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.

Context

The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content

This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset

Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Structure

This dataset (world_population_data.csv) covering from 1970 up to 2023 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

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.

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

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.

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.

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.