In India, the share of the population that earned at least the equivalent of the highest 40 percent of global income earners as of 2022 in purchasing power parity (PPP) terms was 41 percent. Hyderabad topped the list with the highest share of middle-class and above category of consumers. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Centre Island. The dataset can be utilized to gain insights into gender-based income distribution within the Centre Island population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Centre Island, among individuals aged 15 years and older with income, there were 111 men and 99 women in the workforce. Among them, 64 men were engaged in full-time, year-round employment, while 33 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while none of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 82.81% of men in full-time roles earned incomes exceeding $100,000, while 93.94% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Centre Island median household income by race. You can refer the same here

At the lower-middle-income level, the poverty rate in Ghana was forecast at 26.8 percent in 2021, meaning this share of the population lived up on 3.20 U.S. dollars per day. Considering the upper-middle-income level, at 5.50 U.S. dollars per day, the poverty rate was forecast at 51.7 percent. The values changed slightly compared to the previous years in analysis. This means that the rate of poverty in Ghana was not expected to experience drastic changes in the years following 2019. The coronavirus (COVID-19) pandemic and its impact on economic activities could be attributed to the unimproved poverty levels registered in the country.

Income statistics by economic family type and income source, annual.

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

This data collection consists of 18 interview transcripts meant to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. The life and impact of the residential choices of the 'super rich' has been a major strand in research by the research team. This work advanced the proposition that the upper-tier of income groups living in cities tend to exploit particular forms of service provision (such as education, cultural life and personal services), are largely distanced from the mundane flow of social life in urban areas and tend to be withdrawn from the civic life of cities more generally. Some of this work is underpinned by the literature on, for example, gated communities, but it has surprisingly been under-used as the guiding framework for close empirical work in affluent neighbourhoods, perhaps largely as a result of the perceived difficulty of working with such individuals. This project will allow us to generate insights into how super-rich neighbourhoods operate, how people come to live there and the social and economic tensions and trade-offs that exist as such processes are allowed to run. As many people question the role and value of wealth and identify inequality as a growing social problem this research will feed into public conversations and policymaker concerns about how socially vital cities can be maintained when capital investment may undermine such objectives on one level (the creation of neighbourhoods that are both exclusive and often 'abandoned' for large parts of the year), while potentially fulfilling broader ambitions at others (over tax receipts for example).Social research has tended not to focus on the super-rich, largely because they are hard to locate, and even harder to collaborate with in research. In this project we seek to address these concerns by focusing extensive research effort on the question of where and how the super-rich live and invest in the property markets of the cities of Hong Kong and London. We see these cities as exemplary in assisting in the construction of further insights and knowledge in how the super-rich seek residential investment opportunities, how they live there when they are 'at home' in such residences and how these patterns of investment shape the social, political and economic life of these cities more broadly. Given that the super-rich make such decisions on the basis of tax incentives and the attraction of major cultural infrastructure (such as galleries and theatre) we have proposed a program of research capable of offering an inside account of the practices that go to make-up these investment patterns including processes of searching for suitable property, its financing, the kinds of property deemed to be suitable and an analysis of how estate agents and city authorities seek to capitalise and retain the potentially highly mobile investment by the super-rich. In economic terms the life and functioning of rich neighbourhood spaces appears intuitively important. For example, attractive and safe spaces for captains of industry, senior figures in political and non-government organizations are often regarded as major markers of urban vitality and the foundation of social networks that may make-up the broader glue of civic and political society. Yet we know very little about how such neighbourhoods operate, who they attract and how they are linked to other cities and their neighbourhoods globally. Our aim in this research is to grapple with what might be described as the 'problem' of these super-rich neighbourhoods - sometime called the 'alpha territory' - and undertake research that will help us to understand more about the advantages and disadvantages of these kinds of property investment. The research was carried out using semi-structured interviews and participant observation at property fairs and development sites in Hong Kong and different cities in the UK. Moreover, semi-structured interviews were conducted to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. Participants were recruited using searches for relevant key actors as well as accessing personal and professional networks that enabled snowballing techniques to elicit further contacts. Interviews were conducted with individual investors, local government officials, planning officers, inward investment agencies, city government officials and estate agents. Interviews were conducted in both English and Cantonese.

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

India data available from WorldPop here.

Analysis of ‘ Decomposing World Income Distribution Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/dataset/wps2562-decomposing-world-income-distribution-database on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Using national income and expenditure distribution data from 119 countries, the authors decompose total income inequality between the individuals in the world, by continent and by "region" (countries grouped by income level). They use a Gini decomposition that allows for an exact breakdown (without a residual term) of the overall Gini by recipients. Looking first at income inequality in income between countries is more important than inequality within countries. Africa, Latin America, and Western Europe and North America are quite homogeneous continent, with small differences between countries (so that most of the inequality on these continents is explained by inequality within countries). Next the authors divide the world into three groups: the rich G7 countries (and those with similar income levels), the less developed countries (those with per capita income less than or equal to Brazil's), and the middle-income countries (those with per capita income between Brazil's and Italy's). They find little overlap between such groups very few people in developing countries have incomes in the range of those in the rich countries.

--- Original source retains full ownership of the source dataset ---

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Ghana data available from WorldPop here.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Middle Point. The dataset can be utilized to gain insights into gender-based income distribution within the Middle Point population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Middle Point, among individuals aged 15 years and older with income, there were 260 men and 233 women in the workforce. Among them, 139 men were engaged in full-time, year-round employment, while 126 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.35% fell within the income range of under $24,999, while 19.05% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 17.99% of men in full-time roles earned incomes exceeding $100,000, while 2.38% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Middle Point median household income by race. You can refer the same here

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

Introduction

Welcome to the Middle Eastern Facial Images from Past Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

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

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across Middle Eastern countries:

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify Middle Eastern faces 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:

Secure and Ethical Collection

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning technology is one of the most promising approaches to improving oral healthcare services and reducing patient costs. Openly accessible datasets play a crucial role in facilitating the development of machine learning techniques. However, existing dental datasets have limitations such as a scarcity of Cone Beam Computed Tomography (CBCT) data, lack of matched multi-modal data, and insufficient complexity and diversity of the data. This project addresses these challenges by providing a dataset that includes 329 CBCT images from 169 patients, multi-modal data with matching modalities, and images representing various oral health conditions.

About this dataset

Context

The dataset tabulates the South Centre township household income by gender. The dataset can be utilized to understand the gender-based income distribution of South Centre township income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• South Centre Township, Pennsylvania annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• South Centre Township, Pennsylvania annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of South Centre township income distribution by gender. You can refer the same here

Transcripts of in-depth interviews and group discussions with managers, researchers, ethics committee members, field data collectors and community members on the issues around ethical data sharing in the context of research involving women and children in urban India. We interviewed researchers, managers, and research participants associated with a Mumbai non-governmental organization, as well as researchers from other organizations and members of ethics committees. We conducted 22 individual semi-structured interviews and involved 44 research participants in focus group discussions. We used framework analysis to examine ideas about data and data sharing in general; its potential benefits or harms, barriers, obligations, and governance; and the requirements for consent. Both researchers and participants were generally in favor of data sharing, although limited experience amplified their reservations.

It is increasingly recognized that effective and appropriate data sharing requires the development of models of good data sharing practice capable of taking seriously both the potential benefits to be gained and the importance of ensuring that the rights and interests of participants are respected and that risk of harms is minimized. Calls for the greater sharing of individual level data from biomedical and public health research are receiving support among researchers and research funders. Despite its potential importance, data sharing presents important ethical, social, and institutional challenges in low income settings. This dataset comprises qualitative research conducted in India, exploring the experiences of key research stakeholders and their views about what constitutes good data sharing practice.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Mozambique data available from WorldPop here.