WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.


Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):

- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019).

Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This layer shows Hispanic or Latino origin by specific origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population with Hispanic or Latino origins. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B03001 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

At mid-year 2020, Venezuela's total international migrant stock amounted to more than five million people. Neighboring Colombia was the main country of destination of Venezuelan emigrants, with over 1.7 million. Peru came in second, as almost 950,000 Venezuelans had emigrated there, followed by Chile, where over 500,000 Venezuelans resided after leaving their home country.

Modifiers of Mendelian disorders can provide insights into disease mechanisms and guide therapeutic strategies. A recent genome-wide association (GWA) study discovered genetic modifiers of Huntington's disease (HD) onset in Europeans. Here, we performed whole genome sequencing and GWA analysis of a Venezuelan HD cluster whose families were crucial for the original mapping of the HD gene defect. The Venezuelan HD subjects develop motor symptoms earlier than their European counterparts, implying the potential for population-specific modifiers. The main Venezuelan HD family inherits HTT haplotype hap.03, which differs subtly at the sequence level from European HD hap.03, suggesting a different ancestral origin but not explaining the earlier age at onset in these Venezuelans. GWA analysis of the Venezuelan HD cluster suggests both population-specific and population-shared genetic modifiers. Genome-wide significant signals at 7p21.2–21.1 and suggestive association signals at 4p14 and 17q21.2 are evident only in Venezuelan HD, but genome-wide significant association signals at the established European chromosome 15 modifier locus are improved when Venezuelan HD data are included in the meta-analysis. Venezuelan-specific association signals on chromosome 7 center on SOSTDC1, which encodes a bone morphogenetic protein antagonist. The corresponding SNPs are associated with reduced expression of SOSTDC1 in non-Venezuelan tissue samples, suggesting that interaction of reduced SOSTDC1 expression with a population-specific genetic or environmental factor may be responsible for modification of HD onset in Venezuela. Detection of population-specific modification in Venezuelan HD supports the value of distinct disease populations in revealing novel aspects of a disease and population-relevant therapeutic strategies.

Due to the recent hyperinflation crisis in Venezuela, the average inflation rate in Venezuela is estimated to be around 225 percent in 2026. However, this is well below the peak of 63,000 percent observed in 2018.What is hyperinflation?In short, hyperinflation is a very high inflation rate that accelerates quickly. It can be caused by a government printing huge amounts of new money to pay for its expenses. The subsequent rapid increase of prices causes the country’s currency to lose value and shortages in goods to occur. People then typically start hoarding goods, which become even more scarce and expensive, money becomes worthless, financial institutions go bankrupt, and eventually, the country’s economy collapses. The Venezuelan descent into hyperinflationIn Venezuela, the economic catastrophe began with government price controls and plummeting oil prices, which caused state-run oil companies to go bankrupt. The government then starting printing new money to cope, thus prices rose rapidly, unemployment increased, and GDP collapsed, all of which was exacerbated by international sanctions. Today, many Venezuelans are emigrating to find work and supplies elsewhere, and population growth is at a decade-low. Current president Nicolás Maduro does not seem inclined to steer away from his course of price controls and economic mismanagement, so the standard of living in the country is not expected to improve significantly anytime soon.

If you were to leave your home country, how far would you go, and for what reason? Just over the nearest border? Across an ocean? Or to the other side of the world?

People often equate international migration with long journeys. But most migrants actually travel shorter distances, as you might expect if you put yourself into their situation.

Understanding migration patterns helps governments around the world plan for population and economic changes.

This article addresses a simple but important question: how far do international migrants usually move from their home countries?

But before we look at how far migrants travel, it’s useful to keep in mind that most people don’t move to a different country. 96% of the world’s population lives in the country where they were born. That means the people we’ll focus on here are a small fraction of the global population.

Two examples: Syria and Venezuela Syria and Venezuela are two recent examples of countries with large-scale emigration, but for very different reasons — one caused by war, the other by economic collapse and political instability.

Since the start of its civil war in 2011, Syria has become a well-known case of large-scale emigration. By 2020, nearly half (48%) of all Syrian-born people — about 8.5 million — had left the country.

While we don’t have precise data on how far each migrant traveled, we do have reliable estimates of the countries they moved to. This data is published by the United Nations Department of Economic and Social Affairs.

As you can see on the chart, most Syrian emigrants have stayed close to home. The chart below shows Turkey, Lebanon, and Saudi Arabia as the top destinations, with Turkey alone hosting nearly 40% of them. Overall, a large majority of Syrian emigrants — 80% — have remained within Asia.

Data quality: Hamilton, City (C) Total non-response (TNR) rate, short-form census questionnaire: 2.5% Total non-response (TNR) rate, long-form census questionnaire: 3.5% Notes: 86: Serbia excludes Kosovo.87: The official name of United Kingdom is United Kingdom of Great Britain and Northern Ireland. United Kingdom includes Scotland Wales England and Northern Ireland (excludes Isle of Man the Channel Islands and British Overseas Territories).88: The official name of Iran is Islamic Republic of Iran.89: The official name of Syria is Syrian Arab Republic.90: China excludes Hong Kong and Macao.91: The full name of Hong Kong is the Hong Kong Special Administrative Region of China.92: The official name of South Korea is Republic of Korea.95: The official name of Venezuela is Bolivarian Republic of Venezuela.96: Ireland is also referred to as Republic of Ireland.165: For information on data quality for this variable refer to the Education Reference Guide Census of Population 2021 Catalogue no. 98-500-X2021013.166: This includes all persons with a high school diploma or equivalency certificate regardless of whether they also completed a postsecondary certificate diploma or degree.167: 'High (secondary) school diploma or equivalency certificate' includes only people who have this as their highest educational credential. It excludes persons with a postsecondary certificate diploma or degree.168: 'Non-apprenticeship trades certificate or diploma' includes trades certificates or diplomas such as pre-employment or vocational certificates and diplomas from brief trade programs completed at colleges institutes of technology vocational centres and similar institutions. It also includes qualifications from vocational training programs in the province of Quebec such as the Diplôme d'études professionnelles (DEP)/Diploma of Vocational Studies (DVS).169: 'Apprenticeship certificate' includes Certificates of Apprenticeship Certificates of Qualification and Journeyperson's designations.170: College CEGEP and other non-university certificates and diplomas obtained from programs that are typically completed in less than three months are not included in this category.171: 'Earned doctorate' does not include honorary doctorates.172: This variable shows the 'Variant of CIP 2021 - Alternative primary groupings ' with the hierarchy of the primary groupings and two-digit series. When a primary grouping contains more than one subseries from series '30. Multidisciplinary/interdisciplinary studies ' these subseries are grouped together. An exception is made for '30.01 Biological and physical sciences' due to its large size. For more information on the CIP classification see the Classification of Instructional Programs Canada 2021. For information on classification and data quality for this variable refer to the Education Reference Guide Census of Population 2021 Catalogue no. 98-500-X2021013.173: 'No postsecondary certificate diploma or degree' is made up of persons who have not completed any credentials above a high school diploma.174: Includes '30.13 Medieval and renaissance studies ' '30.21 Holocaust and related studies ' '30.22 Classical and ancient studies ' '30.29 Maritime studies ' '30.45 History and language/literature ' '30.47 Linguistics and anthropology ' '30.51 Integrated philosophy politics and economics ' '30.52 Digital humanities and textual studies ' and '30.53 Thanatology.'175: Includes '30.05 Peace studies and conflict resolution ' '30.11 Gerontology ' '30.14 Museology/museum studies ' '30.15 Science technology and society ' '30.17 Behavioural sciences ' '30.20 International/globalization studies ' '30.23 Intercultural/multicultural and diversity studies ' '30.25 Cognitive science ' '30.26 Cultural studies/critical theory and analysis ' '30.28 Dispute resolution ' '30.31 Human computer interaction ' '30.33 Sustainability studies ' '30.34 Anthrozoology ' '30.36 Cultural studies and comparative literature ' '30.40 Economics and foreign language/literature ' '30.44 Geography and environmental studies ' and '30.46 History and political science.'176: Includes '30.10 Biopsychology ' '30.18 Natural sciences ' '30.19 Nutrition sciences ' '30.27 Human biology ' '30.32 Marine sciences ' '30.35 Climate science ' '30.38 Earth systems science ' '30.41 Environmental geosciences ' '30.42 Geoarchaeology ' '30.43 Geobiology ' and '30.50 Mathematics and atmospheric/oceanic science.'177: Includes '30.06 Systems science and theory ' '30.08 Mathematics and computer science ' '30.30 Computational science ' '30.39 Economics and computer science ' '30.48 Linguistics and computer science ' '30.49 Mathematical economics ' '30.70 Data science' and '30.71 Data analytics.'178: Veterinary medicine veterinary science veterinary technology and veterinary administrative support services which were included in series '51. Health professions and related programs' in CIP 2016 are now included in series '01. Agricultural and veterinary sciences/services/operations and related fields' in CIP 2021.179: Includes '30.00 Inclusive postsecondary education' and '30.99 Multidisciplinary/interdisciplinary studies other.'180: For information on classification and data quality for this variable refer to the Education Reference Guide Census of Population 2021 Catalogue no. 98-500-X2021013.181: 'Postsecondary certificate diploma or degree' includes 'apprenticeship or trades certificate or diploma ' 'college CEGEP or other non-university certificate or diploma' and university certificates diplomas and degrees.182: The location of study is not compared with the location of residence for persons who studied outside Canada. The locations of study outside Canada that are listed here are those which were the most frequently reported at the Canada level.183: The official name of Moldova is Republic of Moldova.