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.;;

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?

Analysis of ‘Temperature Time-Series for some Brazilian cities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/volpatto/temperature-timeseries-for-some-brazilian-cities on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Temperature Time-Series for some Brazilian cities

Do you ever wonder how are temperatures in Brazilian cities? Too hot? Cold weather sometimes? And what about climate changes? Is Brazil getting hotter?

This is your chance to check it out!

Context

This datasets are collected in order to provide some answers for the above question through Data Analysis. Maybe you want to try some Machine Learning model in order to practice and predict the evolution of temperature in some Brazilian cities.

Content

The content is provided by NOAA GHCN v4 and post-processed by NASA's GISTEMP v4.

In summary, each data file contains a temperature time series for a station named according to the city. The time series provides temperature records by month for each year. Some mean measurement is calculated, like metANN and D-J-F. I can't give details about these quantities, nor how they are calculated. Please refer for NASA GISTEMP website in this regard. The most important seems to be metANN, which is an annual temperature mean.

Acknowledgements

These datasets are provided through NASA's GISTEMP v4 and recorded by NOAA GHCN v4. Thanks for researchers and staffs for the really nice work!

--- Original source retains full ownership of the source dataset ---

Temperature Time-Series for some Brazilian cities

Do you ever wonder how are temperatures in Brazilian cities? Too hot? Cold weather sometimes? And what about climate changes? Is Brazil getting hotter?

This is your chance to check it out!

Context

This datasets are collected in order to provide some answers for the above question through Data Analysis. Maybe you want to try some Machine Learning model in order to practice and predict the evolution of temperature in some Brazilian cities.

Content

The content is provided by NOAA GHCN v4 and post-processed by NASA's GISTEMP v4.

In summary, each data file contains a temperature time series for a station named according to the city. The time series provides temperature records by month for each year. Some mean measurement is calculated, like metANN and D-J-F. I can't give details about these quantities, nor how they are calculated. Please refer for NASA GISTEMP website in this regard. The most important seems to be metANN, which is an annual temperature mean.

Acknowledgements

These datasets are provided through NASA's GISTEMP v4 and recorded by NOAA GHCN v4. Thanks for researchers and staffs for the really nice work!

About this dataset

Context

The dataset tabulates the Brazil household income by age. The dataset can be utilized to understand the age-based income distribution of Brazil income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brazil, IN annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Brazil, IN household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brazil income distribution by age. You can refer the same here

This repository includes data set and source code used in "Topic Resilience and the Spread of News: Brazilian Cities Case Study".

Raw Data.zip file: Contains news publications collected from Brazilian cities through December 12th, 2018 to March 26th, 2019.

Source code.zip file: Contains four Jupiter files with the source code of each step of the TopicRes framework.

This dataset provides a regional detailed overview of the Brazil digital music consumption in Spotify between 2021-2023. It includes acoustic features and all genres/artists that are listened at least one time in those years. The data is provided by the Spotify API for Developers and the SpotifyCharts wich are used to collect the acoustic features and the summarized most listened songs in city, respectively.

Data description

It contemplates 17 cities of 16 different states in Brazil that achieved 5190 unique tracks, 487 different genres and 2056 artists. The covered cities are: Belém, Belo Horizonte, Brasília, Campinas, Campo Grande, Cuiabá, Curitiba, Florianópolis, Fortaleza, Goiânia, Manaus, Porto Alegre, Recife, Rio de Janeiro, Salvador, São Paulo and Uberlândia. Each city has 119 different weekly's charts wich the week period is described by the file name.

Acoustic features

The covered acoustic features are provided by Spotify and are described as: - Acousticness: Measures from 0.0 to 1.0 of wheter the track is acoustic; 1.0 indicates a totally acoustic song and 0.0 means a song without any acoustic element - Danceability: Describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. - Energy: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. - Instrumentalness: Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. - Key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. - Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. - Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. - Mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0. - Speechiness: Detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. - Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. - Time Signature: An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of "3/4", to "7/4". - Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Data Science Applications:

• Time Series Analysis: Identify seasonal behaviors and the deviation of each city during those 2 years
• Trend Analysis: Identify patterns and trends in digital music consumption based in genres and/or acoustic features in each city to understand seasonal changes
• Clustering Tasks: Group cities based on genre and/or acoustic features to identify different regional patterns between Brazil's regions and describe the difference between each group

About this dataset

Context

The dataset tabulates the Brazil median household income by race. The dataset can be utilized to understand the racial distribution of Brazil income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brazil, IN median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Brazil, IN (2021, 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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brazil median household income by race. You can refer the same here

This repository includes data set and source code used in "Topic Resilience and the Spread of News: Brazilian Cities Case Study".

Raw Data.zip file: Contains news publications collected from Brazilian cities through December 12th, 2018 to March 26th, 2019.

News Websites.zip file: Contains the address list of the online newspapers and blogs used to collect the dataset.

Source code.zip file: Contains four Jupiter files with the source code of each step of the TopicRes framework.

City level open access data from 26 States and the Federal District and from the Brazilian Institute of Geography and Statistics (IBGE) [20], the Department of Informatics of Brazilian Public Health System – DATASUS, Ministry of Health, the Brazilian Agricultural Research Corporation (Embrapa) and from Brazil.io. Data from all 5,570 cities in Brazil were included in the analysis. COVID-19 data included cases and deaths reported between February 26th, 2020 and February 4th, 2021. The following outcomes were computed: a) days between the first case in Brazil until the first case in the city; b) days between the first case in the city until the day when 1,000 cases were reported; and c) days between the first death in city until the day when 50 deaths inhabitants were reported. Descriptive analyses were performed on the following: proportion of cities reaching 1,000 cases; number of cases at three, six, nine and 12 months after first case; cities reporting at least one COVID-19 related death; number of COVID-19 related deaths at three, six, nine and 12 months after first death in the country. All incidence data is adjusted for 100,000 inhabitants.The following covariates were included: a) geographic region where the city is located (Midwest, North, Northeast, Southeast and South), metropolitan city (no/yes) and urban or rural; b) social and environmental city characteristics [total area (Km2), urban area (Km2), population size (inhabitants), population living within urban area (inhabitants), population older than 60 years (%), indigenous population (%), black population (%), illiterate older than 25 years (%) and city in extreme poverty (no/yes)]; c) housing conditions [household with density >2 per dormitory (%), household with garbage collection (%), household connected to the water supply system (%) and household connected to the sewer system (%)]; d) job characteristics [commerce (%) and informal workers (%)]; e) socioeconomic and inequalities characteristics [GINI index; income per capita; poor or extremely poor (%) and households in informal urban settlements (%)]; f) health services access and coverage [number of National Public Health System (SUS) physicians per inhabitants (100,000 inhabitants), number of SUS nurses per inhabitants (100,000 inhabitants), number of intensive care units or ICU per inhabitants (100,000 inhabitants). All health services access and coverage variables were standardized using z-scores, combined into one single variable categorized into tertiles.

About this dataset

Context

The dataset tabulates the Brazil household income by gender. The dataset can be utilized to understand the gender-based income distribution of Brazil income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brazil, IN annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Brazil, IN annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brazil income distribution by gender. You can refer the same here

This polygon shapefile contains the municipal boundaries for the state of Mato Grosso do Sul, Brazil, in 2010. Municipalities are subdivisions of Brazilian states. The seat of the municipal administration is a denominated city, with no consideration from the law about the population, area or facilities. The city has the same name of the municipality. Municipalities can be subdivided, only for administrative purposes, in districts (normally, new municipalities are formed from these districts). Other populated sites are villages, but with no legal effects or regulation. This layer is part of the Evolução da divisão territorial do Brasil 1872 - 2010 dataset, a collection of data representing the evolution of Brazilian states, municipalities and cities.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

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

Abstract: Income variables from the Brazilian population census (IBGE) are often used as proxies for the population’s socioeconomic level in spatial analyses of urban segregation, inequality and social exclusion. However, income variables are dependent on reference values (minimum wage) that change over time, which can be challenging for multitemporal analysis. This paper discusses this issue and proposes a methodology to adjust income data that allows a meaningful comparison between the datasets of two Census periods. The methodology was applied to five medium-sized cities of the state of São Paulo by adjusting income data from Census 2000 and 2010 according to the period’s inflation rates. The analysis shows that the methodology mitigates the comparability issues. Results better reflect the changes in population composition and in residential patterns of different income groups that took place over the 2000s in Brazil in medium-sized cities.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Includes the epidemiological data, wastewater SARS-COV-2 quantification (.csv files), and the R code used for the analysis (.html from Rmarkdown).

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Abstract: Passive smoking causes severe and lethal effects on health. Since 1996 Brazil has been moving forward in the implementation of anti-smoking legislation in enclosed public spaces. This article aims to evaluate the perceived enforcement of anti-smoking legislation in the cities of Porto Alegre (Rio Grande do Sul State), Rio de Janeiro and São Paulo, Brazil, based on the results of the ITC-Brazil Survey (International Tobacco Control Policy Evaluation Project). The results of the survey showed a significant reduction in the proportion of people who saw individuals smoking in restaurants and bars between 2009 and 2013 in the three cities surveyed. Concurrently there was an increase in the proportion of smokers who mentioned having smoked in the outer areas of these facilities. These results likely reflect a successful implementation of anti-smoking laws. Of note is the fact that by decreasing passive smoking we further enhance smoking denormalization among the general population, decreasing smoking initiation and increasing its cessation.