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.

The average for 2023 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 2023. Below is a chart for all countries where data are available.

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.

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 2025, 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.

Historical data for Population density in LLDC: Latin America from 2020 to 2026

Historical data for Population density in Latin America and the Caribbean from 2020 to 2026

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).

Historical data for Population density in South America from 2020 to 2026

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.

• 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.

This statistic shows the density of fintech startups in selected Latin American countries as of February 2019. Chile was the country with the highest number of fintechs among those selected, with more than **** fintech companies per *** million inhabitants.

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.

Historical data for Total population in Latin America and the Caribbean from 1975 to 2026

Historical data for Total population in LLDC: Latin America from 1975 to 2026

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.

In 2020, the total population of Mexico City reached 9.2 million and a population density of 6,163.3 residents by square kilometer. Population density has grown considerably in the country's capital during the past few decades, as it stood at 5,494 inhabitants per square meter in 1990.

Historical data for Total population in South America from 1975 to 2026

In 2020, Chile had *** technology-based startups per million population, the highest density in Latin America and the Caribbean. With a considerably higher population, Brazil reported a lower startup density, standing at *** businesses per million inhabitants. At least ** out of the ** countries depicted reported less than *** tech startup per million population in 2020.