The population density in Brazil saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 25.16 inhabitants per square kilometer. Nevertheless, 2022 still represents a peak in the population density in Brazil with 25.16 inhabitants per square kilometer. Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.

Population density (people per sq. km of land area) in Brazil was reported at 25.16 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Historical dataset showing Brazil population density by year from 1961 to 2022.

Brazil BR: Population Density: People per Square Km data was reported at 25.643 Person/sq km in 2021. This records an increase from the previous number of 25.508 Person/sq km for 2020. Brazil BR: Population Density: People per Square Km data is updated yearly, averaging 18.346 Person/sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 25.643 Person/sq km in 2021 and a record low of 9.013 Person/sq km in 1961. Brazil BR: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Brazil: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

The history of modern Brazil begins in the year 1500 when Pedro Álvares Cabral arrived with a small fleet and claimed the land for the Portuguese Empire. With the Treaty of Torsedillas in 1494, Spain and Portugal agreed to split the New World peacefully, thus allowing Portugal to take control of the area with little competition from other European powers. As the Portuguese did not arrive with large numbers, and the indigenous population was overwhelmed with disease, large numbers of African slaves were transported across the Atlantic and forced to harvest or mine Brazil's wealth of natural resources. These slaves were forced to work in sugar, coffee and rubber plantations and gold and diamond mines, which helped fund Portuguese expansion across the globe. In modern history, transatlantic slavery brought more Africans to Brazil than any other country in the world. This combination of European, African and indigenous peoples set the foundation for what has become one of the most ethnically diverse countries across the globe.

Independence and Monarchy By the early eighteenth century, Portugal had established control over most of modern-day Brazil, and the population more than doubled in each half of the 1800s. The capital of the Portuguese empire was moved to Rio de Janeiro in 1808 (as Napoleon's forces moved closer towards Lisbon), making this the only time in European history where a capital was moved to another continent. The United Kingdom of Portugal, Brazil and the Algarves was established in 1815, and when the Portuguese monarchy and capital returned to Lisbon in 1821, the King's son, Dom Pedro, remained in Brazil as regent. The following year, Dom Pedro declared Brazil's independence, and within three years, most other major powers (including Portugal) recognized the Empire of Brazil as an independent monarchy and formed economic relations with it; this was a much more peaceful transition to independence than many of the ex-Spanish colonies in the Americas. Under the reign of Dom Pedro II, Brazil's political stability remained relatively intact, and the economy grew through its exportation of raw materials and economic alliances with Portugal and Britain. Despite pressure from political opponents, Pedro II abolished slavery in 1850 (as part of a trade agreement with Britain), and Brazil remained a powerful, stable and progressive nation under Pedro II's leadership, in stark contrast to its South American neighbors. The booming economy also attracted millions of migrants from Europe and Asia around the turn of the twentieth century, which has had a profound impact on Brazil's demography and culture to this day.

The New Republic

Despite his popularity, King Pedro II was overthrown in a military coup in 1889, ending his 58 year reign and initiating six decades of political instability and economic difficulties. A series of military coups, failed attempts to restore stability, and the decline of Brazil's overseas influence contributed greatly to a weakened economy in the early 1900s. The 1930s saw the emergence of Getúlio Vargas, who ruled as a fascist dictator for two decades. Despite a growing economy and Brazil's alliance with the Allied Powers in the Second World War, the end of fascism in Europe weakened Vargas' position in Brazil, and he was eventually overthrown by the military, who then re-introduced democracy to Brazil in 1945. Vargas was then elected to power in 1951, and remained popular among the general public, however political opposition to his beliefs and methods led to his suicide in 1954. Further political instability ensued and a brutal, yet prosperous, military dictatorship took control in the 1960s and 1970s, but Brazil gradually returned to a democratic nation in the 1980s. Brazil's economic and political stability fluctuated over the subsequent four decades, and a corruption scandal in the 2010s saw the impeachment of President Dilma Rousseff. Despite all of this economic instability and political turmoil, Brazil is one of the world's largest economies and is sometimes seen as a potential superpower. The World Bank classifies it as a upper-middle income country and it has the largest share of global wealth in Latin America. It is the largest Lusophone (Portuguese-speaking), and sixth most populous country in the world, with a population of more than 210 million people.

In 2024, the Southeast was the most populated region in Brazil, according to the estimations. In that year, more than 88 million people lived in the four states of this region: Espírito Santo, Minas Gerais, Rio de Janeiro and São Paulo. The Central-West region, where the country's capital, Brasília, is located, was the least populated region in the South American country in 2021, with only 17 million inhabitants. Throughout the past decade, Brazil's population has grown at slower rates than before.

Comprehensive socio-economic dataset for Brazil 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.

As of 2021, Ecuador had a population density of 72 people per squared kilometer, the highest in South America. Colombia ranked second, with 42 people per km2 of land area. When it comes to total population in South America, Brazil had the largest number, with over 216 million inhabitants.

Chart and table of population level and growth rate for the Sao Paulo, Brazil metro area from 1950 to 2025.

Abstract The term vulnerability has been more frequently used in several studies, striving to better understand the specificities and needs of different population groups. The scope of this study was to identify, characterize and analyze populations in situations of socio-environmental vulnerability in Rio de Janeiro city, consolidating social, economic, environmental, health and public security indicators in a synthesis index – the Socio-Environmental Vulnerability Index. The data sources used were the IBGE-2010 Demographic Census, the Geo-Rio Foundation and the Public Security Institute of the state of Rio de Janeiro. The methodology integrated Multicriteria Decision Analysis into a Geographic Information System. According to our results, the socio-environmental vulnerability in Rio de Janeiro city is aggravated by risk situations and environmental degradation. Those aspects are accentuated by the population density in shantytown areas, where the most disadvantaged strata exist in a process of environmental and urban exclusion. The study makes it possible to locate spatially vulnerable areas, emphasizing the importance of these tools to guide resource allocation, formulation and implementation of more effective public policies.

Urban population (% of total population) in Brazil was reported at 88.02 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This dataset consists of spatiotemporal data on counts of the soil mite Rostrozetes ovulum (Oribatida: Haplozetidae) in central Amazonia, along with data on climate and litterfall variables used to model the mite's population dynamics.We sampled the mite in 20 transects a 800-ha forest remnant in Manaus, northern Brazil (03°04’34”S; 59°57’30”W). Each transect was 20-m long. Transects were distributed all over the forest landscape and sampled from June 2014 to June 2015. Ten transects were in valleys, while the remaining transects were located on plateaus, at least 150 m away from any drainage catchment. At each transect, one soil sample was taken each meter using an aluminum soil corer (3.5 × 3.5 × 5 cm), covering a total of 245 cm2. This material was taken to the laboratory, where the soil fauna was extracted using a Berlese-Tullgren apparatus (Franklin & Morais 2006). Each soil core was put in a sieve with mesh size 1.5 mm, which was placed in a plastic funnel. Then, the funnel was put into a wooden box, where it was fitted through a perforated polystyrene board, with a glass vial filled with 95 percent alcohol below it. Next, the box was gradually heated from ambient temperature (ca. 27ºC) to 35 – 40 ºC using light bulbs (25 W). Vials were checked daily for fallen animals. Heating lasted until the core was completely dry and animals stopped falling into the vial (7 to 10 days). The collected material was surveyed under a stereomicroscope for R. ovulum. Adult individuals were counted and preserved in 95 percent alcohol. Transects were sampled on nine months (June to September and November 2014; and January, March, April and June 2015). Therefore, the spatiotemporal coverage of our study was 20 transects × 13 months = 240 spatiotemporal units, of which 20 transects × 9 surveys = 180 counts were recorded from a total of 3600 soil cores.Environmental seasonality data were obtained from research sites nearby the study area, or estimated from such sites. Temperature and rainfall data were gathered online from the nearest station of the Brazilian Institute for Meteorology (INMET), which is 1 km from the study area. We extracted daily readings to compute cumulative rainfall (mm) and maximum daily air temperature (°C) for each transect and month covered by our sampling.Litterfall was estimated using time series of monthly litter production per habitat (plateau and valley) from the Cuieiras Biological Reserve (22,735-ha), 60 km from the study area. Litterfall was sampled with 30 PVC collectors (50 × 50 cm) randomly placed 50 cm above ground in each habitat, between May 2004 and December 2005, January 2009 and December 2010, and November 2014 and August 2015. In parallel, we obtained meteorological data from the INMET station corresponding to the litterfall measurements to model the latter as a function of (1) monthly sunlight hours, monthly cumulative rainfall and their interaction, (2) habitat (valley or plateau), and (3) time (months, coded as integers spanning the temporal coverage of the data) in order to account for any long-term trend. The model was the used to predict the expected litterfall for each spatiotemporal unit in which the mite was sampled, given the corresponding environmental conditions.

Raw perpendicular distance data obtained for population density estimates using distance-sampling method, for three endangered bird taxa endemic to the Pernambuco Center of Endemism, northeastern Brail. The analyzed taxa were the White-shouldered Antshrike Thamnophilus aetiops distans (Edangered), the Brown-winged Mourner Schiffornis turdina intermedia (Vulnerable), and the White-bellied Tody-tyrant Hemitriccus griseipectus naumburgae (Vulnerable).

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

The data were restricted to the hot season (November to March).

In 2025, there were approximately **** medical doctors per 1,000 people in Brazil, an increase compared to the physician density of about **** doctors per 1,000 inhabitants reported a year earlier. That same year, the number of doctors registered in the South American country totaled about ******* professionals, most of them based in São Paulo.

Odds ratios (OR) and 95% confidence intervals (95% CI) for the cumulative effect of heat waves on cardiovascular and respiratory mortality, over 5 and 10-day lags respectively, in the city of Rio de Janeiro, Brazil, stratified by age and sex.

The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of - 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).