Brazil BR: Population in Largest City data was reported at 22,806,704.000 Person in 2024. This records an increase from the previous number of 22,619,736.000 Person for 2023. Brazil BR: Population in Largest City data is updated yearly, averaging 15,288,036.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 22,806,704.000 Person in 2024 and a record low of 4,493,182.000 Person in 1960. Brazil BR: Population in Largest City 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 in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

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!

Abstract This paper compares the occupational structure of cities in Brazil and United States aiming to evaluate the extent to which the economic structure of these urban agglomerations is associated with the different stages of development, specifically when comparing a rich country with a developing one. Using a harmonized occupational database and microdata from the Brazilian 2010 Demographic Census and the U.S. American Community Survey (2008-2012), results show that Brazilian cities have a stronger connection between population size, both with occupational structure and human capital distribution, than the one found for cities in the United States. These findings suggest a stronger primacy of large cities in Brazil’s urban network and a more unequal distribution of economic activity across cities when compared to USA, indicating a strong correlation between development and occupational structure.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Brazil: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 140(4.03%) households where the householder is under 25 years old, 1,151(33.13%) households with a householder aged between 25 and 44 years, 1,096(31.55%) households with a householder aged between 45 and 64 years, and 1,087(31.29%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of Brazil, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil median household income by age. You can refer the same here

Context

Brazil has a very powerful Freedom of Information law which allows any citizen to request any data from the government which is not restricted, and where these restrictions are well defined exceptions. But still, having the right to request the information does not mean it is easy to get it. Bureaucracy and ignorance of the law gets in the way many times. In order to encourage the government to put their databases in order and to inspire people to have the courage to ask the government for information, we made a massive request of information, for the complete dataset of crime data available for the last 10 years, in the biggest city of South America.

Content

This dataset contains structured data about all crime occurrences that have been acted upon by the PM, the main police force in Sao Paulo. The dataset is not consistent in its completeness, as some of the towns comprising the Greater Sao Paulo were slow in collecting full data. It also does not contain the actual historic of each crime report, as that would violate privacy.

Acknowledgements

We would like to acknowledge the prompt assistance from the SSP (Secretaria de Seguranca Publica), for providing the data with minimal resistance.

Inspiration

Primarily we would like to see a visualisation of this data, so that the people can have an idea of how crime has evolved in their city, which crimes are more prevalent in which areas, etc. In addition, any model which can predict at what times and where the police is most needed would be helpful, as this can then be sent to the SSP to help them in planning.

Abstract School choice is an issue little studied in Brazil, despite its huge importance, especially in the USA. In this paper, using game theory, how students are allocated in municipality of São Paulo is analyzed. As students ‘preferences are not taken into account, the São Paulo system does not meet the main qualities of an allocation mechanism: stability, non-manipulation and efficiency. Alternatively, the use of the Gale-Shapley mechanism is proposed. Simulations are performed confirming theoretical results and also indicating a huge potential for improvement in the system.

These files contain the data and scripts needed to replicate the analyses found in "City Size and Public Service Access: Evidence from Brazil and Indonesia."

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Brazil: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 140(4.21%) households where the householder is under 25 years old, 1,113(33.48%) households with a householder aged between 25 and 44 years, 1,082(32.55%) households with a householder aged between 45 and 64 years, and 989(29.75%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of Brazil, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil median household income by age. You can refer the same here

The capybara is the largest living rodent, attracting attention due to its large size, its formation of large herds, and because it is commonly seen in urban environments. Thus, understanding the dynamics of capybara populations living in urban environments is relevant, especially given the conflicts observed between the species and humans in these environments. Here, we investigated the hypothesis of overpopulation of the capybara in Lago Paranoá, a lake in a large neotropical city, BrasÃlia, Brazil. To do this, we investigated their spatial distribution at the site and estimated the capybara population using a variation of the mark-recapture method and compared it to known population estimates for the species. We found that the capybaras in our study area mainly form small flocks of 1 to 9 animals and occupy almost the entire shore of Lake Paranoá. We estimated the occurrence of 0.30 to 0.52 ind./ha (average = 0.41 ind./ha), demonstrating that the number of capybaras in our region is ..., , # Population estimate and spatial distribution of capybaras in Lake Paranoá, BrasÃlia, Brazil

Dataset DOI: 10.5061/dryad.fttdz094g

Description of the data and file structure

Over a year (10/2021 - 09/2022), the shore of Lake Paranoá was covered with the help of a voadeira (aluminum boat with an outboard motor) at a speed of around 20 km/h and approximately 30 m from the shore (Figure 2). The same route was covered every month for 12 months. We standardized the counts for the afternoon, after 4 pm, based on the literature, which reports greater activity of the species at dusk and dawn (Moreira et al., 2013c). Due to the large expanse of the shore of Lake Paranoá, complete monitoring took place over four sampling days, totaling around 8 hours of sampling per month. Counts were carried out on consecutive days whenever possible, except in cases of adverse weather conditions. When activities were canceled, the count was restarted on the next day with suitab...,

Do you want to explore and predict real estate data of the biggest city of south emisphere, 4th largest in the world? Sao Paulo - Brazil has over 14,000 official real state transactions per month. This dataset shows REAL transactions and values registered in the city hall (it is not advertising scrapping). That means you will be dealing with real market values, aside of expeculations. You can predict property prices, check the most valued or devalued districts, look for features that affect prices the most, find trends on the different type of properties and much more. The data is quite recent, from May/22 to Oct/22.

Column descriptions: tax_id: tax id at the city hall registers street_name: street name where the property is located street_number: property street number complement: complement like apartment number, block or tower in an apartment building, etc. district: zone or district in the Sao Paulo city, where the property is located reference: general reference of the property zip_code: zip code transaction_nature: legal motivation for that transaction like a simple buy/sell, or a transmission of rights, person-company transferences, etc. transaction_value_BRL: real value of the transaction in BRL (Brazilian Reais) date: date of the transaction cadastral_value: property value in the city hall registers in BRL (Brazilian Reais) tax_base_value: base value for transaction tax calculation in BRL (Brazilian Reais) mortgage_type: mortgage type, if any mortgage_value: value in BRL (Brazilian Reais) of the mortgage registry_number: real estate registration office id property_id: property id in the real estate registration offices city_hall_status: status of the property according to the city hall land_area_m2: area of the property in squared meters (m2) front_length_m: front length of the property, facing the street in meters (m) ideal_fraction: fraction of the total property transactioned area_built_m2: property built area in squared meters (m2) description_1: occupation description description_2: type of property year_built: year of the construction conclusion

Abstract Background: Hypertension is the main risk factor for cardiovascular diseases. Technical quality of sphygmomanometers is a prerequisite for the correct measurement of arterial pressure. Objectives: To evaluate sphygmomanometers available in emergency services in the city of Belo Horizonte, Brazil. Methods: We performed a cross-sectional, observational, non-interventional study to evaluate characteristics of the sphygmomanometers available in adult emergency services of public and private hospitals in the city of Belo Horizonte, Brazil. We evaluated 337 sphygmomanometers of 25 hospitals - 15 (of 16) public hospitals and 10 (of 12) private hospitals. Results: Twenty-six percent (88/337) of devices were considered inadequate regarding the INMETRO (National Institute of Metrology, Quality and Technology) standards, 39.2% (132/337) for calibration dates, and 54% (188/337) for the mismatching between cuff's and device's brands. In 13 of 25 hospitals (52%), there were no spare cuffs in different sizes for different arm circumferences. Higher adequacy was found for aneroid and mercury sphygmomanometers used in private hospitals (p = 0.038 and p < 0.001, respectively) and electronic devices used in public hospitals (p < 0.001) compared with others. Conclusion: Seventy-eight percent of sphygmomanometers available in emergency services had technical inadequacies, and half of these services had no spare cuffs in different sizes available. These findings serve as a warning of the conditions of the equipment used in healthcare services provided to the general population in Brazil.

Congenital syphilis (CS) is a significant public health problem in Brazil. Despite efforts to increase syphilis testing and treatment among pregnant women, rates of CS in the country remain high. We conducted a retrospective case-control study to identify potential associations between the mothers’ sociodemographic characteristics, clinical factors related to the current and previous pregnancies, and the occurrence of CS among newborns in Fortaleza, a populous city with one of the highest incidences of CS in Brazil. Data from newborns diagnosed with CS between 2017 and 2020 were extracted from SINAN, the national database for notifiable diseases. Data from women who had delivered an infant with CS were extracted from SINASC, the national database for registration of live births, and linked with their infant’s data. CS cases and non-CS controls were matched by year of birth at a ratio of 1:3 respectively. Potential associations were estimated using a multivariate regression model accounting for sociodemographic, obstetric, and antenatal care-related factors. Epidemiological data from 8,744 live births were included in the analysis, including 2,186 cases and 6,588 controls. The final multivariate regression model identified increased odds of delivering an infant with CS among pregnant women and girls aged below 20 years (OR 1.29), single women (OR 1.48), women who had less than 8 years of formal education (OR 2.42), women who delivered in a public hospital (OR 6.92), women who had more than 4 previous pregnancies (OR 1.60), and women who had one or more prior fetal loss (OR 1.19). The odds of delivering an infant with CS also increased as the number of antenatal visits decreased. Women who did not attend any antenatal visits had 3.94 times the odds of delivering an infant with CS compared to women who attended 7 or more visits. Our study found that increased odds of delivering an infant with CS were highly associated with factors related to socioeconomic vulnerability. These determinants not only affect the access to essential antenatal care services, but also the continuity and quality of such preventive measures. Future policies aimed at reducing the incidence of CS should not only target those pregnant women and adolescents with identifiable risk factors for testing, but also assure high quality care, treatment and follow-up for this group.