100+ datasets found
  1. d

    Final Report of the Asian American Quality of Life (AAQoL)

    • catalog.data.gov
    • datahub.austintexas.gov
    • +4more
    Updated Apr 25, 2025
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    data.austintexas.gov (2025). Final Report of the Asian American Quality of Life (AAQoL) [Dataset]. https://catalog.data.gov/dataset/final-report-of-the-asian-american-quality-of-life-aaqol
    Explore at:
    Dataset updated
    Apr 25, 2025
    Dataset provided by
    data.austintexas.gov
    Area covered
    Asia
    Description

    The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

  2. n

    1,995 People Face Images Data (Asian race)

    • m.nexdata.ai
    • nexdata.ai
    Updated Jun 3, 2023
    + more versions
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    Nexdata (2023). 1,995 People Face Images Data (Asian race) [Dataset]. https://m.nexdata.ai/datasets/computervision/142?source=Github
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    Dataset updated
    Jun 3, 2023
    Dataset provided by
    nexdata technology inc
    Nexdata
    Authors
    Nexdata
    Variables measured
    Device, Accuracy, Data size, Data format, Data diversity, Age distribution, Race distribution, Gender distribution, Collecting environment
    Description

    1,995 People Face Images Data (Asian race). For each subject, more than 20 images per person with frontal face were collected. This data can be used for face recognition and other tasks.

  3. Data from: East Asian Social Survey (EASS), Cross-National Survey Data Sets:...

    • icpsr.umich.edu
    ascii, delimited, r +3
    Updated Nov 3, 2022
    + more versions
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    Iwai, Noriko; Kim, Jibum; Fu, Yang-Chih; Li, Lulu (2022). East Asian Social Survey (EASS), Cross-National Survey Data Sets: Culture and Globalization in East Asia, 2018 [Dataset]. http://doi.org/10.3886/ICPSR38489.v1
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    r, sas, ascii, delimited, spss, stataAvailable download formats
    Dataset updated
    Nov 3, 2022
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Iwai, Noriko; Kim, Jibum; Fu, Yang-Chih; Li, Lulu
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/38489/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38489/terms

    Time period covered
    Nov 10, 2017 - Feb 28, 2019
    Area covered
    Japan, East Asia, Asia, South Korea, Taiwan, China (Peoples Republic)
    Description

    The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: the Chinese General Social Survey (CGSS), the Japanese General Social Survey (JGSS), the Korean General Social Survey (KGSS), and the Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Since its 1st module survey in 2006, EASS produces and disseminates its module survey datasets and this is the harmonized data for the 7th module survey, called 'Culture and Globalization in East Asia'. Survey information in this module is the same topic as the second module of the EASS 2008, and it focuses on cultural norms and expectations of respondents. Respondents were asked about their exposure to East Asian cultural activities and rituals as well as opinion on family responsibilities and roles. Other topics include sources of international news and discussion frequency, countries or regions traveled, as well as where acquaintances live. Additionally, respondents were asked how accepting they would be of people from other countries as coworkers, neighbors, and in marriage. Information was collected regarding foreign practices, whether the respondent was working for a foreign capital company, and the economic environment. Respondents were also asked to assess their own proficiency when reading, speaking, and writing in English. Demographic information specific to the respondent and their spouse includes age, sex, marital status, education, employment status and hours worked, occupation, earnings and income, religion, class, size of community, and region.

  4. F

    South Asian Facial Images with Occlusion Dataset

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). South Asian Facial Images with Occlusion Dataset [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-occlusion-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 Face with Occlusion Dataset, meticulously curated to enhance face recognition models and support the development of advanced occlusion detection systems, biometric identification systems, KYC models, and other facial recognition technologies.

    Facial Image Data

    This dataset comprises over 5,000 human facial images, divided into participant-wise sets with each set including:

    Occluded Images: 5 different high-quality facial images per individual occluded through various accessories such as masks, caps, sunglasses, or a combination of these accessories.
    Normal Images: One image without any accessories.

    Diversity and Representation

    The dataset includes contributions from a diverse network of individuals across South Asian countries:

    Geographical Representation: Participants from countries including India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives, and more.
    Demographics: Participants range from 18 to 70 years old, representing both males and females in 60:40 ratio, respectively.
    File Format: The dataset contains images in JPEG and HEIC file format.

    Quality and Conditions

    To ensure high utility and robustness, all images are captured under varying conditions:

    Lighting Conditions: Images are taken in different lighting environments to ensure variability and realism.
    Backgrounds: A variety of backgrounds are available to enhance model generalization.
    Device Quality: Photos are taken using the latest mobile devices to ensure high resolution and clarity.

    Metadata

    Each facial image set is accompanied by detailed metadata for each participant, including:

    Unique Identifier
    File Name
    Age
    Gender
    Country
    Demographic Information
    Occlusion Type
    File Format

    This metadata is essential for training models that can accurately recognize and identify human faces with occlusions across different demographics and conditions.

    Usage and Applications

    This facial image dataset is ideal for various applications in the field of computer vision, including but not limited to:

    Facial Recognition Models: Improving the accuracy and reliability of facial recognition systems.
    KYC Models: Streamlining the identity verification processes for financial and other services.
    Biometric Identity Systems: Developing robust biometric identification solutions.
    Occlusion Identification: Enhancing models to accurately identify faces with occlusions.

    Secure and Ethical Collection

    Data Security: Data was securely stored and processed within our platform, ensuring data security and confidentiality.
    Ethical Guidelines: The biometric data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
    Participant Consent: All participants were informed of the purpose of collection and potential use of the data, as agreed through written consent.

    Updates and Customization

    We understand the evolving nature of

  5. h

    stock-photos-asian-people

    • huggingface.co
    Updated Oct 3, 2012
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    Alan Tseng (2012). stock-photos-asian-people [Dataset]. https://huggingface.co/datasets/agentlans/stock-photos-asian-people
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    Dataset updated
    Oct 3, 2012
    Authors
    Alan Tseng
    License

    https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/

    Description

    Stock Photos (Asian People, Stable Diffusion 1.5)

    Collage of randomly selected images from the dataset

    Collection of synthetic stock photographs created with Stable Diffusion 1.5, emphasizing Asian people (including East Asians, Southeast Asians, South Asians, and Central Asians). Images were generated using a diverse set of prompts and filtered for quality, realism, and safety.

      Supported Tasks
    

    Image generation Image captioning Visual representation learning… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/stock-photos-asian-people.

  6. N

    cities in Pinellas County Ranked by Multi-Racial Asian Population // 2025...

    • neilsberg.com
    csv, json
    Updated Feb 11, 2025
    + more versions
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    Neilsberg Research (2025). cities in Pinellas County Ranked by Multi-Racial Asian Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/cities-in-pinellas-county-fl-by-multi-racial-asian-population/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 11, 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
    Florida, Pinellas County
    Variables measured
    Multi-Racial Asian Population, Multi-Racial Asian Population as Percent of Total Population of cities in Pinellas County, FL, Multi-Racial Asian Population as Percent of Total Multi-Racial Asian Population of Pinellas County, FL
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.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

    This list ranks the 24 cities in the Pinellas County, FL by Multi-Racial Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

    Content

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

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Multi-Racial Asian Population: This column displays the rank of cities in the Pinellas County, FL by their Multi-Racial Asian population, using the most recent ACS data available.
    • cities: The cities for which the rank is shown in the previous column.
    • Multi-Racial Asian Population: The Multi-Racial Asian population of the cities is shown in this column.
    • % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total Pinellas County Multi-Racial Asian Population: This tells us how much of the entire Pinellas County, FL Multi-Racial Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    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/.

  7. N

    Dublin, CA Population Breakdown By Race (Excluding Ethnicity) Dataset:...

    • neilsberg.com
    csv, json
    Updated Jul 7, 2024
    + more versions
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    Neilsberg Research (2024). Dublin, CA Population Breakdown By Race (Excluding Ethnicity) Dataset: Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/2dd9631f-230c-11ef-bd92-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jul 7, 2024
    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
    California, Dublin
    Variables measured
    Asian Population, Black Population, White Population, Some other race Population, Two or more races Population, American Indian and Alaska Native Population, Asian Population as Percent of Total Population, Black Population as Percent of Total Population, White Population as Percent of Total Population, Native Hawaiian and Other Pacific Islander 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) 2018-2022 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total 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 do not rely on any ethnicity 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 population of Dublin by race. It includes the population of Dublin across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Dublin across relevant racial categories.

    Key observations

    The percent distribution of Dublin population by race (across all racial categories recognized by the U.S. Census Bureau): 29.65% are white, 4.27% are Black or African American, 0.66% are American Indian and Alaska Native, 53.47% are Asian, 0.36% are Native Hawaiian and other Pacific Islander, 2.38% are some other race and 9.20% are multiracial.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 (excluding ethnicity) for the Dublin
    • Population: The population of the racial category (excluding ethnicity) in the Dublin is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each race as a proportion of Dublin total 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 Dublin Population by Race & Ethnicity. You can refer the same here

  8. w

    Dataset of books series that contain Asian people who changed history

    • workwithdata.com
    Updated Nov 25, 2024
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    Work With Data (2024). Dataset of books series that contain Asian people who changed history [Dataset]. https://www.workwithdata.com/datasets/book-series?f=1&fcol0=j0-book&fop0=%3D&fval0=Asian+people+who+changed+history&j=1&j0=books
    Explore at:
    Dataset updated
    Nov 25, 2024
    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

    Description

    This dataset is about book series. It has 1 row and is filtered where the books is Asian people who changed history. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

  9. N

    London, OH Population Breakdown By Race (Excluding Ethnicity) Dataset:...

    • neilsberg.com
    csv, json
    Updated Jul 7, 2024
    + more versions
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    Neilsberg Research (2024). London, OH Population Breakdown By Race (Excluding Ethnicity) Dataset: Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/2e1e8b32-230c-11ef-bd92-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jul 7, 2024
    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
    London
    Variables measured
    Asian Population, Black Population, White Population, Some other race Population, Two or more races Population, American Indian and Alaska Native Population, Asian Population as Percent of Total Population, Black Population as Percent of Total Population, White Population as Percent of Total Population, Native Hawaiian and Other Pacific Islander 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) 2018-2022 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total 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 do not rely on any ethnicity 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 population of London by race. It includes the population of London across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of London across relevant racial categories.

    Key observations

    The percent distribution of London population by race (across all racial categories recognized by the U.S. Census Bureau): 90.46% are white, 2.37% are Black or African American, 0.32% are American Indian and Alaska Native, 2.31% are Asian, 0.02% are Native Hawaiian and other Pacific Islander, 0.16% are some other race and 4.36% are multiracial.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 (excluding ethnicity) for the London
    • Population: The population of the racial category (excluding ethnicity) in the London is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each race as a proportion of London total 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 London Population by Race & Ethnicity. You can refer the same here

  10. P

    FairFace Dataset

    • paperswithcode.com
    • library.toponeai.link
    • +2more
    Updated Apr 18, 2024
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    Kimmo Kärkkäinen; Jungseock Joo (2024). FairFace Dataset [Dataset]. https://paperswithcode.com/dataset/fairface
    Explore at:
    Dataset updated
    Apr 18, 2024
    Authors
    Kimmo Kärkkäinen; Jungseock Joo
    Description

    FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.

  11. d

    Map Data | Asia & MENA | Premium Demographics & Point-of-Interest Data To...

    • datarade.ai
    .json, .csv
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    GapMaps, Map Data | Asia & MENA | Premium Demographics & Point-of-Interest Data To Optimise Business Decisions | GIS Data | Demographic Data [Dataset]. https://datarade.ai/data-products/gapmaps-global-map-data-asia-mena-150m-x-150m-grids-cu-gapmaps
    Explore at:
    .json, .csvAvailable download formats
    Dataset authored and provided by
    GapMaps
    Area covered
    Indonesia, India, Saudi Arabia, Philippines, Malaysia, Singapore, Asia
    Description

    Sourcing accurate and up-to-date map data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

    GapMaps Map Data uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographics data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

    GapMaps Map Data also includes the latest Point-of-Interest (POI) Data for leading retail brands across a range of categories including Fast Food/ QSR, Health & Fitness, Supermarket/Grocery and Cafe sectors which is updated monthly.

    With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

    • Better understand your customers
    • Identify optimal locations to expand your retail footprint
    • Define sales territories for franchisees
    • Run targeted marketing campaigns.

    GapMaps Map Data for Asia and MENA can be utilized in any GIS platform and includes the latest estimates (updated annually) on:

    1. Population (how many people live in your local catchment)
    2. Demographics (who lives within your local catchment)
    3. Worker population (how many people work within your local catchment)
    4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
    5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

    Primary Use Cases for GapMaps Map Data:

    1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
    2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
    3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
    4. Target Marketing: Develop effective marketing strategies to acquire more customers.
    5. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.
    6. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
    7. Customer Profiling
    8. Target Marketing
    9. Market Share Analysis
  12. 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
    South Asia, 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 July of 2025.

  13. d

    Premium GIS Data | Asia/ MENA | Latest Estimates on Population, Consuming...

    • datarade.ai
    .json, .csv
    Updated Nov 23, 2024
    + more versions
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    GapMaps (2024). Premium GIS Data | Asia/ MENA | Latest Estimates on Population, Consuming Class, Retail Spend, Demographics | Map Data | Demographic Data [Dataset]. https://datarade.ai/data-products/gapmaps-premium-demographics-gis-data-asia-mena-150m-x-1-gapmaps
    Explore at:
    .json, .csvAvailable download formats
    Dataset updated
    Nov 23, 2024
    Dataset authored and provided by
    GapMaps
    Area covered
    India, Indonesia, Singapore, Philippines, Malaysia, Saudi Arabia, Asia
    Description

    Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

    GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

    With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

    • Better understand your customers
    • Identify optimal locations to expand your retail footprint
    • Define sales territories for franchisees
    • Run targeted marketing campaigns.

    Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

    1. Population (how many people live in your local catchment)
    2. Demographics (who lives within your local catchment)
    3. Worker population (how many people work within your local catchment)
    4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
    5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

    Primary Use Cases for GapMaps Demographics GIS Data:

    1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
    2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
    3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
    4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
    5. Target Marketing: Develop effective marketing strategies to acquire more customers.
    6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

    7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

    8. Tenant Recruitment

    9. Target Marketing

    10. Market Potential / Gap Analysis

    11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

    12. Customer Profiling

    13. Target Marketing

    14. Market Share Analysis

  14. d

    Demographics

    • catalog.data.gov
    • datasets.ai
    • +4more
    Updated Nov 22, 2024
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    Lake County Illinois GIS (2024). Demographics [Dataset]. https://catalog.data.gov/dataset/demographics-0be32
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    Dataset updated
    Nov 22, 2024
    Dataset provided by
    Lake County Illinois GIS
    Description

    Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

  15. i

    Asian Barometer Survey 2010-2011, Wave 3 - China, Hong Kong SAR, China,...

    • catalog.ihsn.org
    • dev.ihsn.org
    Updated Aug 26, 2021
    + more versions
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    Institute of Political Science (2021). Asian Barometer Survey 2010-2011, Wave 3 - China, Hong Kong SAR, China, Indonesia, India, Japan, Cambodia, Korea, Rep., Sri Lanka, Mongolia, Ma [Dataset]. https://catalog.ihsn.org/catalog/3001
    Explore at:
    Dataset updated
    Aug 26, 2021
    Dataset provided by
    East Asia Democratic Studies
    Institute of Political Science
    Time period covered
    2010 - 2011
    Area covered
    Sri Lanka, China, South Korea, Indonesia, Cambodia, Japan, Hong Kong, India, Mongolia
    Description

    Abstract

    The third wave of the Asian Barometer survey (ABS) conducted in 2010 and the database contains nine countries and regions in East Asia - the Philippines, Taiwan, Thailand, Mongolia, Singapore, Vietnam, Indonesia, Malaysia and South Korea. The ABS is an applied research program on public opinion on political values, democracy, and governance around the region. The regional network encompasses research teams from 13 East Asian political systems and 5 South Asian countries. Together, this regional survey network covers virtually all major political systems in the region, systems that have experienced different trajectories of regime evolution and are currently at different stages of political transition.

    The mission and task of each national research team are to administer survey instruments to compile the required micro-level data under a common research framework and research methodology to ensure that the data is reliable and comparable on the issues of citizens' attitudes and values toward politics, power, reform, and democracy in Asia.

    The Asian Barometer Survey is headquartered in Taipei and co-hosted by the Institute of Political Science, Academia Sinica and The Institute for the Advanced Studies of Humanities and Social Sciences, National Taiwan University.

    Geographic coverage

    13 East Asian political systems: Japan, Mongolia, South Koreas, Taiwan, Hong Kong, China, the Philippines, Thailand, Vietnam, Cambodia, Singapore, Indonesia, and Malaysia; 5 South Asian countries: India, Pakistan, Bangladesh, Sri Lanka, and Nepal

    Analysis unit

    -Individuals

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Compared with surveys carried out within a single nation, cross-nation survey involves an extra layer of difficulty and complexity in terms of survey management, research design, and database modeling for the purpose of data preservation and easy analysis. To facilitate the progress of the Asian Barometer Surveys, the survey methodology and database subproject is formed as an important protocol specifically aiming at overseeing and coordinating survey research designs, database modeling, and data release.

    As a network of Global Barometer Surveys, Asian Barometer Survey requires all country teams to comply with the research protocols which Global Barometer network has developed, tested, and proved practical methods for conducting comparative survey research on public attitudes.

    Research Protocols:

    • National probability samples that give every citizen in each country an equal chance of being selected for interview. Whether using census household lists or a multistage area approach, the method for selecting sampling units is always randomized. The samples may be stratified, or weights applied, to ensure coverage of rural areas and minority populations in their correct proportions. As such, Asian Barometer samples represent the adult, voting-age population in each country surveyed.

    A model Asian Barometer Survey has a sample size of 1,200 respondents, which allows a minimum confidence interval of plus or minus 3 percent at 95 percent probability.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    A standard questionnaire instrument containing a core module of identical or functionally equivalent questions. Wherever possible, theoretical concepts are measured with multiple items in order to enable testing for construct validity. The wording of items is determined by balancing various criteria, including: the research themes emphasized in the survey, the comprehensibility of the item to lay respondents, and the proven effectiveness of the item when tested in previous surveys.

    Survey Topics: 1.Economic Evaluations: What is the economic condition of the nation and your family: now, over the last five years, and in the next five years? 2.Trust in institutions: How trustworthy are public institutions, including government branches, the media, the military, and NGOs. 3.Social Capital: Membership in private and public groups, the frequency and degree of group participation, trust in others, and influence of guanxi. 4.Political Participatio: Voting in elections, national and local, country-specific voting patterns, and active participation in the political process as well as demonstrations and strikes. Contact with government and elected officials, political organizations, NGOs and media. 5.Electoral Mobilization: Personal connections with officials, candidates, and political parties; influence on voter choice. 6.Psychological Involvement and Partisanship: Interest in political news coverage, impact of government policies on daily life, and party allegiance. 7.Traditionalism: Importance of consensus and family, role of the elderly, face, and woman in theworkplace. 8.Democratic Legitimacy and Preference for Democracy: Democratic ranking of present and previous regime, and expected ranking in the next five years; satisfaction with how democracy works, suitability of democracy; comparisons between current and previous regimes, especially corruption; democracy and economic development, political competition, national unity, social problems, military government, and technocracy. 9.Efficacy, Citizen Empowerment, System Responsiveness: Accessibility of political system: does a political elite prevent access and reduce the ability of people to influence the government. 10.Democratic vs. Authoritarian Values: Level of education and political equality, government leadership and superiority, separation of executive and judiciary. 11.Cleavage: Ownership of state-owned enterprises, national authority over local decisions, cultural insulation, community and the individual. 12.Belief in Procedural Norms of Democracy: Respect of procedures by political leaders: compromise, tolerance of opposing and minority views. 13.Social-Economic Background Variables: Gender, age, marital status, education level, years of formal education, religion and religiosity, household, income, language and ethnicity. 14.Interview Record: Gender, age, class, and language of the interviewer, people present at the interview; did the respondent: refuse, display impatience, and cooperate; the language or dialect spoken in interview, and was an interpreter present.

    Cleaning operations

    Quality checks are enforced at every stage of data conversion to ensure that information from paper returns is edited, coded, and entered correctly for purposes of computer analysis. Machine readable data are generated by trained data entry operators and a minimum of 20 percent of the data is entered twice by independent teams for purposes of cross-checking. Data cleaning involves checks for illegal and logically inconsistent values.

  16. d

    AmeriCorps Members Demographic

    • catalog.data.gov
    • data.americorps.gov
    • +1more
    Updated Nov 29, 2023
    + more versions
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    CNCS (2023). AmeriCorps Members Demographic [Dataset]. https://catalog.data.gov/dataset/americorps-members-demographic
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    Dataset updated
    Nov 29, 2023
    Dataset provided by
    CNCS
    Description

    The data is prepared using AmeriCorps members who began service on any day in fiscal year (FY) 2017. The members may have served 1 to 365 days during their term. Members who are in never served, disqualified, pre-service, or deferred statuses were excluded from this analysis. AmeriCorps VISTA and AmeriCorps NCCC race and ethnicity data come from the member application to serve. The code to extract the data between the two programs is the same. The ASN race and ethnicity data comes from the enrollment form. The enrollment form may exist multiple times if the member enrolled in more than one term. It is not uncommon for each enrollment form to have conflicting information about the member’s race and ethnicity. The member may have enrollment form data for terms served outside of the timeframe of the dataset. For example, if we are reporting on members who began service in FY17, then a member who also served in FY16 may have race and ethnicity information in the FY16 enrollment form and no race or ethnicity information or conflicting information in the FY17 enrollment form. In the case of conflicting information, this analysis assumes each instance of race designation is correct. If a member reports themselves as “Asian or Asian American” in one enrollment form and “White” in another enrollment form, then the analysis categorizes this person as someone who identifies with multiple race selections vs. one or the other. In the case of ethnicity, if a member indicates that they are not Hispanic or Latino/a in one form, but that they are in another, this analysis assumes the affirmative—and they will be categorized as Hispanic or Latino/a. Lastly, the totals include the total results from the query plus the difference between the query and the raw count of members who started service in that fiscal year. The members who did not have a record in the invite table and enrollment table were added to the non-response category. Senior Corps Figures come from the Annual Progress Report Supplement as of April 11, 2018. Percentages are calculated from totals of the subcategories, excluding the non-response categories.

  17. N

    Greenland, AR Population Breakdown By Race (Excluding Ethnicity) Dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 21, 2025
    + more versions
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    Neilsberg Research (2025). Greenland, AR Population Breakdown By Race (Excluding Ethnicity) Dataset: Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/757627cb-ef82-11ef-9e71-3860777c1fe6/
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    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
    Greenland
    Variables measured
    Asian Population, Black Population, White Population, Some other race Population, Two or more races Population, American Indian and Alaska Native Population, Asian Population as Percent of Total Population, Black Population as Percent of Total Population, White Population as Percent of Total Population, Native Hawaiian and Other Pacific Islander 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) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total 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 do not rely on any ethnicity 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 population of Greenland by race. It includes the population of Greenland across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Greenland across relevant racial categories.

    Key observations

    The percent distribution of Greenland population by race (across all racial categories recognized by the U.S. Census Bureau): 79.14% are white, 7.06% are Black or African American, 0.99% are American Indian and Alaska Native, 0.15% are Asian, 0.83% are some other race and 11.84% are multiracial.

    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 (excluding ethnicity) for the Greenland
    • Population: The population of the racial category (excluding ethnicity) in the Greenland is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each race as a proportion of Greenland total 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 Greenland Population by Race & Ethnicity. You can refer the same here

  18. Asia Covid 19 Cases

    • kaggle.com
    zip
    Updated Oct 11, 2021
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    Vivek Chowdhury (2021). Asia Covid 19 Cases [Dataset]. https://www.kaggle.com/vivek468/asia-covid-19-cases-updated-10-oct-21
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    zip(2174 bytes)Available download formats
    Dataset updated
    Oct 11, 2021
    Authors
    Vivek Chowdhury
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    Asia
    Description

    About the Data:

    A year ago, when WHO declared COVID-19 outbreak a pandemic, countries in WHO South-East Asia Region were either responding to their first cases of importation or cluster of cases or keeping a strict vigil against importation of the new coronavirus.

    The following months were unprecedented, and for many reasons. Scientists, experts, governments, societies, communities and even individuals responded to the new virus with urgency and measures never witnessed before.

    Metadata:

    ID: Unique Identifier Country: Name of Country TotalCases: Total Number of cases recorded so far TotalDeaths: Total Deaths recorded so far TotalRecovered: How many people survived ActiveCases: Number of people who currently has the virus TotalCasesPerMillion: How many cases are recorded per million individual TotalDeathsPerMillion: How many deaths recorded per million individual TotalTests: Total number of COVID19 tests conducted RTPCR + RAT + any other tests TotalTestsPerMillion: How many tests were conducted per million individual TotalPopulation: Population of the country

    Acknowledgements:

    This dataset was collected from: https://www.worldometers.info/coronavirus/#countries

    Call For Code:

    Fellow Data Scientist and ML engineers, can you identify which countries are doing relatively well and which ones need immediate attention? Your insights can save millions of lives in Asia!

  19. Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A...

    • zenodo.org
    • data.niaid.nih.gov
    csv
    Updated Oct 26, 2023
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    Gunther Jikeli; Gunther Jikeli; Sameer Karali; Sameer Karali; Katharina Soemer; Katharina Soemer (2023). Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A Dataset for Machine Learning and Text Analytics [Dataset]. http://doi.org/10.5281/zenodo.8147308
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    csvAvailable download formats
    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Gunther Jikeli; Gunther Jikeli; Sameer Karali; Sameer Karali; Katharina Soemer; Katharina Soemer
    License

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

    Description

    ### Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims

    The ISCA project compiled this dataset using an annotation portal, which was used to label tweets as either biased or non-biased, among other labels. Note that the annotation was done on live data, including images and context, such as threads. The original data comes from annotationportal.com. They include representative samples of live tweets from the years 2020 and 2021 with the keywords "Asians, Blacks, Jews, Latinos, and Muslims".

    A random sample of 600 tweets per year was drawn for each of the keywords. This includes retweets. Due to a sampling error, the sample for the year 2021 for the keyword "Jews" has only 453 tweets from 2021 and 147 from the first eight months of 2022 and it includes some tweets from the query with the keyword "Israel." The tweets were divided into six samples of 100 tweets, which were then annotated by three to seven students in the class "Researching White Supremacism and Antisemitism on Social Media" taught by Gunther Jikeli, Elisha S. Breton, and Seth Moller at Indiana University in the fall of 2022, see this report. Annotators used a scale from 1 to 5 (confident not biased, probably not biased, don't know, probably biased, confident biased). The definitions of bias against each minority group used for annotation are also included in the report.

    If a tweet called out or denounced bias against the minority in question, it was labeled as "calling out bias."

    The labels of whether a tweet is biased or calls out bias are based on a 75% majority vote. We considered "probably biased" and "confident biased" as biased and "confident not biased," "probably not biased," and "don't know" as not biased.

    The types of stereotypes vary widely across the different categories of prejudice. While about a third of all biased tweets were classified as "hate" against the minority, the stereotypes in the tweets often matched common stereotypes about the minority. Asians were blamed for the Covid pandemic. Blacks were seen as inferior and associated with crime. Jews were seen as powerful and held collectively responsible for the actions of the State of Israel. Some tweets denied the Holocaust. Hispanics/Latines were portrayed as being in the country illegally and as "invaders," in addition to stereotypical accusations of being lazy, stupid, or having too many children. Muslims, on the other hand, were often collectively blamed for terrorism and violence, though often in conversations about Muslims in India.

    # Content:

    This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias.

    1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians.

    1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks.

    1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews.

    1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines.

    1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.

    # File Description:

    The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:


    'TweetID': Represents the tweet ID.

    'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet.

    'Text': Represents the full text of the tweet (not pre-processed).

    'CreateDate': Represents the date the tweet was created.

    'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0).

    'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0).

    'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.

    # Licences

    Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)

    # Acknowledgements

    We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin Ray, Kaleb Samuels, Chloe Sherman, Rachel Weber, Molly Winkeljohn, Ally Wolfgang, Rowan Wolke, Michael Wong, Jane Woods, Kaleb Woodworth, and Aurora Young.

    This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

  20. 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.

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data.austintexas.gov (2025). Final Report of the Asian American Quality of Life (AAQoL) [Dataset]. https://catalog.data.gov/dataset/final-report-of-the-asian-american-quality-of-life-aaqol

Final Report of the Asian American Quality of Life (AAQoL)

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8 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 25, 2025
Dataset provided by
data.austintexas.gov
Area covered
Asia
Description

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

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