The Brazilian Participatory Budgeting (PB) Census is a data set that Participedia contributor Paolo Spada began collecting in 2008. It integrates and updates previous data collection efforts by Ribeiro & De Grazia (1997-2002) and Avritzer & Wampler (2004). The census includes all Brazilian cities with more than 50,000 inhabitants from 1989 through 2012 and identifies those that have implemented Participatory Budgeting. The resulting data set contains approximately 500 cities for six time periods, divided into four-year intervals. The PB census is geolocated and easily integrates with existing data sets maintained by the Institute for Applied Economic Research (IPEA), the Superior Electoral Court (TSE), and the Brazilian Institute of Geography and Statistics (IBGE). The census uses Yves Sintomer's five criteria to define participatory budgeting, and is created using a two-step methodology. First, internet research is conducted to identify elements that indicate the potential presence of participatory budgeting in a city. Next, each potential PB site is contacted by phone to determine whether or not the city has implemented participatory budgeting. The data set uses a four-year coding scheme that follows the electoral cycle. A city is coded as having implemented participatory budgeting if it has completed at least two cycles of participatory budgeting. This method was used twice to conduct a census: first in the spring of 2008 and a second time in the spring of 2012. See "Full PB Census 1989 to 2012.") In 2012, Spada together with Brian Wampler, Mike Touchton, and Denilson Coelho, implemented a more detailed telephonic survey in the census to obtain data on the variety of designs of participatory budgeting. The results of the new survey are included below. (See "PB Census 2012 - variety of PB survey.") In 2016 Spada has replicated the census for a third time thanks to the support of the EMPATIA project and in collaboration with Wagner de Melo Romão, Brian Wampler, and Mike Touchton. Many additional Brazilian scholars have provided comments and support. The research is funded by the EMPATIA project and by a grant from the São Paulo Research Foundation (FAPESP). The data is currently being reviewed and after a short embargo period will be released (December 2018). Below it is possible to access the previous iteration of the census with data up to 2012. A sneak preview of the 2016 dataset shows a further decline in participatory budgeting implementation. The data is currently being used in several ongoing research projects. If you have questions about these Brazilian Participatory data sets, please contact Paolo Spada. How to cite these data sets Use of these data sets is governed by the Creative Commons Attribution Non-Commercial-Sharelike 3.0 Unported license (CC BY-NC-SA 3.0). Please use the following APSA style format when citing these data sets: Spada, P. 2012. "Brazilian Participatory Budgeting Census: 1989-2012." Available at: http://dx.doi.org/10.7910/DVN/EDSNJS (DATE OF ACCESS). Spada, P., Wampler, B., Touchton, M. & Coelho, D. 2012. "Variety of Brazilian Participatory Budgeting Designs: 2012." Available at: http://dx.doi.org/10.7910/DVN/EDSNJS (DATE OF ACCESS). Citation in Text: (Spada 2012) (Spada, Wampler, Touchton, and Coelho 2012)

This is a GSM dataset acquired in partnership with a Brazilian telecom company that comprises several areas in diverse states in Brazil with a 15-minute granularity.The dataset contains 526.894 instances from 12 months or 350 days starting in September 2017 and finishing in September 2018 consisting of 4.545 individuals. The points were recorded in many cities in Brazil with a coarse granularity of one point every 15 minutes. No information about the users is derived from these data as the entire dataset is anonymized. Each point consists of a user sequential identification number, a pair of (latitude, and longitude), and a timestamp.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)

Postal Codes Dataset for Brazil, BR including name of the city, town, or place, various administrative divisions and alternative city names.

This point shapefile contains the locations of state capitals in Brazil in 1900. These data were created using a custom World Polyconic Projection. 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.Read More

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 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

This polygon shapefile contains the municipal boundaries for the state of Mato Grosso do Sul, Brazil, in 2001. 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.Read More

This dataset contains land use data of Cambé-PR, Brazil, collected by the authors from 2018 to 2020, available in two formats: table (.csv) and shapefile (.zip).

To address the absence of comparable city-level water data, this dataset compiles data from 15 global South cities located in sub-Saharan Africa, South Asia, and Latin America and among the regions that are the focus of the World Resources Report (WRR) "Towards a More Equal City". The 15 cities are Kampala, Uganda; Lagos, Nigeria; Maputo, Mozambique; Mzuzu, Malawi; Nairobi, Kenya; Bengaluru, India; Colombo, Sri Lanka; Dhaka, Bangladesh; Karachi, Pakistan; Mumbai, India; Caracas, Venezuela; Cochabamba, Bolivia; Rio de Janeiro, Brazil; São Paulo, Brazil; and Santiago de Cali, Colombia. To compile a data set on each city, we collaborated with local researchers who had a minimum of seven years of experience in the water sector. Data were obtained from a combination of interviews, fieldwork in an informal settlement, publicly available data sets, administrative records, websites, and project documents. Researchers in each city conducted an average of seven key informant interviews. Data were collected about household water and sanitation access at the city level and fieldwork was conducted in one informal settlement in each city. The dataset includes cost, % coverage, availability, and cost burdens on household water and sanitation practices; water intermittency; household treatment practices; access to facilities; citywide sanitation infrastructure; cost of on-site sanitation construction, and fecal sludge removal; fees for piped sewage; the lining of pit latrines; and proximity of septic tanks and pit latrines to water sources. At the city level, data were collected on the water utility, the city’s sources of water, and the water utility’s legal and administrative status. We augmented the city-level data with fieldwork and data from one informal settlement in each city, for two reasons: (1) city-level data are usually presented in averages and thus tend to mask extremes at both ends of the socioeconomic distribution; and (2) in many cities, informal settlements are excluded from formal city-level statistics because their land occupation is considered illegal. To select the informal settlement in each city, the researchers identified a centrally located, well-established settlement that did not represent either the city’s “best” or “worst” conditions but instead represented challenges to water access common in similar settlements in the city.

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

This polygon shapefile represents the provincial boundaries Brazil in 1872. These data were created using a custom World Polyconic Projection. 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.Read More

Abstract This paper presents a comparative analysis of residents from Brazilian cities and Hong Kong to an incentive of travelling as sport event tourists. The findings from Hong Kong (n=134) and São Paulo (n=151) reveal their different travel incentives, ability and characteristics in terms of annual and infrequent sport events. When determining interest in a sport event, excitement and safety are of paramount concern to respondents from both territories. As sport event tourism does not automatically flourish and remain sustained after the presence of a mega-event, city governments are recommended to react to the preferences of specific market.

The original COVID-19 dataset included information about tested patients, containing early-stage symptoms, comorbidities, demographics information, and symptoms description. The patients were tested by applying viral or rapid tests. The raw data was collected by the public health agency of the city of Campina Grande, Paraíba state, in Northeast Brazil. Such a public agency is informed by all the COVID-19 exams performed in the city of Campina Grande. The health agency employees removed patient identification, and the data made available were reused to enable this study.

We preprocessed the dataset by selecting only completed tests, being marked as positive or negative, applied string matching algorithms to correct some inconsistencies, and removed rows with duplicated instances and asymptomatic patients. We also focused on the most frequent and relevant demographics information and reported early-stage symptoms to select features, and balanced the data considering positive and negative cases by random undersampling using the NearMiss algorithm. We also use unbalanced datasets.

Using this dataset, we implemented and evaluated supervised machine learning models for COVID-19 detection in Brazil based on early-stage symptoms and basic personal information.

This dataset relates to the study entitled "Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil".

The General Transit Feed Specification (GTFS) for the city of Rio de Janeiro, Brazil, also known as GTFS static or static transit to differentiate it from the GTFS realtime extension, defines a common format for public transportation schedules and associated geographic information.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

Tourism is increasing around the world and has many benefits for countries and cities, such as creating jobs, increasing company revenue, and improving government tax collection. As such, tourism is an unstoppable trend followed by countries and municipalities that try to stimulate this activity. However, unexpected impacts of this, in principle, wealthy activity, must be observed.

This is the dataset for the paper "Evidence Analysis of Tourism and Geographic Location Correlation with Syphilis Incidence in Bahia State—Brazil." It contains a valuable set of data and information concerning the incidence of syphilis in Bahia state, Brazil, for 10 years, suitable for machine learning algorithm analysis. A KMeans clustering analysis database is included in the dataset.

This polygon shapefile contains the microregion boundaries for the state of Rio de Janeiro, Brazil, in 2001. Microregions are legally defined administrative areas in Brazil consisting of groups of municipalities bordering urban areas. Microregions are grouped together into mesoregions. In principle, Brazilian law provides for member municipalities to cooperate on matters of common interest, but in practice, these divisions are primarily used for statistical purposes by the Brazilian Institute of Geography and Statistics. 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.Read More

This article presents some of the reflections produced by the Possible Gardens research, which explores the world of gardens where living beings interact directly, creating multispecific worlds. It is directed toward everyday gardens, which are still very present in Brazilian cities. It uses comparative case studies of multiple exemplar cases throughout the Arrudas River territory in the city of Belo Horizonte, Brazil. The aim is to present the contributions of the Possible Gardens, this expanded category of garden understood as cosmopolitical worlds, to the thinking of contemporary cities based on ecological practices derived from urban daily life. In addition, it opens an understanding of the potential of gardens as a culturally relevant element, as an example and catalyst for environmental policies.

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.

Context

The dataset extracted from the website of the Brazilian Institute of Geography and Statistics (IBGE) contains all demographic, economic, geographic and human development information on Brazilian cities.

Content

There was no complete dataset to download all this information. So, I did a webscrapping that entered all the pages of each Brazilian cities and got all the information available. After that, I consolidated everything into a single file and now share with you to serve as research and studies of Brazil's performance on development, economics, and other topics.

This file contains 14 columns and 5571 rows (with headers):

• IBGECode - ID of the cities
• LocalCidade - Name of the cities
• LocalUF - State initials of the cities
• LocalEstado - Full name of city states
• RegiaoBrasil - IBGE's Region for each city
• Latitude - city ​​latitude
• Longitude - city ​​longitude
• Gentilico - name given to people born in the city
• PopEstimada_2018 - Population estimated in 2018
• PopCenso 2010 - Population in 2010
• IDHM - HDI of each city
• ReceitasRealizadas_2014 - Revenues realized by cities
• DespesasEmpenhadas_2014 - Expenditure incurred by cities
• Pib_2014 - GDP by cities

Acknowledgements

I thank my co-workers who helped me develop web scrapping and distribute the consolidated information to all of you.

Inspiration

Questions to be answered about this dataset:

1. What is the forecast of the GDP of each city in Brazil?
2. Can we group the cities by some specific division related to HDI or GDP?
3. What will be the total population of the Brazilian in the South of the Country in 2020?

And so on.

Abstract

As of July 2019, it is estimated that over 4,054,000 Venezuelans have left the country and approximately 168,357 have either requested asylum or temporary residency in Brazil, mainly in Roraima state and progressively in the city of Manaus in Amazonas state. Utilising an Area-Based Approach, REACH collected localised information on the situation of Venezuelan asylum seekers and migrants living in host communities and abrigos managed by humanitarian actors in city neighbourhoods across Boa Vista, Pacaraima and Manaus. The aim was to increase the understanding of humanitarian actors of the living conditions, primary needs, vulnerabilities and coping strategies of the asylum seekers and migrants. This study aims to provide a representative overview of the profiles of Venezuelan asylum seekers and migrants living in different geographic locations and shelter settings in Brazil, for the purpose of increasing the understanding of humanitarian actors as to the extent to which the living conditions, needs, and vulnerabilities of Venezuelan households vary between households living in abrigos and those living in host communities, across three cities that are relevant nodes in the Brazilian refugee response: Pacaraima, Boa Vista, and Manaus. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. Of all services, Venezuelans seem to face the most challenges regarding access to education; findings suggest that a lack of required documents and a limited local capacity are constraining the enrolment of Venezuelan children into local schools. These two factors were also the most likely to pose barriers to accessing social services and healthcare facilities. Difficulties in speaking the local language and long distances to facilities were found to further constrain households' access to services, albeit to a lesser extent.

Geographic coverage

Pacaraima, Boa Vista, and Manaus.

Analysis unit

Household

Universe

Households living in shelters.

Kind of data

Sample survey data [ssd]

Sampling procedure

A master list of households resident within each abrigo was requested from the relevant site manager. The requested dataset required the following fields:

• Tent location (Sector / Tent Number) to facilitate locating selected households;
• Household composition (number of household members, age, sex, focal point y/n);
• Individual and/or Group ID to facilitate secondary data verification to ProGress dataset (if necessary).

The dataset from each abrigo was merged into one master list. Each household within the master dataset was allocated with a consecutive number and households were selected using a random number generator. A total of 1119 households were interviewed.

Mode of data collection

Face-to-face interview