Context

A large chain store requires making the decision to create new distribution hosts and shut down hosts that do not generate business value. Nothing better than Machine Learning to support this decision making.

Content

This Dataset contains the main cities in Brazil and this network is already in all of its regional capitals (capital = admin).

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!

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

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 149(4.48%) households where the householder is under 25 years old, 1,000(30.05%) households with a householder aged between 25 and 44 years, 1,136(34.13%) households with a householder aged between 45 and 64 years, and 1,043(31.34%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 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) 2017-2021 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

Abstract Ambulatory care sensitive hospitalizations have been used as an indicator of the effectiveness of primary health care. The research involved a descriptive analysis of the evolution of national indicators from 1998 to 2012 and a cross-sectional study of Brazilian municipalities with populations greater than 50,000, by region of the country, for the year 2012, using correlation and linear regression statistical techniques. There was a slight decline in the proportion of ambulatory care sensitive hospitalizations in Brazil. Socioeconomic and demographic factors and physician supply in the healthcare system are associated with the proportion of ambulatory care sensitive hospitalizations, differing by region of the country. Despite advances in the expansion of the Family Health Strategy, some challenges remain, including better distribution of physicians and other health professionals in the country and effective changes in the healthcare model.

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

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Brazil. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Brazil, the median household income for the households where the householder is White increased by $6,638(15.70%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $42,287 in 2011 and $48,925 in 2021.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2011 and 2021.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/brazil-in-median-household-income-by-race-trends.jpeg" alt="Brazil, IN median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Brazil.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

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 race. You can refer the same here

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."

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.

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...,

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.

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