, Non-Hispanic Black Population, Non-Hispanic White Population, Non-Hispanic Some other race Population, Non-Hispanic Two or more races Population, Non-Hispanic American Indian and Alaska Native Population, Non-Hispanic Native Hawaiian and Other Pacific Islander Population, Non-Hispanic Asian Population as Percent of Total Non-Hispanic Population, Non-Hispanic Black Population as Percent of Total Non-Hispanic Population, Non-Hispanic White Population as Percent of Total Non-Hispanic Population, and 4 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To measure the two variables, namely (a) Non-Hispanic population and (b) population as a percentage of the total Non-Hispanic population, we initially analyzed and categorized the data for each of the racial categories idetified by the US Census Bureau. It is ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories, and are part of Non-Hispanic classification. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Non-Hispanic population of South San Francisco by race. It includes the distribution of the Non-Hispanic population of South San Francisco across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of South San Francisco across relevant racial categories.

    Key observations

    Of the Non-Hispanic population in South San Francisco, the largest racial group is Asian alone with a population of 27,324 (61.21% of the total Non-Hispanic population).

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Racial categories include:

    • White
    • Black or African American
    • American Indian and Alaska Native
    • Asian
    • Native Hawaiian and Other Pacific Islander
    • Some other race
    • Two or more races (multiracial)

    Variables / Data Columns

    • Race: This column displays the racial categories (for Non-Hispanic) for the South San Francisco
    • Population: The population of the racial category (for Non-Hispanic) in the South San Francisco is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each race as a proportion of South San Francisco total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

    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 South San Francisco Population by Race & Ethnicity. You can refer the same here

  7. Distribution of the global population by continent 2024

    • statista.com
    • ai-chatbox.pro
    Updated Mar 27, 2025
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    Statista (2025). Distribution of the global population by continent 2024 [Dataset]. https://www.statista.com/statistics/237584/distribution-of-the-world-population-by-continent/
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    Dataset updated
    Mar 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

  8. w

    Distribution of population per country full name in Southern Asia

    • workwithdata.com
    Updated May 8, 2025
    + more versions
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    Work With Data (2025). Distribution of population per country full name in Southern Asia [Dataset]. https://www.workwithdata.com/charts/countries?agg=sum&chart=bar&f=1&fcol0=region&fop0=%3D&fval0=Southern+Asia&x=country_long&y=population
    Explore at:
    Dataset updated
    May 8, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Asia, South Asia
    Description

    This bar chart displays population (people) by country full name using the aggregation sum in Southern Asia. The data is about countries.

  9. i

    Living Standards Survey 2003 - South Asia Labor Flagship Dataset - Bhutan

    • dev.ihsn.org
    Updated Apr 25, 2019
    + more versions
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    National Statistical Bureau (2019). Living Standards Survey 2003 - South Asia Labor Flagship Dataset - Bhutan [Dataset]. https://dev.ihsn.org/nada/catalog/72557
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    National Statistical Bureau
    Time period covered
    2003
    Area covered
    Bhutan
    Description

    Abstract

    South Asia Regional Flagship: More and Better Jobs in South Asia

    Employment is a major issue throughout the world. To enjoy life, people need productive jobs that remove them from the daily struggle of making ends meet. According to the International Labour Organization (ILO), as many as 30 million people lost their jobs as a result of the 2008 crisis. Youth unemployment is especially high and inequality has increased. As recent events in the Middle East and North Africa demonstrate, joblessness and inequality can trigger political instability and unrest.

    When the World Bank South Asia Region decided to initiate a yearly Flagship Report series, it was clear that the very first report needed to concentrate on the important topic of More and Better Jobs in South Asia. Although one of the fastest growing regions, South Asia is still home to the largest number of the world's poor and the pace of creating productive jobs has lagged behind economic growth. Conflict and social and gender issues also increase the challenge of generating more and more productive jobs. Without urgent action, the potential for the demographic dividend from about 150 million entrants to the labor force over the next decade may not be realized.

    The Flagship seeks to answer four questions, which could have implications beyond South Asia. • How is South Asia performing in creating more and better jobs? • Where are the better jobs? • What are constraints in supply and demand in moving towards better jobs? • How does conflict affect job creation?

    Kind of data

    Sample survey data [ssd]

    Mode of data collection

    Face-to-face [f2f]

  10. Number of internet users SEA 2014-2029

    • statista.com
    Updated Jul 1, 2025
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    Statista Research Department (2025). Number of internet users SEA 2014-2029 [Dataset]. https://www.statista.com/topics/9093/internet-usage-in-southeast-asia/
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    Dataset updated
    Jul 1, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    The number of internet users in Southeast Asia was forecast to continuously increase between 2024 and 2029 by 86.4 million users (+15.32 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach a new peak at 650.4 million in 2029. Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the data source clarifies, connection quality and usage frequency are distinct aspects, not taken into account here. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic, and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press, and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like Central Asia and Eastern Asia.

  11. Mobile internet users in Southeast Asia 2010-2029

    • statista.com
    Updated Jul 1, 2025
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    Statista Research Department (2025). Mobile internet users in Southeast Asia 2010-2029 [Dataset]. https://www.statista.com/topics/9093/internet-usage-in-southeast-asia/
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    Dataset updated
    Jul 1, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    South East Asia, Asia
    Description

    The number of smartphone users in Southeast Asia was forecast to continuously increase between 2024 and 2029 by in total 105.9 million users (+23.9 percent). After the nineteenth consecutive increasing year, the smartphone user base is estimated to reach 548.92 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Western Asia and Southern Asia.

  12. F

    South Asian Facial Images Dataset | Selfie & ID Card Images

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). South Asian Facial Images Dataset | Selfie & ID Card Images [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-selfie-id-south-asian
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    South Asia
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the South Asian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

    Facial Image Data

    The dataset contains over 8,000 facial image sets of South Asian individuals. Each set includes:

    Selfie Images: 5 high-quality selfie images taken under different conditions
    ID Card Images: 2 clear facial images extracted from different government-issued ID cards

    Diversity & Representation

    Geographic Diversity: Participants represent South Asian countries including India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives, and more
    Demographics: Individuals aged 18 to 70 years with a 60:40 male-to-female ratio
    File Formats: Images are provided in JPEG and HEIC formats for compatibility and quality retention

    Image Quality & Capture Conditions

    All images were captured with real-world variability to enhance dataset robustness:

    Lighting: Captured under diverse lighting setups to simulate real environments
    Backgrounds: A wide variety of indoor and outdoor backgrounds
    Device Quality: Captured using modern smartphones to ensure high resolution and clarity

    Metadata

    Each participant’s data is accompanied by rich metadata to support AI model training, including:

    Unique participant ID
    Image file names
    Age at the time of capture
    Gender
    Country of origin
    Demographic details
    File format information

    This metadata enables targeted filtering and training across diverse scenarios.

    Use Cases & Applications

    This dataset is ideal for a wide range of AI and biometric applications:

    Facial Recognition: Train accurate and generalizable face matching models
    KYC & Identity Verification: Enhance onboarding and compliance systems in fintech and government services
    Biometric Identification: Build secure facial recognition systems for access control and identity authentication
    Age Prediction: Train models to estimate age from facial features
    Generative AI: Provide reference data for synthetic face generation or augmentation tasks

    Secure & Ethical Collection

    Data Security: All images were securely stored and processed on FutureBeeAI’s proprietary platform
    Ethical Compliance: Data collection was conducted in full alignment with privacy laws and ethical standards
    Informed Consent: Every participant provided written consent, with full awareness of the intended uses of the data

    Dataset Updates & Customization

    To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

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  13. National Sample Survey 1993-1994 (50th round) - South Asia Labor Flagship...

    • dev.ihsn.org
    Updated Apr 25, 2019
    + more versions
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    National Sample Survey Organisation (2019). National Sample Survey 1993-1994 (50th round) - South Asia Labor Flagship Dataset - India [Dataset]. https://dev.ihsn.org/nada/catalog/72561
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    National Sample Survey Organisation
    Time period covered
    1993 - 1994
    Area covered
    India
    Description

    Abstract

    South Asia Regional Flagship: More and Better Jobs in South Asia

    Employment is a major issue throughout the world. To enjoy life, people need productive jobs that remove them from the daily struggle of making ends meet. According to the International Labour Organization (ILO), as many as 30 million people lost their jobs as a result of the 2008 crisis. Youth unemployment is especially high and inequality has increased. As recent events in the Middle East and North Africa demonstrate, joblessness and inequality can trigger political instability and unrest.

    When the World Bank South Asia Region decided to initiate a yearly Flagship Report series, it was clear that the very first report needed to concentrate on the important topic of More and Better Jobs in South Asia. Although one of the fastest growing regions, South Asia is still home to the largest number of the world's poor and the pace of creating productive jobs has lagged behind economic growth. Conflict and social and gender issues also increase the challenge of generating more and more productive jobs. Without urgent action, the potential for the demographic dividend from about 150 million entrants to the labor force over the next decade may not be realized.

    The Flagship seeks to answer four questions, which could have implications beyond South Asia. - How is South Asia performing in creating more and better jobs? - Where are the better jobs? - What are constraints in supply and demand in moving towards better jobs? - How does conflict affect job creation?

    Geographic coverage

    National

    Kind of data

    Sample survey data [ssd]

    Mode of data collection

    Face-to-face [f2f]

  14. T

    A 1 km cropland dataset of South Asia from 640 to 2016

    • data.tpdc.ac.cn
    zip
    Updated Apr 10, 2025
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    Shicheng LI; Xin LIU (2025). A 1 km cropland dataset of South Asia from 640 to 2016 [Dataset]. http://doi.org/10.11888/HumanNat.tpdc.302027
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    zipAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset provided by
    TPDC
    Authors
    Shicheng LI; Xin LIU
    Area covered
    Description

    Based on a large number of historical records and previous studies, we first estimated the historical population of South Asia (including India, Pakistan, Nepal, and Bangladesh) for AD 640-1871, and then calculated the per capita cropland area of South Asia from 640 to 1871 through some reliable historical archives at several time points. Then, by multiplying the historical per capita cropland area by the number of people, the cropland area from 640 to 1871 AD was estimated, and it was connected with the official cropland area statistics from 1900 to 2016 to obtain the cropland area in South Asia from 640 to 2016. Finally, according to the topography, soil and climate characteristics of South Asia, we evaluated the land suitability for cultivation and constructed the spatial reconstruction model of historical cropland in South Asia, and the estimated cropland area was input into the model, and the 1km cropland dataset from 640 to 2016 in South Asia was obtained. Compared with the global historical land use datasets HYDE and KK10, this dataset can more realistically reflect the history of cropland change in South Asia, and can be used to explore the impact of cropland change in South Asia on carbon emissions, climate change, biodiversity and ecosystem services changes in the past millennium.

  15. g

    Population by Nationality

    • gimi9.com
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    Population by Nationality [Dataset]. https://gimi9.com/dataset/uk_population-by-nationality/
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    Description

    🇬🇧 United Kingdom English This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Two files are available to download: Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole. A Tableau visualisation tool is also available. Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

  16. South Africa Population: Mid Year: Indian and Asian: Above 80 Years

    • ceicdata.com
    Updated Jul 23, 2018
    + more versions
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    CEICdata.com (2018). South Africa Population: Mid Year: Indian and Asian: Above 80 Years [Dataset]. https://www.ceicdata.com/en/south-africa/population-mid-year-by-group-age-and-sex/population-mid-year-indian-and-asian-above-80-years
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    Dataset updated
    Jul 23, 2018
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 1, 2006 - Jun 1, 2017
    Area covered
    South Africa
    Variables measured
    Population
    Description

    South Africa Population: Mid Year: Indian and Asian: Above 80 Years data was reported at 19,993.000 Person in 2018. This records an increase from the previous number of 14,829.578 Person for 2017. South Africa Population: Mid Year: Indian and Asian: Above 80 Years data is updated yearly, averaging 10,196.012 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 19,993.000 Person in 2018 and a record low of 6,006.000 Person in 2001. South Africa Population: Mid Year: Indian and Asian: Above 80 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G003: Population: Mid Year: by Group, Age and Sex.

  17. b

    Korea SEA Visitors by Ship – Passenger Volume (Monthly, 2005–2022)

    • aida.informatics.buu.ac.th
    Updated Jul 27, 2024
    + more versions
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    (2024). Korea SEA Visitors by Ship – Passenger Volume (Monthly, 2005–2022) [Dataset]. https://aida.informatics.buu.ac.th/dataset/southeast-asian-visitors-to-korea-by-ship-monthly
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    Dataset updated
    Jul 27, 2024
    Area covered
    South Korea
    Description

    This data is from the Korea Tourism Knowledge Information System and shows the population statistics of Thai, Indonesian, Filipino, and Vietnamese people who entered Korea through the major ports of Incheon and Busan. Source: Tourism Knowledge & Information System (hosted by Ministry of Culture, Sports and Tourism) Countries: Thailand, Indonesia, Philippines, Vietnam Ports of entry: Incheon, Busan Data type: Monthly and weekly (inferred from monthly data) Time period: Up to November 2022

  18. W

    Asian Population Concentration - Southern CA

    • wifire-data.sdsc.edu
    geotiff, wcs, wms
    Updated Mar 25, 2025
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    California Wildfire & Forest Resilience Task Force (2025). Asian Population Concentration - Southern CA [Dataset]. https://wifire-data.sdsc.edu/dataset/clm-asian-population-concentration-southern-ca
    Explore at:
    wcs, geotiff, wmsAvailable download formats
    Dataset updated
    Mar 25, 2025
    Dataset provided by
    California Wildfire & Forest Resilience Task Force
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Southern California, California
    Description

    Relative concentration of the Southern California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

    "Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.

  19. F

    South Asian Multi-Year Facial Image Dataset

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
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    FutureBee AI (2022). South Asian Multi-Year Facial Image Dataset [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-historical-south-asian
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    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    South Asia
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the South Asian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

    Facial Image Data

    This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:

    Historical Images: 22 facial images per participant captured across a span of 10 years
    Enrollment Image: One recent high-resolution facial image for reference or ground truth

    Diversity & Representation

    Geographic Coverage: Participants from India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives, and more and other South Asian regions
    Demographics: Individuals aged 18 to 70 years, with a gender distribution of 60% male and 40% female
    File Formats: All images are available in JPEG and HEIC formats

    Image Quality & Capture Conditions

    To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

    Lighting Conditions: Images captured under various natural and artificial lighting setups
    Backgrounds: A wide range of indoor and outdoor backgrounds
    Device Quality: Captured using modern, high-resolution mobile devices for consistency and clarity

    Metadata

    Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

    Unique participant ID
    File name
    Age at the time of image capture
    Gender
    Country of origin
    Demographic profile
    File format

    Use Cases & Applications

    This dataset is highly valuable for a wide range of AI and computer vision applications:

    Facial Recognition Systems: Train models for high-accuracy face matching across time
    KYC & Identity Verification: Improve time-spanning verification for banks, insurance, and government services
    Biometric Security Solutions: Build reliable identity authentication models
    Age Progression & Estimation Models: Train AI to predict aging patterns or estimate age from facial features
    Generative AI: Support creation and validation of synthetic age progression or longitudinal face generation

    Secure & Ethical Collection

    Platform: All data was securely collected and processed through FutureBeeAI’s proprietary systems
    Ethical Compliance: Full participant consent obtained with transparent communication of use cases
    Privacy-Protected: No personally identifiable information is included; all data is anonymized and handled with care

    Dataset Updates & Customization

    To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

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  20. T

    South Asia - People Practicing Open Defecation (% Of Population)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 27, 2017
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    TRADING ECONOMICS (2017). South Asia - People Practicing Open Defecation (% Of Population) [Dataset]. https://tradingeconomics.com/south-asia/people-practicing-open-defecation-percent-of-population-wb-data.html
    Explore at:
    excel, xml, csv, jsonAvailable download formats
    Dataset updated
    May 27, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Asia, South Asia
    Description

    People practicing open defecation (% of population) in South Asia was reported at 9.3363 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - People practicing open defecation (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

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TRADING ECONOMICS (2017). South Asia - Individuals Using The Internet (% Of Population) [Dataset]. https://tradingeconomics.com/south-asia/individuals-using-the-internet-percent-of-population-wb-data.html

South Asia - Individuals Using The Internet (% Of Population)

Explore at:
csv, excel, xml, jsonAvailable download formats
Dataset updated
Jun 9, 2017
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Jan 1, 1976 - Dec 31, 2025
Area covered
Asia, South Asia
Description

Individuals using the Internet (% of population) in South Asia was reported at 42.85 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Individuals using the Internet (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

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