Note: This repository is a fork of the original geobr project.
GEOfinder is a computational package to download official spatial data sets
of Brazil. The package covers a wide range of spatial data sets,
available at various geographic scales and for various years with
harmonized attributes, projection and fixed topology (see detailed list
of available data sets below).
The package is currently available in R and Python.
# Install the development version from GitHub
utils::remove.packages('geobr')
remotes::install_github("yan-fab/GEOfinder", subdir = "r-package")obs. If you use Linux, you need to install a couple dependencies
before installing the libraries sf and geobr. More info
here.
pip install git+https://github.com/yan-fab/GEOfinder.git#subdirectory=python-packageWindows users:
conda create -n geo_env
conda activate geo_env
conda config --env --add channels conda-forge
conda config --env --set channel_priority strict
conda install python=3 geopandas
pip install git+https://github.com/yan-fab/GEOfinder.git#subdirectory=python-packageThe syntax of all geobr functions operate on the same logic so it
becomes intuitive to download any data set using a single line of code.
Like this:
library(geobr)
# Read specific municipality at a given year
mun <- read_municipality(code_muni = 1200179, year = 2022)
# Read all municipalities of given state at a given year
mun <- read_municipality(code_muni = "RJ", year = 2022) # or
mun <- read_municipality(code_muni = 33, year = 2022)
# Read all municipalities in the country at a given year
mun <- read_municipality(code_muni="all", year = 2022)from geobr import read_municipality
# Read specific municipality at a given year
mun = read_municipality(code_muni=1200179, year=2017)
# Read all municipalities of given state at a given year
mun = read_municipality(code_muni=33, year=2010) # or
mun = read_municipality(code_muni="RJ", year=2010)
# Read all municipalities in the country at a given year
mun = read_municipality(code_muni="all", year=2018)You can check all the data sets available with list_geobr()
| Function | Geographies available | Source | Years available |
|---|---|---|---|
| read_amazon | Brazil’s Legal Amazon | MMA | 2019, 2020, 2021, 2022, 2024 |
| read_biomes | Biomes | IBGE | 2006, 2019, 2025 |
| read_census_tract | Census tract (setor censitário) | IBGE | 2000, 2010, 2022 |
| read_conservation_units | Environmental Conservation Units | MMA | 202402, 202503 |
| read_country | Country | IBGE | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_disaster_risk_area | Disaster risk areas | CEMADEN and IBGE | 2010 |
| read_favelas | Favelas and urban communities | IBGE | 2022 |
| read_health_facilities | Health facilities | CNES, DataSUS | 201704, 201707, 201710, 201801, 201804, 201807, 201810, 201901, 201904, 201907, 201910, 202001, 202004, 202007, 202010, 202101, 202104, 202107, 202110, 202201, 202204, 202207, 202210, 202301, 202304, 202307, 202310, 202401, 202404, 202407, 202410, 202501, 202504, 202507, 202510, 202601 |
| read_health_region | Health regions and macro regions | DataSUS | 1991, 1994, 1997, 2001, 2005, 2013, 2023, 2024, 2025 |
| read_immediate_region | Immediate region | IBGE | 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_indigenous_land | Indigenous lands | FUNAI | 2016, 2017, 2018, 2019, 2022, 2024, 2025 |
| read_intermediate_region | Intermediate region | IBGE | 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_meso_region | Meso region | IBGE | 2000, 2001, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 |
| read_metro_area | Metropolitan areas | IBGE | 1970, 2001, 2002, 2003, 2005, 2008, 2009, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 |
| read_micro_region | Micro region | IBGE | 2000, 2001, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 |
| read_municipality | Municipality | IBGE | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2005, 2007, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_municipal_seat | Municipality seats (sedes municipais) | IBGE | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2010, 2022 |
| read_neighborhood | Neighborhood limits | IBGE | 2010, 2022 |
| read_polling_places | Voting places | TSE | 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024 |
| read_urban_concentrations | Urban concentration areas (concentrações urbanas) | IBGE | 2010 |
| read_pop_arrangements | Population arrangements (arranjos populacionais) | IBGE | 2010 |
| read_quilombola_lands | Quilombola lands officialy recognized | Incra | 202605 |
| read_comparable_areas | Historically comparable municipalities, aka áreas mínimas comparáveis (AMCs) | IBGE | temporarily suspended |
| read_region | Region | IBGE | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_schools | Schools | INEP | 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_semiarid | Semi Arid region | IBGE | 2005, 2017, 2021, 2022 |
| read_state | States | IBGE | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025 |
| read_statistical_grid | Statistical Grid (gridded population) | IBGE | 2010 |
| read_urban_area | Urban footprints | IBGE | 2005, 2015, 2019 |
| read_weighting_area | Census weighting area (área de ponderação) | IBGE | 2010 |
point_right: All datasets use geodetic reference system “SIRGAS2000”, CRS(4674).
| Function | Action |
|---|---|
list_geobr |
List all datasets available in the geobr package |
lookup_muni |
Look up municipality codes by their name, or the other way around |
remove_islands |
Removes distant oceanic islands from Brazil |
grid_state_correspondence_table |
Loads a correspondence table indicating what quadrants of IBGE’s statistical grid intersect with each state |
cep_to_state |
Determine the state of a given CEP postal code |
| … | … |
Note 1. Data sets and Functions marked with “dev” are only available in
the development version of geobr.
Note 2. Most data sets are available at scale 1:250,000 (see documentation for details).
Original shapefiles are created by official government institutions. The GEOfinder package is maintained by yanju. If you want to cite this package, you can cite it as:
- yanju (2026) GEOfinder: Download Official Spatial Data Sets of Brazil. v2.0.0 GitHub repository.
This repository now includes geoBR Explorer, an interactive web application that allows you to easily discover, build, and visualize spatial datasets right from your browser.
- Interactive Map Viewer (WebAssembly): View and explore generated GeoPackage (
.gpkg) files locally entirely within your browser using an embedded Leaflet map and SQL.js, without needing to upload your data to any external server. - Native geoBR Integration: Visually construct datasets from the official IPEA
geobrPython package, including geometries for the Country, Regions, States, Biomes, Legal Amazon, Health Regions, and Urban Areas. - External API Catalog: Search and extract spatial layers from external catalogs with over 15,000 combined datasets:
- GeoSampa (WFS): +400 spatial layers of the city of São Paulo.
- INDE Brasileiro: National spatial data infrastructure catalog.
- IBGE Mapas: Spatial data service from the Brazilian Institute of Geography and Statistics.
- Data Customization: The robust Python builder engine automatically projects the data correctly, supports CRS manipulation (e.g., EPSG:31983, EPSG:4674 to standard EPSG:4326), and saves in GeoPackage, GeoJSON, or Shapefile.
Run the application locally via the included batch script (Windows) or manually start the Python background servers:
# To start the API and Web interface
.\INICIAR.batThen navigate to http://localhost:8080 in your web browser.