This dataset is about countries in Caribbean. It has 13 rows. It features 3 columns: land area, and rural population.

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world.

This map features a global estimate of human population for 2016 with a focus on the Caribbean region . Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones.

This scatter chart displays urban land area (km²) against rural population (people) in Caribbean. The data is about countries.

The population of Latin America and the Caribbean increased from 175 million in 1950 to 515 million in 2000. Where did this growth occur? What is the magnitude of change in different places? How can we visualize the geographic dimensions of population change in Latin America and the Caribbean? We compiled census and other public domain information to analyze both temporal and geographic changes in population in the region. Our database includes population totals for over 18,300 administrative districts within Latin America and the Caribbean. Tabular census data was linked to an administrative division map of the region and handled in a geographic information system. We transformed vector population maps to raster surfaces to make the digital maps comparable with other commonly available geographic information. Validation and error-checking analyses were carried out to compare the database with other sources of population information. The digital population maps created in this project have been put in the public domain and can be downloaded from our website. The Latin America and Caribbean map is part of a larger multi-institutional effort to map population in developing countries. This is the third version of the Latin American and Caribbean population database and it contains new data from the 2000 round of censuses and new and improved accessibility surfaces for creating the raster maps.

Based on land area, Brazil is the largest country in Latin America by far, with a total area of over 8.5 million square kilometers. Argentina follows with almost 2.8 million square kilometers. Cuba, whose surface area extends over almost 111,000 square kilometers, is the Caribbean country with the largest territory.

Brazil: a country with a lot to offer

Brazil's borders reach nearly half of the South American subcontinent, making it the fifth-largest country in the world and the third-largest country in the Western Hemisphere. Along with its landmass, Brazil also boasts the largest population and economy in the region. Although Brasília is the capital, the most significant portion of the country's population is concentrated along its coastline in the cities of São Paulo and Rio de Janeiro.

South America: a region of extreme geographic variation

With the Andes mountain range in the West, the Amazon Rainforest in the East, the Equator in the North, and Cape Horn as the Southern-most continental tip, South America has some of the most diverse climatic and ecological terrains in the world. At its core, its biodiversity can largely be attributed to the Amazon, the world's largest tropical rainforest, and the Amazon river, the world's largest river. However, with this incredible wealth of ecology also comes great responsibility. In the past decade, roughly 80,000 square kilometers of the Brazilian Amazon were destroyed. And, as of late 2019, there were at least 1,000 threatened species in Brazil alone.

This scatter chart displays land area (km²) against male population (people) in Caribbean. The data is about countries.

Gridded Population of the World, Version 4 (GPWv4): Land and Water Area - Water Area displays estimates of the water area (including permanent ice and water) per pixel in square kilometers. See more information at: http://dx.doi.org/10.7927/H45M63M9.

Comprehensive socio-economic dataset for Caribbean Netherlands including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

The data were produced by WorldPop at the University of Southampton. These data include gridded population estimates, at approximately 100m resolution, for 40 countries in Latin America and the Caribbean (Appendix A). These results were created using official population estimates at the finest-available resolution provided by National Statistic Offices (NSOs) throughout the region, and built-up area, height and volume covariates produced from World Settlement Footprint 3D (WSF3D) datasets1. We acknowledge the contribution of WorldPop’s partners, notably the United Nations Population Fund (UNFPA) Latin America and Caribbean Regional Office in supporting the collection of population and administrative boundary data, and to the German Aerospace Center (DLR) for preparing and providing built settlement data from the WSF3D framework. Modelling work and geospatial data processing was carried out by McKeen T., Bondarenko M., Kerr D. and Sorichetta A. Esch T., Marconcini M., Zeidler J. and Palacios-Lopez D. prepared and provided the WSF3D datasets. Juran S. and Valle C. aided with population and administrative boundary data collection. Oversight was provided by Andrew J. Tatem fourth and final part.

LAC is the most water-rich region in the world by most metrics; however, water resource distribution throughout the region does not correspond demand. To understand water risk throughout the region, this dataset provides population and land area estimates for factors related to water risk, allowing users to explore vulnerability throughout the region to multiple dimensions of water risk. This dataset contains estimates of populations living in areas of water stress and risk in 27 countries in Latin America and the Caribbean (LAC) at the municipal level. The dataset contains categories of 18 factors related to water risk and 39 indices of water risk and population estimates within each with aggregations possible at the basin, state, country, and regional level. The population data used to generate this dataset were obtained from the WorldPop project 2020 UN-adjusted population projections, while estimates of water stress and risk come from WRI’s Aqueduct 3.0 Water Risk Framework. Municipal administrative boundaries are from the Database of Global Administrative Areas (GADM). For more information on the methodology users are invited to read IADB Technical Note IDB-TN-2411: “Scarcity in the Land of Plenty”, and WRIs “Aqueduct 3.0: Updated Decision-relevant Global Water Risk Indicators”.

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

This scatter chart displays urban land area (km²) against female population (people) in Caribbean. The data is about countries.

Objectives: Direct comparative work in morphology and growth on widely dispersed wild primate taxa is rarely accomplished, yet critical to understanding ecogeographic variation, plastic local varia- tion in response to human impacts, and variation in patterns of growth and sexual dimorphism. We investigated population variation in morphology and growth in response to geographic variables (i.e., latitude, altitude), climatic variables (i.e., temperature and rainfall), and human impacts in the vervet monkey (Chlorocebus spp.).

Methods: We trapped over 1,600 wild vervets from across Sub-Saharan Africa and the Caribbean, and compared measurements of body mass, body length, and relative thigh, leg, and foot length in four well-represented geographic samples: Ethiopia, Kenya, South Africa, and St. Kitts & Nevis.

Results: We found significant variation in body mass and length consistent with Bergmann’s Rule in adult females, and in adult males when excluding the St. Kitts & Nevis population, which was more sexually dimorphic. Contrary to Rensch’s Rule, although the South African population had the largest average body size, it was the least dimorphic. There was significant, although very small, variation in all limb segments in support for Allen’s Rule. Females in high human impact areas were heavier than those with moderate exposures, while those in low human impact areas were lighter; human impacts had no effect on males.

Conclusions: Vervet monkeys appear to have adapted to local climate as predicted by Bergmann’s and, less consistently, Allen’s Rule, while also responding in predicted ways to human impacts. To better understand deviations from predicted patterns will require further comparative work in vervets.

Methods The data derive from field collections made over many years using a common protocol: Ethiopia in 1973, Kenya in 1978-79; South Africa in 2002–2008, and several African countries and the Caribbean in 2009– 2011 in collaboration with the International Vervet Research Consortium (Jasinska et al., 2013). The International Vervet Research Consortium is a multidisciplinary research group that has, in addition to morphological variation, studied variation in patterns of growth and development (Schmitt et al., 2018), genetic/genomic (Jasinska, et al., 2013; Schmitt et al., 2018; Svardal et al., 2017; Turner et al. 2016a; Warren et al. 2015) and transcriptomic (Jasinska et al., 2017) variation, SIV immune response (Ma et al., 2013, 2014; Svardal et al., 2017), hor- monal variation (Fourie et al., 2015), C4 isotopes variation in hair (Loudon et al., 2014), gut parasite and disease variation (Gaetano et al., 2014; Senghore et al., 2016), genital morphology and appearance (Cramer et al., 2013; Rodriguez et al., 2015a,b), and other biological parameters within the genus Chlorocebus.

Vervet monkeys were trapped at locations across sub-Saharan Africa, including South Africa, Botswana, Zambia, Ethiopia, The Gambia, Ghana, and on the Caribbean islands of St. Kitts and Nevis (Figure 1). Trapping in Africa employed individual drop traps as described by Brett, Turner, Jolly, & Cauble (1982) and Grobler and Turner (2010), while trapping in St. Kitts and Nevis was done by local trappers using large group traps (Jasinska et al., 2013). Animals were anesthetized while in the trap and then removed to a processing area. Sex was determined by visual and manual inspection, while age classes were assigned from dental eruption sequences and based on previous observations (Table 2). All animals were weighed with either an electronic or hanging scale, and measured with a tape measure and sliding calipers. Parameters and protocols describing all measurements are available through the Bones and Behavior Working Group (2015; http://www.bonesandbehavior. org/). All animals were released to their social group after sampling and recovery from anesthesia. Observations during trapping allowed us to confirm the animals’ social group and local population affiliation.

For the present study, we chose metrics representative of skeletal size (body length, thigh length, leg length, and foot length) and body mass from a total of 1,613 vervets in four geographically and genomi- cally distinct populations: Ch. aethiops in Ethiopia, Ch. p. hilgerti in Kenya, Ch. p. pygerythrus in South Africa, and Ch. sabaeus on the Carib- bean islands of St. Kitts and Nevis (Table 3). The Caribbean populations are known to be descended from West African Ch. sabaeus brought to the Caribbean several hundred years ago (Warren et al., 2015). Of the whole sample, 288 females and 460 males were dentally immature. Sexual maturity is typically not reached in vervets until near the time of canine tooth eruption, here denoting the beginning of dental age 6 (Cramer et al., 2013; Rodriguez et al., 2015a); although somatic and skeletal growth often continues beyond the emergence of the third molar, which is here denoted as adult (Bolter & Zihlman, 2003). As is common, dental age and skeletal age are presumed to be similarly cor- related across the genus, meaning that comparable dental age implies comparable skeletal developmental age across populations (Seselj, 2013).

All measurements were developed by CJJ and TRT and other measurers (CAS and JDC) were trained directly by TRT. During training, repeated measures of the same individual were conducted in tandem with TRT until concordance was reached.

The location of each trapping site is reported in decimal degrees (Table 1), and for most sites measured using hand-held GPS units. For those trapping sites lacking GPS readings, a general latitude and longi- tude for the trapping area (e.g., game reserve, town) was used. Human impact at each trapping location was assessed according to conditions during the time of trapping using a previously published index devel- oped by Pampush (2010) to study variation in vervet body size, and subsequently used by Loudon et al. (2014) and Fourie et al. (2015) (Table 1). This index includes presence/absence measures of reliable access to (1) agricultural land, (2) human food, (3) rubbish or garbage dumps, and (4) whether animals are regularly provisioned, as well as a three-level scale of human activity within the presumed home range of the group (low, moderate, or high). In the index, point values are assigned to each value, with the lowest tier of human impact each receiving a 1, scaling up by 1 for each level. Added together, these val- ues comprise a human impact group ranging from low (lowest score in each category; index 5 5), to moderate (index 5 6–8), to high (index- 5 9–11). These measures take into account only the ecological impact of humans, and do not address local ecological variables (such as native plant productivity) that might also influence body size and growth. As a proxy for these measures, we collected several climatic variables for trapping sites from the WorldClim 2 database, which has a spatial reso- lution of about 1 km2 (Fick & Hijmans, 2017). Climatic variables consid- ered for inclusion in our models were (1) annual mean temperature (in degrees Celsius), (2) temperature seasonality (measured as the standard deviation of annual mean temperature multiplied by 100), (3) the mini- mum temperature of the coldest month (in degrees Celsius), (4) the mean temperature of the coldest quarter of the year, (5) annual precipi- tation (in mm), and (6) precipitation seasonality (measured as thecoefficient of variation of monthly precipitation). Climate data were accessed via the R package raster v. 2.6-7 (Hijmans & van Etten, 2012), and assigned to trapping sites based on latitude and longitude.

Gridded Population of the World, Version 4 (GPWv4): Population Density displays human population density estimates represented by number of persons per square kilometer in each grid cell for the year 2020, derived by dividing the population count grids by land area grids. See more information at: http://dx.doi.org/10.7927/H4NP22DQ.

The Population and Housing Censuses database contains the censuses harmonized in such a way as to provide comparable census information over time and across countries. The variables in these databases are constructed under a common approach and structure, with standardized names, definitions, and disaggregations, and stored in a single file for each country. Currently, the harmonization of Population and Housing Censuses includes databases for 22 countries. The indicators are categorized into seven themes: demographics, education, labor market, housing, income, migration, and diversity. When possible, we add multiple disaggregations for indicators. The available disaggregations are ethnicity, gender, age, education level, and area of geographic residence. The management and harmonization of this database are provided by the Social Sector (SCL) of the Vice Presidency of Knowledge and Sectors to strengthen the analytical content of projects and studies.

In 2025, the degree of urbanization worldwide was at 58 percent. North America, Latin America, and the Caribbean were the regions with the highest level of urbanization, with over four-fifths of the population residing in urban areas. The degree of urbanization defines the share of the population living in areas defined as "cities". On the other hand, less than half of Africa's population lives in urban settlements. Globally, China accounts for over one-quarter of the built-up areas of more than 500,000 inhabitants. The definition of a city differs across various world regions - some countries count settlements with 100 houses or more as urban, while others only include the capital of a country or provincial capitals in their count. Largest agglomerations worldwideThough North America is the most urbanized continent, no U.S. city was among the top ten urban agglomerations worldwide in 2023. Tokyo-Yokohama in Japan was the largest urban area in the world that year, with 37.7 million inhabitants. New York ranked 13th, with 21.4 million inhabitants. Eight of the 10 most populous cities are located in Asia. ConnectivityIt may be hard to imagine how the reality will look in 2050, with 70 percent of the global population living in cities, but some statistics illustrate the ways urban living differs from suburban and rural living. American urbanites may lead more “connected” (i.e., internet-connected) lives than their rural and/or suburban counterparts. As of 2021, around 89 percent of people living in urban areas owned a smartphone. Internet usage was also higher in cities than in rural areas. On the other hand, rural areas always have, and always will, attract those who want to escape the rush of the city.

In 2024, Brazil and Mexico were expected to be the countries with the largest gross domestic product (GDP) in Latin America and the Caribbean. In that year, Brazil's GDP could reach an estimated value of 2.4 trillion U.S. dollars, whereas Mexico's amounted to almost two trillion U.S. dollars. GDP is the total value of all goods and services produced in a country in a given year. It measures the economic strength of a country and a positive change indicates economic growth.

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This dataset is about countries in Caribbean. It has 13 rows. It features 3 columns: land area, and female population.