The population density in Brazil amounted to 25.16 people in 2022. In a steady upward trend, the population density rose by 16.23 people from 1961.

Population density (people per sq. km of land area) in Brazil was reported at 25.26 sq. Km in 2023, 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 November of 2025.

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

View yearly updates and historical trends for Brazil Population Density. Source: World Bank. Track economic data with YCharts analytics.

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.

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 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.

Dataset Overview

This dataset provides a comprehensive overview of Brazil’s 2022 Census data, focusing on São Paulo’s neighbourhoods. The data combines demographic and socioeconomic information with geospatial shapefiles of São Paulo’s neighbourhoods, enabling users to perform statistical and spatial analyses.

Users can explore patterns, trends, and transformations in São Paulo’s urban landscape by linking census sectors to neighbourhood boundaries.

Key Components

Census 2022 Data

• Source: Brazil's 2022 Census (IBGE)
• Content: Demographic data including age, gender, income, education levels, household size, and population density across census sectors.
• Format: CSV

São Paulo Neighborhood Shapefile

• Source: GIS-based shapefiles for São Paulo neighbourhoods (IBGE Census Sectors and Manually created Neighbourhoods)
• Content: Spatial geometry for São Paulo's neighbourhoods with census sector identifiers.
• Format: Parquet

Use Cases

• Neighborhood Demographics Analysis: Combine census data with shapefiles to generate neighborhood-level demographic reports.
• Urban Development Studies: Study how São Paulo neighbourhoods have grown using historical context and 2022 Census data.
• Spatial Data Visualizations: Create maps showing income distribution, population density, or other demographic factors across neighbourhoods.
• Policy Planning & Research: Support urban planning, resource allocation, and policy development in São Paulo.

Potential Applications

• Analyze the relationship between neighbourhood demographics and urban growth patterns.
• Visualize inequalities in population distribution, income, or education levels.
• Identify trends in housing and population density for urban studies.
• Provide insights into São Paulo’s historical and ongoing transformations.

Why Use This Dataset?

• Comprehensive Coverage: Detailed census data and spatial boundaries allow in-depth analyses.
• Flexible Integration: Easily combine demographic data with shapefiles to enable advanced spatial analyses.

Dataset Details

• File Formats: CSV (Census Data), GeoJSON/Shapefile (Neighborhood Shapefiles)
• Spatial Resolution: Census sector linked to São Paulo’s neighbourhood boundaries

Geographic Scope: São Paulo, Brazil

This dataset is ideal for data scientists, urban planners, and researchers seeking to uncover the dynamics of São Paulo’s neighbourhoods through an intersection of demographic and spatial data.

Contribute to new insights and empower decision-making in understanding Brazil’s largest city!

This dataset collects information on municipal expenditures, water-sewerage-and trash collection service coverage, and basic socioeconomic characteristics at municipal level, for two census waves (2000; 2010) for all municipalities of Brazil, Chile, and Mexico.

ABSTRACT The spotted paca, Cuniculus paca (Linnaeus, 1766), is a Neotropical, opportunistic, frugivorous caviomorph rodent, that inhabits primarily broadleaf forests. We aimed to provide the first estimates of density of C. paca for the Ilha Grande, an island located in the Atlantic Rain Forest biome of Brazil. Density and population size were estimated using the total number of individuals observed along each trail through the program DISTANCE 7. Our estimates of density and population size reinforces the importance of the Ilha Grande as an important reservoir of the species. Therefore, the results presented herein can be a starting point to support future action plans for the species, making predictions regarding the ecosystem and management and conservation of the spotted paca. Furthermore, the results can be used as a surrogate for other regions in which the species occurs.

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

The dataset supplied comprises a comprehensive collection of information pertaining to numerous geographical and environmental characteristics across microregions in Brazil from 2000 to 2019. Microregion codes and names, mesoregion codes and names, state codes and names, region codes and names, biome codes and names, ecozone codes and names, climate regimes, months, years, times, dengue cases, population estimates, population density, maximum and minimum temperatures, Palmer's drought severity index, urban population percentages, access to water network percentages, and reported water shortage frequency are all included in the dataset. This information is linked to individual microregions and provides insights into population dynamics, climatic patterns, urbanization trends, water resources, and disease occurrences.

Abstract In light of the forms of access to housing for low-income population in Brazil, this paper analyzes, specifically, the living conditions of slum dwellers using available census data. It evaluates the meaning of living in a slum in Brazil during the first decade of the 21st century according to some key issues: was there an increase in the number of slum dwellers in Brazil? Where was this increase most significant? Was this increase produced by new slums or by the increase in slums’ population density? What are the characteristics of slum households? Was there any improvement in infrastructure-related indicators? The paper is also an unprecedented effort to analyze the 2010 Census "Territorial Reading", a single database for slum households.

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).

The aim of this study was to evaluate the performance of the Centers for Dental Specialties (CDS) in the country and associations with sociodemographic indicators of the municipalities, structural variables of services and primary health care organization in the years 2004-2009. The study used secondary data from procedures performed in the CDS to the specialties of periodontics, endodontics, surgery and primary care. Bivariate analysis by χ2 test was used to test the association between the dependent variable (performance of the CDS) with the independents. Then, Poisson regression analysis was performed. With regard to the overall achievement of targets, it was observed that the majority of CDS (69.25%) performance was considered poor/regular. The independent factors associated with poor/regular performance of CDS were: municipalities belonging to the Northeast, South and Southeast regions, with lower Human Development Index (HDI), lower population density, and reduced time to deployment. HDI and population density are important for the performance of the CDS in Brazil. Similarly, the peculiarities related to less populated areas as well as regional location and time of service implementation CDS should be taken into account in the planning of these services.

Feira de Santana–Bahia, April to September, 2017.

This data set provides the results of (1) synoptic streamwater sampling and analyses from numerous sites across Rondonia and (2) corresponding watershed characteristics derived from remote sensing and historical/available data sources. Sixty streams, in both forested and non-forested sites, were sampled once during the dry season in August of 1998 and 49 of the same streams were sampled again during the wet season in January-February of 1999. Analyses included sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), silica (Si), chloride (Cl), sulfate, pH, and acid neutralizing capacity. Watershed characteristics, including soil cation content, pH, watershed lithology, area, percent deforested, and urban watershed population density, were derived and calculated from digitized soil maps and available soil profile analyses, digitized topographic maps, land use mosaics from Landsat Thematic Mapper (TM) images, and Brazilian census data. The objective of the study was to determine the relative influence of watershed soil exchangeable cation content, rock type, deforestation, and urban population density on stream concentrations of base cations, dissolved silicon, chloride and sulfate in both the dry and wet seasons in a humid tropical region undergoing regional land use transformation. There are three comma-delimited data files with this data set.

The present study assessed the identification of animal and object hoarding disorder cases by contact and mapping and the presence of animal protection programs in association with seven social–economic indicators of the metropolitan area of the ninth-biggest metropolitan area of Brazil. City Secretaries of Health and Environment provided demographic information and responded to a questionnaire. Overall, a very high level of hoarding case identification per municipality was associated with a higher Human Development Index, population, density, and income and related to distance from Curitiba, the capital of Parana State. Low and very low levels of hoarding case identification were related to greater area, higher Social Vulnerability Index (SVI), inequality, illiteracy, and rural areas. Very high identification level of animal protection programs was also associated with higher HDI, density and population, urban area, and high income, and geographical area. Similarly, low and very low levels of animal protection programs identification were major explained by low income, illiteracy, and distance related to higher population, urbanization, and higher HDI. In summary, better identification of hoarding cases and animal protection programs have shown an association with better socioeconomic indicators and higher population, density, and urban area. Whether municipalities with better human socioeconomic indicators may stimulate society's demands for identification of cases of individuals with hoarding disorder and animal programs should be further established. Regardless, animal health and welfare have been associated with improving human quality of life in a major Brazilian metropolitan area.

Disclosure of information with far right's ideas, negationism of science and anti-vaccine attitude x Risk of COVID-19

The electoral preference by Bolsonaro in the first round of Brazil presidential election 2018 per state, shows a relation with the amount of deaths by Covid-19 per 100000, excess death per 100,000, increased P-score and intensity in reducing Brazilian population growth in the 1st quarter 2021

Content

In the period from January to April (1st Quadrimester Q1) from 2021 and 2019 per state (UF)

Main variables for each of the 27 Brazilian states and 4 States groups

1. The main population rates: - Number deaths, excess deaths, births, birth rate, mortality rate, vegetative growth, p-score, total population, population> 70A., Demographic density

2. The main rates of Pandemic by Coronavirus - Covid-19:

   • No. Total cases, cases Q1, Nº Total deaths, Nº Q1 deaths, Total deaths / 100000 hab, mortality rate, cases / 100000 hab

3. The main metrics of the 2018 presidential election:

   • Voters, voting paragraphs, nº of votes in Bolsonararo 1st turn, nº of abstinences.

Groups of Brazilian UFS (Federation States)

1. States that Bolsonaro received more than 50% of the votes in the 1st turn
2. States that Bolsonaro received less than 50% of the votes in the 1st turn and more than 50% in the 2nd turn
3. States that Bolsonaro received less than 50% of the votes in the 1st and 2nd shifts
4. Sum of the 27 Brazilian states

PT(BR) - version

Divulgação de informações com idéias da extrema direita, negacionismo da ciência e atitude anti-vacina x risco de Covid-19

A preferência eleitoral por Bolsonaro no 1º turno de 2018 por estado, mostra-se relacionada com a quantidade de mortes por COVID-19, excesso de mortes por 100000, aumento do P-score e intensidade na redução do crescimento populacional brasileiro no 1ºquadrimestre de 2021.

No período de Janeiro a Abril(1º Quadrimestre Q1) de 2021 e 2019 por estado (UF)

Principais variáveis

1. As principais taxas populacionais: - nº mortes, excesso de mortes, nº nascimentos, taxa de natalidade, taxa de mortalidade, crescimento vegetativo, P-score, população total, população > 70a., densidade demográfica

2. As principais taxas da pandemia por Coronavirus - COVID-19:

   • nº casos totais, nº casos Q1, nº mortes totais, nº mortes Q1, mortes totais/100000 hab, taxa de Mortalidade, casos/100000 hab

3. As principais métricas da eleição presidencial de 2018:

   • nº eleitores, nº votantes, nº de votos em Bolsonaro 1º turno, nº de abstinências.

Grupos de UFs (Estados da Federação)

1.Estados que Bolsonaro recebeu mais de 50% dos votos no 1º turno 2.Estados que Bolsonaro recebeu menos que 50% dos votos no 1º turno e mais de 50% no 2º turno 3.Estados que Bolsonaro recebeu menos que 50% dos votos no 1º e 2º turnos 4.Soma dos 27 Estados Brasileiros

In 2024, the number of licensed plastic surgeons in Brazil amounted to approximately ***** doctors. This is equivalent to **** plastic surgeons per 100,000 population, a decrease in density of *** in comparison to 2022 figures. In 2013, there were an estimated **** plastic surgeons per 100,000 population in the South American country, the lowest density recorded during the period depicted.