In 2024, approximately 11.9 million people lived in São Paulo, making it the largest municipality in Brazil and one of the most populous cities in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. Brazil's cities Brazil is home to two large metropolises: São Paulo with close to 11.9 million inhabitants, and Rio de Janeiro with around 6.7 million inhabitants. It also contains a number of smaller but well-known cities, such as Brasília, Salvador, Belo Horizonte, and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. While smaller than some of the other cities, Brasília was chosen to be the capital because of its relatively central location. The city is also well-known for its modernist architecture and utopian city plan, which is quite controversial - criticized by many and praised by others. Sports venues capitals A number of Brazil’s medium-sized and large cities were chosen as venues for the 2014 World Cup, and the 2015 Summer Olympics also took place in Rio de Janeiro. Both of these events required large sums of money to support infrastructure and enhance mobility within a number of different cities across the country. Billions of dollars were spent on the 2014 World Cup, which went primarily to stadium construction and renovation but also to a number of different mobility projects. Other short-term spending on infrastructure for the World Cup and the Rio Olympic Games was estimated at 50 billion U.S. dollars. While these events have poured a lot of money into urban infrastructure, a number of social and economic problems within the country remain unsolved.

Context

While working with geographical data in my internship, I had to analyze information regarding the cities of Brazil, using their respective latitudes and longitudes. The data was obtained from varied sources.

Content

Schema: 1. cd_ibge ("IBGE" code of the City) 2. nm_municipio (name of each City) 3. nm_uf (name of each State) 4. cd_uf (State name abbreviation) 5. bl_capital (boolean indicating if the city is the State capital or not) 6. regiao_uf (region of the State) 7. lat_municipio (latitude of the City) 8. long_municipio (longitude of the City) 9. lat_long_municipio (lat/long of the City) 10. lat_central_uf (latitude of the State - centralized) 11. long_central_uf (longitude of the State - centralized) 12. lat_long_central_uf (lat/long of the State - centralized)

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

Context

This dataset has information about brazilian cities.

Content

• city: the name of the city
• lat: the latitude of each city
• lng: the longitude of each city
• country: the country of each city (all Brazil in this case)
• iso2: country code (BR)
• admin: state of each city
• capital: wheter the city is a capital or not
• population: amount of inhabitants of each city
• population_proper: area contained within city limits

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

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.

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

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

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 data set contains actual sales data for a chain of Brazilian stores. I modified the names of products, customers, and employees to preserve their identity. I am making this data available so that they can help me get the most out of it, analysis such as:

• Sales forecast

• Customer segmentation

• Employee productivity

• Profitable products

• And everything else that can be extracted from it.

Columns description

Company Code - Affiliate code that sold Order Number - Unique code to identify the sale Employee - Employee who made the sale Product - Name of product sold Product Category - category the product belongs to Client - Name of the customer who made the purchase Client City - City name of the customer who made the purchase Sale Date Time - Date and time the sale was made Product Cost - Cost per unit sold Discount Amount - Total sale discount Amount - Item Quantity Total - Total item value Form of payment - Form of payment

The column values: - Client - Client City - Employee They were exchanged for fictitious names.

The category of the products was maintained, but translated into English, the name of the product consists of the name of the category to which it belongs concatenated with a random number. The rule does not apply to products in the Fuel category, for these, fictitious names were invented.

📄 Dataset Description

This dataset contains climate and air quality data collected from 10 Brazilian cities, covering a wide range of environmental variables. It aims to support data science projects related to weather analysis, air pollution monitoring, public health research, and environmental modeling.

Each row represents a single timestamped observation for a specific city.

The data was collected through web scraping from a public website using an Apache Airflow pipeline, which fetched the data hourly between May 25, 2025 and June 1, 2025.

🌍 Cities Included

• Fernandópolis
• Ribeirão Preto
• São Paulo
• Porto Alegre
• Rio de Janeiro
• Manaus
• Belo Horizonte
• Fortaleza
• Campo Grande
• Curitiba

🌡️ Columns

• city: Name of the Brazilian city where the data was collected.
• timestamp: Date and time (ISO format) of the observation.
• temperature: Ambient temperature in degrees Celsius (°C).
• wind: Wind speed in meters per second (m/s).
• humidity: Relative humidity as a percentage (%).
• dew_point: Dew point temperature in degrees Celsius (°C).
• pressure: Atmospheric pressure in hectopascals (hPa).
• uv_index: Ultraviolet radiation index (scale from 0 to 11+).

🧪 Air Quality Indicators

Each pollutant has two associated fields:

• [pollutant]_aqi: Air Quality Index value for the pollutant, following local environmental standards.
• [pollutant]_medida: Measured concentration of the pollutant (in µg/m³ or ppm, depending on the pollutant).

Pollutants included:

• O3_aqi, O3_medida: Ozone (O₃)
• CO_aqi, CO_medida: Carbon Monoxide (CO)
• NO2_aqi, NO2_medida: Nitrogen Dioxide (NO₂)
• PM10_aqi, PM10_medida: Particulate Matter ≤10 µm
• PM2_5_aqi, PM2_5_medida: Particulate Matter ≤2.5 µm
• SO2_aqi, SO2_medida: Sulfur Dioxide (SO₂)

Feel free to use this dataset for exploratory analysis, modeling, forecasting, or visualization projects related to air and climate conditions in urban Brazil.

This dataset contains messages published and replies received by government weather and climate authorities on the X (former Twitter) social media platforms. The data comprises government weather and climate authorities for the Brazilian cities of São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre, and Belém. Government weather and climate authorities are city hall departments or sectors responsible for informing and keeping the population updated about weather events. Publications made by the authority and replies published by citizens to these publications are observed. This data supports the study on the interaction dynamics between the climate authority and citizens over time. Data Structure Two files are available publications.csv and replies.csv. Each line in the publications' file (publications.csv) refers to an authority publication/tweet. For each publication, it is stored the public authority's unique Twitter identifier (AUTHORITY_ID), the tweet unique identifier (TWEET_ID), the Unix timestamp that indicates when it was published (TIMESTAMP), and the text of the publication (TEXT). Each line in the replies file (replies.csv) is a reply from a citizen to an authority. For each reply, it is stored the authority's unique Twitter identifier (AUTHORITY_ID), the unique identifier of the authority's tweet being replied to (TWEET_ID), the replier masked unique Twitter identifier (AUTHOR_ID), and the reply Unix timestamp (TIMESTAMP) that indicates when it was published. All data were collected through the X's application programming interface (API) provided to scientific researchers. Publications and replies were posted by users (authorities and citizens) with public visibility. Data Content The dataset covers 1-year observation period, starting on July 17, 2021, and ending on June 16, 2022. It contains a total of 10,229 publications and 5,471 replies. The observed authorities are as follows:

City Authority name X handle AUTHORITY_ID São Paulo Centro de Gerenciamento de Emergências Climáticas da Prefeitura de SP @cge_sp 268407434 Rio de Janeiro Sistema de Alerta localizado no Centro de Operações do Rio (COR) @alertario 87487749 Belo Horizonte Defesa Civil de Belo Horizonte @defesacivilbh 837731966 Porto Alegre Defesa Civil Porto Alegre @defesacivilpoa 1037420896473022466 Belém Defesa Civil de Belém @defesacivilbel 1346501728632500225

As weather and climate authorities are government bodies, the whole content of their publications is of public interest according to Brazilian law. Thus, the text messages in their publications on social media are in the public domain and are stored in this dataset. As the data structure describes, text messages of citizens' replies are not stored. According to the terms of use of the X platform, citizen text messages cannot be publicly stored outside the X platform. Such text messages are public on that platform, and, for reproductivity, they can be recollected using the platform web page or API informing the TWEET_ID stored in this dataset.

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.

The Brazilian National Institute of Meteorology (INMET, from the Portuguese "Instituto Nacional de Meteorologia'') is the Brazilian government agency responsible for monitoring, analysing and forecasting weather and climate. It provides meteorological warnings to be used by the local-level municipal authorities.

Data Content

INMET periodically publishes data on its website and provides them via XML RSS Feed. This dataset was collected from the RSS feeds mentioning the Brazilian cities of Belém located in the state of Pará, Belo Horizonte in Minas Gerais state, Porto Alegre in Rio Grande do Sul state, Rio de Janeiro in Rio de Janeiro state and São Paulo in São Paulo state from July/2021 to July/2022.

Data Structure

The description of columns collected from INMET warnings and stored in the warnings file (inmet-meteorological-warnings-1658070001.csv) is presented below. The warnings issued by INMET follow the Common Alerting Protocol (CAP). CAP provides an open, non-proprietary digital message format for all types of alerts and notifications [Standard, OASIS (2010). Common Alerting Protocol Version 1.2. Jul, 1, pp. 1-47. http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html ].

Columns:

CITY: Name of the city for which the warning was issued.

STATE: The Brazilian acronym for the state in which the city is located, for example, MG for Minas Gerais.

CITYCODE: Unique numeric code for the city for which the warning was issued.

IDENTIFIER: Unique identifier to INMET warning.

RESPONSETYPE: Reaction to the warning.

URGENCY: Urgency for taking action. For example, “Prepare”.

SEVERITY: Severity of the meteorological event. For example, “Future”

CERTAINTY: How likely is the event to happen? For example, “Observed” - Determined to have occurred or to be ongoing; “Likely” - (p > ~50%); “Possible” - Possible but not likely (p <= ~50%).

WARNING: Standardized type of warning. For example, "Aviso de Acumulado de Chuva", "Aviso de Tempestade", "Aviso de Declínio de Temperatura".

TIMESTAMPDATEONSE: Unix timestamp of the minimum time at which the event is expected to start.

TIMESTAMPDATEEXPIRES: Unix timestamp of the maximum at which the event is expected to occur or the warning expires.

COLORRISK: Event colour in hexadecimal following the INMET nomenclature, with yellow meaning potential danger, orange indicating danger, and red indicating great danger.

BASESOURCE: INMET RSS XML file from which the warning was extracted.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL

Features may have these attributes:

• shop
• addr:housenumber
• addr:city
• opening_hours
• name
• tourism
• rooms
• man_made
• amenity
• addr:full
• addr:street
• source
• beds

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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.

Context

OpenAddresses's goal is to connect the digital and physical worlds by sharing geographic coordinates, street names, house numbers and postal codes.

Content

This dataset contains one data file for each of these countries:

• Argentina - argentina.csv
• Brazil - brazil.csv
• Chile - chile.csv
• Columbia - columbia.csv
• Uraguay - uraguay.csv

Field descriptions:

• LON - Longitude
• LAT - Latitude
• NUMBER - Street number
• STREET - Street name
• UNIT - Unit or apartment number
• CITY - City name
• DISTRICT - ?
• REGION - ?
• POSTCODE - Postcode or zipcode
• ID - ?
• HASH - ?

Acknowledgements

Data collected around 2017-07-25 by OpenAddresses (http://openaddresses.io).

Address data is essential infrastructure. Street names, house numbers and postal codes, when combined with geographic coordinates, are the hub that connects digital to physical places.

Data licenses can be found in LICENSE.txt.

Data source information can be found at https://github.com/openaddresses/openaddresses/tree/9ea72b079aaff7d322349e4b812eb43eb94d6d93/sources

Inspiration

Use this dataset to create maps in conjunction with other datasets to map weather, crime, or plan your next canoeing trip.

This polygon shapefile contains the municipal boundaries for the state of Amapá, Brazil in 2001. 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.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.