The average for 2021 based on 12 countries was 25 people per square km. The highest value was in Ecuador: 72 people per square km and the lowest value was in Guyana: 4 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

As of 2025, Barbados was the most densely populated country in Latin America and the Caribbean, with approximately 657.16 people per square kilometer. In that same year, Argentina's population density was estimated at approximately 16.75 people per square kilometer.

Our Population Density Grid Dataset for South America offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

As of 2023, the top five most densely populated cities in Latin America and the Caribbean were in Colombia. The capital, Bogotá, ranked first with over ****** inhabitants per square kilometer.

In 2022, Haiti ranked first by population density among the 21 countries presented in the ranking. Haiti's population density amounted to ****** people, while El Salvador and the Dominican Republic, the second and third countries, had records amounting to ****** people and ****** people, respectively.

Historical dataset showing Latin America & Caribbean population density by year from 1961 to 2022.

The Latin America and the Caribbean Population Time Series data set provides total population estimates using spatially consistent and comparable units for Latin American municipalities or equivalent administrative units for the years 1990 and 2000. The data set consists of two vector polygon layers: one layer displays population estimates for subnational administrative units in 1990 and 2000, including population counts, density, and percent change, at the municipality level or equivalent (level 2); a second layer summarizes this information at the country level (level 0). To describe changing population distribution and growth in Latin America and the Caribbean using spatially consistent and comparable units at a spatial resolution suitable to regional change analysis.

As of 2023, the largest segment of the population in Latin America falls within the age group of 19 to 30 years, which consists of the youth population. This age range comprises approximately 127.9 million individuals across the countries encompassing the region.

This bar chart displays population (people) by country using the aggregation sum in South America. The data is about countries per year.

In 2023, Brazil ranked first by total population among the 24 territories presented in the ranking. Brazil's total population amounted to 211.14 million people, while Mexico and Colombia, the second and third territories, had records amounting to 129.74 million people and 52.32 million people, respectively.

This bar chart displays urban population (people) by region using the aggregation sum in South America. The data is about countries.

High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.

• The variable names are used in Random Forest model output and throughout the text as reference to the specific data they were derived from. The first three letters are derived from the data type (e.g. “lan” indicates land cover) and the last three letters, if present, indicates what type of data each variable represents (e.g. “_cls” is a binary classification and “_dst” is a calculated Euclidean distance-to variable.† The default data for populated places is merged from several VMAP0 data sources. There are three VMAP0 data sets used: The point data pop/builtupp and pop/mispopp are buffered to 100 m and merged with the pop/builtupa polygons creating avector-based built layer. This layer is then converted to binary class and distance-to rasters for use in modeling.Country-specific data sources and variable names used for population density estimation used for dasymetric weights.

The statistic shows age distribution in Latin America & Caribbean between 2014 to 2024. In 2024, around 22.51 percent of the population of Latin America & Caribbean was between 0 and 14 years old, 67.65 percent was between 15 and 64 and 9.84 percent was 65 years old and over.

DATA DESCRIPTION: Version 2.0 estimates of total number of people per grid square for five timepoints between 2000 and 2020 at five year intervals; national totals have been adjusted to match UN Population Division estimates for each time point(1) REGION: Latin America and the Caribbean SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - LAC_PPP_2010_adj_v2.tif = Latin America and the Caribbean (LAC) population dataset presenting people per pixel (PPP) for 2010, adjusted to match UN national estimates (adj), dataset version 2.0 (v2) DATASET CONSTRUCTION DETAILS: This dataset is a mosaic of all WorldPop country level LAC datasets resampled to 1km resolution. The continental grouping of countries honours the macro geographical classification developed and maintained by the United Nations Statistics Division(2). For countries within each continental group which have not been mapped by WorldPop, GPWv4 1km population count data(3) was used to complete the mosaic. Full details of WorldPop population mapping methodologies are described here: www.worldpop.org.uk/data/methods/ DATE OF PRODUCTION: November 2016 Also included: (i) csv table describing the data source of the modelled population data for each country dataset (either WorldPop or GPWv4) which featured in the continental raster mosaic. _ (1) United Nations Population Division, WorldPopulation Prospects, 2015 Revision. http://esa.un.org/wpp/ (2) United Nations Statistics Division. http://unstats.un.org/unsd/methods/m49/m49regin.htm (3) Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4X63JVC. Accessed 30 Sept 2016

This bar chart displays urban population (people) by demonym using the aggregation sum in South America. The data is about countries.

The first column identifies the year each sample was carried out; columns 2-5 are the elements of the population projection matrix and represent the parameters the fecundity (newborn females per adult female (a13), the survival of female newborns (a21), the survival of female juveniles (a32), and the survival of female adults (a33). “Fem newborns”, “Fem Juveniles” and “Fem Adult” are the number of guanacos of each age class (the population vector) as calculated by the matrix model for each year; “Fem Population” results from the addition of the previous three columns, that is, it represents the total female population resulting from the matrix model. “Total Population both sexes (model)” is the total guanaco population (both sexes) originated from “Fem Population” multiplied by 2 (the sex-ratio in the guanacos is 1:1). “Total field population” is the total field guanaco population obtained by sampling by the Distance method (see text). “SSQ” is the sum of squares function as the goodness of fit criterion used for fitting the differences between the “Total Population both sexes (model)” and “Total field population” by changing the values of the population matrix elements (“a13”, “a21”, “a32” and “a33”). “Lambda” is the population growth rate (lambda) as estimated for each year with the PopTools “add-in” of the Excel spreadsheet based on the corresponding transition matrix of that year.

This bar chart displays female population (people) by countries using the aggregation sum in South America. The data is about countries.

Estimated density of people per grid-cell, approximately 1km (0.008333 degrees) resolution. The units are number of people per Km² per pixel, expressed as unit: "ppl/Km²". The mapping approach is Random Forest-based dasymetric redistribution. The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty. Acknowledgements information at https://www.worldpop.org/acknowledgements

Two different error assessment methods are presented: root mean square error (RMSE), also expressed as a percentage of the mean population size of the administrative level (% RMSE); and the mean absolute error (MAE).Accuracy assessment results for the RF, Afri/AsiaPop, GRUMP and GPW modeling methods for Cambodia, Vietnam and Kenya.