From 1990 to 2024, the population of the city of Rio de Janeiro grew almost every year, from approximately 5.5 million to 6.7 million residents, which is an increase of 22 percent. The data shows several fluctuations; however, these may be attributed to changes in methodology. It is unclear whether the changes shown in the 2022 census are due to counting methodologies, or if it is representative of the impact of the COVID-19 pandemic. Growth of the population The city of Rio de Janeiro has consistently had a higher birth rate than the death rate, although the gap between these two indicators is narrowing. In 2023, 9.3 births were registered per 1,000 inhabitants of Rio, while in the same period a rate of 8.4 deaths per 1,000 inhabitants was reported. The surrounding of the city Considering the entire metropolitan area of Rio de Janeiro, the region is home to approximately 13 million people. Rio's population is aging, with about eight million people over the age of 30 and half this value between the ages of 30 and 49. In the most recent census, whites made up nearly 44 percent of the population, followed by Pardo Brazilians, who composed about 37 percent of all residents.

Brazil Population Census: Southeast: Rio de Janeiro: Cabo Frio data was reported at 186,227.000 Person in 2010. This records an increase from the previous number of 162,229.000 Person for 2007. Brazil Population Census: Southeast: Rio de Janeiro: Cabo Frio data is updated yearly, averaging 144,528.500 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 186,227.000 Person in 2010 and a record low of 115,759.000 Person in 1996. Brazil Population Census: Southeast: Rio de Janeiro: Cabo Frio data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC055: Population Census: by Municipality: Southeast: Rio de Janeiro.

Abstract This article aims to analyze the process of urban expansion of the State of Rio de Janeiro that took place from the 1980s to the 2010s, trying to understand the meaning of internalization of population growth in the state. In this analysis, we place emphasis on internal migration, considering its key role in this process. In this sense, the study discusses the change in the role played by the State of Rio de Janeiro in the context of internal migrations in Brazil and the loss of importance of interstate migration as determinant of the expansion of urban areas and internalization of growth. In addition, this article demonstrates the increasing importance of intrastate migration to such processes. The key (consolidated) indicators of internal migration and the mean annual rates of population growth showed that since around the early 1980s, the municipalities along the northern coast of Rio de Janeiro - Coastal Lowlands and North of Rio de Janeiro - have been the ones with the greatest positive net migration, the largest TLM and the highest rates of population growth. These migration flows represent, therefore, the main front of internalization of the State. It is important to note that these migrations consist of as till timid process of internalization of growth, given the huge concentration of the population in the metropolitan area of Rio de Janeiro.

Population Census: Southeast: Rio de Janeiro: Age 40 to 49 Years data was reported at 2,244,818.000 Person in 2010. This records an increase from the previous number of 1,923,003.000 Person for 2000. Population Census: Southeast: Rio de Janeiro: Age 40 to 49 Years data is updated yearly, averaging 2,083,910.500 Person from Jul 2000 (Median) to 2010, with 2 observations. The data reached an all-time high of 2,244,818.000 Person in 2010 and a record low of 1,923,003.000 Person in 2000. Population Census: Southeast: Rio de Janeiro: Age 40 to 49 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC028: Population Census: by State and Age: Southeast: Rio de Janeiro.

Historical dataset of population level and growth rate for the Rio de Janeiro, Brazil metro area from 1950 to 2025.

Brazil Population Census: Southeast: Rio de Janeiro: São João da Barra data was reported at 32,747.000 Person in 2010. This records an increase from the previous number of 28,889.000 Person for 2007. Brazil Population Census: Southeast: Rio de Janeiro: São João da Barra data is updated yearly, averaging 30,818.000 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 63,939.000 Person in 1996 and a record low of 27,682.000 Person in 2000. Brazil Population Census: Southeast: Rio de Janeiro: São João da Barra data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC055: Population Census: by Municipality: Southeast: Rio de Janeiro.

Brazil Population: Residents: Southeast: Rio de Janeiro: Metropolitan Region of Rio de Janeiro data was reported at 12,182.000 Person th in 2015. This records an increase from the previous number of 12,133.000 Person th for 2014. Brazil Population: Residents: Southeast: Rio de Janeiro: Metropolitan Region of Rio de Janeiro data is updated yearly, averaging 11,591.000 Person th from Sep 1992 (Median) to 2015, with 17 observations. The data reached an all-time high of 12,182.000 Person th in 2015 and a record low of 9,888.541 Person th in 1992. Brazil Population: Residents: Southeast: Rio de Janeiro: Metropolitan Region of Rio de Janeiro data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA004: Population: by States.

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.

Brazil Population Census: Southeast: Rio de Janeiro: Guapimirim data was reported at 51,483.000 Person in 2010. This records an increase from the previous number of 44,692.000 Person for 2007. Brazil Population Census: Southeast: Rio de Janeiro: Guapimirim data is updated yearly, averaging 41,322.000 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 51,483.000 Person in 2010 and a record low of 32,614.000 Person in 1996. Brazil Population Census: Southeast: Rio de Janeiro: Guapimirim data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC055: Population Census: by Municipality: Southeast: Rio de Janeiro.

Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age Over 90 Years data was reported at 480,418.000 Person in 2060. This records an increase from the previous number of 472,133.000 Person for 2059. Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age Over 90 Years data is updated yearly, averaging 178,019.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 480,418.000 Person in 2060 and a record low of 42,074.000 Person in 2010. Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age Over 90 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB028: Population: Projection: by Age: Southeast: Rio de Janeiro.

Brazil Population: Residents: Southeast: Rio de Janeiro: Três Rios data was reported at 81,453.000 Person in 2018. This records an increase from the previous number of 79,402.000 Person for 2017. Brazil Population: Residents: Southeast: Rio de Janeiro: Três Rios data is updated yearly, averaging 75,403.000 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 81,453.000 Person in 2018 and a record low of 66,361.000 Person in 1997. Brazil Population: Residents: Southeast: Rio de Janeiro: Três Rios data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA051: Population: by Municipality: Southeast: Rio de Janeiro.

Brazil Population Census: Southeast: Rio de Janeiro: Maricá data was reported at 127,461.000 Person in 2010. This records an increase from the previous number of 105,294.000 Person for 2007. Brazil Population Census: Southeast: Rio de Janeiro: Maricá data is updated yearly, averaging 91,015.500 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 127,461.000 Person in 2010 and a record low of 60,286.000 Person in 1996. Brazil Population Census: Southeast: Rio de Janeiro: Maricá data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC055: Population Census: by Municipality: Southeast: Rio de Janeiro.

Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age 80 to 84 Years data was reported at 724,730.000 Person in 2060. This records an increase from the previous number of 710,828.000 Person for 2059. Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age 80 to 84 Years data is updated yearly, averaging 441,153.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 724,730.000 Person in 2060 and a record low of 181,088.000 Person in 2010. Brazil Population: Projection: Residents: Southeast: Rio de Janeiro: Age 80 to 84 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB028: Population: Projection: by Age: Southeast: Rio de Janeiro.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of black households in the rural Eastern Cape and black and colored households in urban Western Cape.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or colored EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Stats SA) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Colored)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban colored (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban colored households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least

1.Traditionally, trait‐based studies have explored single‐trait‐fitness relationships. However, this approximation in the study of fitness components is often too simplistic, given that fitness is determined by the interplay of multiple traits, which could even lead to multiple functional strategies with comparable fitness (i.e. alternative designs).

2.Here we suggest that an analytical framework using boosted regression trees (BRT) can prove more informative to test hypotheses on trait combinations compared to standard linear models. We use two published datasets for comparisons: a botanical garden dataset with 557 plant species (Herben et al., 2012) and an observational dataset with 83 plant species (Adler et al., 2014).

3.Using the observational dataset, we found that BRTs predict the role of traits on the relative importance of survival, growth, and reproduction for population growth rate better than linear models do. Moreover, we split species cultivated in different habitats within the botanical garden and observed that seed and vegetative reproduction depended on trait combinations in most habitats. Our analyses suggest that, while not all traits impact fitness components to the same degree, it is crucial to consider traits that represent different ecological dimensions.

4.Synthesis:The analysis of trait combinations, and corresponding alternative designs via BRTs, represent a promising approach for understanding and managing functional changes in vegetation composition through measurement of suites of relatively easily measurable traits.

Brazil Population Census: Southeast: Rio de Janeiro: Rio Bonito data was reported at 55,551.000 Person in 2010. This records an increase from the previous number of 51,942.000 Person for 2007. Brazil Population Census: Southeast: Rio de Janeiro: Rio Bonito data is updated yearly, averaging 50,816.500 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 55,551.000 Person in 2010 and a record low of 46,495.000 Person in 1996. Brazil Population Census: Southeast: Rio de Janeiro: Rio Bonito data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC055: Population Census: by Municipality: Southeast: Rio de Janeiro.

Brazil Population: Projection: Residents: Southeast: Male: Rio de Janeiro: Age 45 to 49 Years data was reported at 547,491.000 Person in 2060. This records an increase from the previous number of 537,640.000 Person for 2059. Brazil Population: Projection: Residents: Southeast: Male: Rio de Janeiro: Age 45 to 49 Years data is updated yearly, averaging 586,987.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 649,739.000 Person in 2044 and a record low of 522,853.000 Person in 2010. Brazil Population: Projection: Residents: Southeast: Male: Rio de Janeiro: Age 45 to 49 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB055: Population: Projection: by Age: Male: Southeast: Rio de Janeiro.

Brazil Population: Projection: Residents: Southeast: Female: Rio de Janeiro: Age 65 to 69 Years data was reported at 581,430.000 Person in 2060. This records an increase from the previous number of 580,710.000 Person for 2059. Brazil Population: Projection: Residents: Southeast: Female: Rio de Janeiro: Age 65 to 69 Years data is updated yearly, averaging 525,655.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 621,503.000 Person in 2049 and a record low of 274,229.000 Person in 2010. Brazil Population: Projection: Residents: Southeast: Female: Rio de Janeiro: Age 65 to 69 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB082: Population: Projection: by Age: Female: Southeast: Rio de Janeiro.

This study was an assessment of time trends in morbid obesity prevalence in the adult population in Brazil’s state capitals from 2006 to 2017. A cross-sectional study was performed with data from the Risk and Protective Factors Surveillance System for Chronic Noncommunicable Diseases Through Telephone Interview (Vigitel), analyzed by simple linear regression. The results showed an upward trend in morbid obesity prevalence in Brazil. Women showed higher prevalence rates (1.3% in 2006 and 1.9% in 2017) when compared to men (0.9% and 1.4%). The 25-44-year age bracket showed an upward trend from 0.9% to 2.1% (p < 0.001). There was an increase in morbid obesity in all levels of schooling and all regions of Brazil. The state capitals with upward trends in males were Campo Grande, Curitiba, Fortaleza, Macapá, Manaus, Palmas, Porto Velho, Rio Branco, and Teresina. In females, they were Belo Horizonte, Campo Grande, Rio de Janeiro, and Teresina. The growth in morbid obesity in Brazil sounds a warning on the urgent need to adopt measures to detain it, such as regulation of ultra-processed foods and health education measures for the entire population.

Brazil Population Census: Southeast: Rio de Janeiro: Age 60 Years or More data was reported at 2,080,608.000 Person in 2010. This records an increase from the previous number of 1,540,754.000 Person for 2000. Brazil Population Census: Southeast: Rio de Janeiro: Age 60 Years or More data is updated yearly, averaging 1,810,681.000 Person from Jul 2000 (Median) to 2010, with 2 observations. The data reached an all-time high of 2,080,608.000 Person in 2010 and a record low of 1,540,754.000 Person in 2000. Brazil Population Census: Southeast: Rio de Janeiro: Age 60 Years or More data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC028: Population Census: by State and Age: Southeast: Rio de Janeiro.