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.

Data used in my analysis of COVID-19 underreporting in Brazil. It includes 2019 brazilian population estimates by state, provided by IBGE, and a rds file with Brazilian map also by state.

Abstract Household surveys are one of the primary methodologies used in population-based studies. This narrative review of the literature aims to gather and describe the leading national and international household surveys of relevance. In Brazil, the historical role played by the Brazilian Institute of Geography and Statistics (IBGE) in conducting the most relevant research in the production of social data stands out. The Medical-Health Care Survey (AMS) and the National Household Sample Survey (PNAD), with the serial publication of Health Supplements, are the country’s primary sources of health information. In 2013, in partnership with the Ministry of Health, IBGE launched the National Health Survey (PNS), the most significant household health survey ever conducted in Brazil. The PNS-2019 received a major thematic and sampling expansion and, for the first time, applied the Primary Care Assessment Tool to assess PHC services in all 27 Brazilian states.

Introduction

This dataset contains information related to Brazilian states, like names, abbreviations, population size, latitude, longitude, capitals, area, GDP, HDI and much more. This data was compiled extracting several datasets from IBGE.

Summary : Fuel demand is shown to be influenced by fuel prices, people's income and motorization rates. We explore the effects of electric vehicle's rates in gasoline demand using this panel dataset.

Files : dataset.csv - Panel dimensions are the Brazilian state ( i ) and year ( t ). The other columns are: gasoline sales per capita (ln_Sg_pc), prices of gasoline (ln_Pg) and ethanol (ln_Pe) and their lags, motorization rates of combustion vehicles (ln_Mi_c) and electric vehicles (ln_Mi_e) and GDP per capita (ln_gdp_pc). All variables are all under the natural log function, since we use this to calculate demand elasticities in a regression model.

adjacency.csv - The adjacency matrix used in interaction with electric vehicles' motorization rates to calculate spatial effects. At first, it follows a binary adjacency formula: for each pair of states i and j, the cell (i, j) is 0 if the states are not adjacent and 1 if they are. Then, each row is normalized to have sum equal to one.

regression.do - Series of Stata commands used to estimate the regression models of our study. dataset.csv must be imported to work, see comment section.

dataset_predictions.xlsx - Based on the estimations from Stata, we use this excel file to make average predictions by year and by state. Also, by including years beyond the last panel sample, we also forecast the model into the future and evaluate the effects of different policies that influence gasoline prices (taxation) and EV motorization rates (electrification). This file is primarily used to create images, but can be used to further understand how the forecasting scenarios are set up.

Sources: Fuel prices and sales: ANP (https://www.gov.br/anp/en/access-information/what-is-anp/what-is-anp) State population, GDP and vehicle fleet: IBGE (https://www.ibge.gov.br/en/home-eng.html?lang=en-GB) State EV fleet: Anfavea (https://anfavea.com.br/en/site/anuarios/)

Abstract There is an estimated deficit of six million nurses worldwide. Despite its importance for health systems, sociodemographic studies are scarce due to the absence of systematized data specific to nurses. The objective of this study was to compare the population coverage of nurses in Brazil based on sources from the Brazilian Institute of Geography and Statistics (IBGE), in the years 2010 and 2015, and the Federal Nursing Council (Cofen), in the years 2013 and 2019. In both sources, there was an average increase of 164 thousand nurses throughout Brazil. The growth rate for the period of the IBGE surveys (15.7% per year) was triple that recorded in the Cofen data (5.3% per year). Coverage in the states of Brazil remains below the international recommendation (40 nurses per 10 thousand inhabitants), with greater deficits in the states of the North and Northeast regions. The comparisons in this study reiterate the importance of the availability of standardized and systematized data for Nursing in Brazil. Accurate health indicators subsidize public policies to reduce health inequities, with emphasis on the coverage of nurses, especially in regions with high socioeconomic vulnerabilities.

Net total Deaths per state Brazil Jan/2014 to Aug/2021 Two files with all net deaths (no traumatic) and general mortality rate in Brazil per state All mortality rates was per 100000 and was computed with population of year (2014 to 2021) Source: IBGE, SIM/MS SUS and Registro Civil Arpen Portal from Brazil All geographic variables was a geojson and flag link file Provenance info was set for all data

How did Brazil become a global epicenter of the outbreak? After seeming to ease, is the virus making a comeback?

A world leader in infections and deaths.

Latin America became an epicenter of the coronavirus pandemic in May, driven by Brazil’s ballooning caseload. Ten months after its first known case, Brazil has had more than 7.9 million cases and over 200,000 deaths.

In early June, Brazil began averaging about 1,000 deaths per day from Covid-19, joining the United States — and later India — as the countries with the world’s largest death tolls.

This dataset contains information about COVID-19 in Brazil extracted on the date 16/06/2021. It is the most updated dataset available about Covid in Brazil

Features:

🔍 date: date that the data was collected. format YYYY-MM-DD.
🔍 state: Abbreviation for States. Example: SP
🔍 city: Name of the city (if the value is NaN, they are referring to the State, not the city)
🔍 place_type: Can be City or State
🔍 order_for_place: Number that identifies the registering order for this location. The line that refers to the first log is going to be shown as 1, and the following information will start the count as an index.
🔍 is_last: Show if the line was the last update from that place, can be True or False
🔍 city_ibge_code: IBGE Code from the location
🔍confirmed: Number of confirmed cases.
🔍deaths: Number of deaths.
🔍estimated_population: Estimated population for this city/state in 2020. Data from IBGE
🔍estimated_population_2019: Estimated population for this city/state in 2019. Data from IBGE.
🔍confirmed_per_100k_inhabitants: Number of confirmed cases per 100.000 habitants (based on estimated_population).
🔍death_rate: Death rate (deaths / confirmed cases).

Acknowledgements

This dataset was downloaded from the URL bello. Thanks, Brasil.IO! Their main goal is to make all Brazilian data available to the public DATASET URL: https://brasil.io/dataset/covid19/files/ Cities map file https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/Brasil/BR/

Similar Datasets

COVID-19 - https://www.kaggle.com/rafaelherrero/covid19-brazil-full-cases-17062021 COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019 Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

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.

Comparison between Botucatu’s patient data and IBGE rural worker population data.

Dataset Name: Dengue Cases in Brazil, 2012-2021

File format: Comma Seperated Values (CSV)

Dataset Files and Decriptions: - Brazil_Dengue_Model_Data_w_pop.csv - Dengue Data for Brazil as a whole country, 2012 - 2021 - State_Dengue_Model_Data_w_pop.csv - Dengue Data for Individual States / Federative Units in Brazil, 2012 - 2021

Dataset Sources: - Records of Dengue Cases in Brazil: Brazilian Government’s Sistema de Informação de Agravos de Notificação (SINAN) -URL Link: https://data.mendeley.com/datasets/2d3kr8zynf/4 - Brazil State Codes / Federative Unit Codes: Brazilian Government’s Instituto Brasileiro de Geografia e Estatística (IBGE) -URL Link: https://github.com/datasets-br/state-codes - Evironmental Data in Brazil (Temperature and Percipitation): World Bank Climate Knowledge -URL Link: https://climateknowledgeportal.worldbank.org/country/brazil/climate-data-historical - Brazil Population Data: Brazilian Government’s Instituto Brasileiro de Geografia e Estatística (IBGE) -URL Link: https://www.ibge.gov.br/en/statistics/social/population/18448-estimates-of-resident-population-for-municipalities-and-federation-units.html?edicao=28688&t=conceitos-e-metodos

Dataset Managers: - Jimmy Zhang | jz876@drexel.edu - Jonathan Watkins | jfw68@drexel.edu - Jascha Brettschneider | jmb598@drexel.edu

Column Headers: Year - a Year Between 2012 and 2021 State - Brazil or a Brazillian State / Federative Unit Mean_Tmp - Mean Temperature in Degrees Celsius Min_Tmp - Min Temperature in Degrees Celsius Max_Tmp - Max Temperature in Degrees Celsius Percipitation - Annual Percipitation Given in Millimeters Change_Tmp - Max_Tmp minus Min_Temp in Degrees Celsius State_ID - Abbreviation for State / Federative Unit Cases - Number of Recorded Dengue Cases Region - Directional Location Relative to Brazil's Center. Possible Values: North (N), Northeast (NE), Center-West (CO), Southeast (SE), South (S) State_Area(km2) - Area of State / Federative Unit Given in Squared Kilometers Population - Estimation of Total Population

ABSTRACT The objective of this study was to know the prevalence of full motor difficulty (MD) (walking or climbing stairs) and according to degrees (mild, moderate, severe) in the Brazilian states and in the country; present the federal expenditures on prostheses, orthotics and materials (OPM) related to such difficulty; and verify the correlation between the prevalence of disabilities and public expenditures on OPM. Population data was used from every major city in Brazil, obtained from the IBGE website, and OPM expenditures related to MD, extracted from the DATASUS website in 2010. Data was analyzed through the prevalence of MD and OPM expenses related to MD. We used the Stata 11 software for the implementation of the Spearman correlation test with a significance level of 5%. The prevalence of MD in Brazil in the year of 2010 was 6.91%; ranging from 8.63% (state of Alagoas) to 5.28% (state of Tocantins). The expenditures on OPM varied according to the state, and these expenditures were proportional to the prevalence of MD in the cities of the states of Acre and Piauí (orthotics); Pernambuco (prostheses), and Acre and Maranhão (equipment). The correlation between the amount spent and the prevalence of MD was inverse in the cities of the states of Espírito Santo, Minas Gerais, Paraná, Rio Grande do Sul, Santa Catarina and São Paulo (orthotics); Espírito Santo, Minas Gerais, Paraná, Rio Grande do Sul, Santa Catarina and São Paulo (prostheses); and Espírito Santo, Minas Gerais, Rio Grande do Sul and São Paulo (equipment).

Context

In order to produce cancer estimates in Brazil, the governmet, more specificly the National Cancer Intitute (INCA), has systematic centers for collection of data. They are known as RCBP (Cancer Registers with Populational Basis). This data is in accordance with regional laws and can be required by anyone.

Here I translated the variables in order to help in any analysis, but most of the values are not translated due to lazyness. However almost every term is translatable using google or part of a international code system (CID-10 -- classification of diseases -- or CID-O3 -- classification of cancers having in mind topography and morphology). More about the terms can be seen here (unfortunentely this document is in portuguese): www.inca.gov.br/publicacoes/manuais/manual-de-rotinas-e-procedimentos-para-registros-de-cancer-de-base-populacional

Moreover I added estimated populational data of almost all cities in Brazil. This data is produced by IBGE and was organized bt Ricardo Dahis (email: rdahis@basedosdados.org | github_user: rdahis | website: www.ricardodahis.com | ckan_user: rdahis) and can be dowloaded again here https://basedosdados.org/dataset/br-ibge-populacao

Content

This data is quite organized, however it has some flaws: 1) RCBP were added throughout the time 2) People do not always are treated in their state, so ratios can be implicated by it 3) It seems that there is a lack of data from 2013-2019

Even though, this is the best dataset possible in terms of what is happening in cancer in Brazil!

Acknowledgements

This dataset was entirely produced by INCA and I only translated some terms and replaced strings that meant NA for NA.

What should you do with it?

There are some questions that I believe that can be answerd

1) Which cancers are more incident in which population/sub-populations ? 2) Which cancers are had their survival rate enhanced? 3) Do people treat their cancers in their state or they go to other states? is there any trends related to that? 4) Do some centers treat their patients better than others? (is their big differences in outcome depening on where the person was diagnosed) 5) How badly do people fill these forms? (How much NA their is? How much unspecific? Which variables are simply unusable?)