geobr 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.
| R | Python | Repo |
|---|---|---|
  
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Installation R
# From CRAN
install.packages("geobr")
# or use the development version with latest features
utils::remove.packages('geobr')
remotes::install_github("ipeaGIT/geobr", 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.
Basic Usage
The 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:
R, reading the data as an sf object
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)More examples in the intro Vignette
Python, reading the data as a geopandas object
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)More examples here
Available datasets:
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).
Other support functions:
| 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).
Contributing to geobr
If you would like to contribute to geobr and add new functions or data sets, please check this guide to propose your contribution.
Related projects
As of today, there there are no other R or Python computational packages similar to geobr. The geobr package makes different contributions to the community, including for example:
- Access to a wider range of official spatial data sets, such as states and municipalities, census tracts, urbanized areas, etc
- A consistent syntax structure across all functions, making the package very easy and intuitive to use
- Access to spatial data sets with updated geometries for various years
- Harmonized attributes and geographic projections across geographies and years
- Option to download geometries with simplified borders for fast rendering
- Option to download geometries as geoarrow objects out of memory
- Stable version published on CRAN for R users, and on PyPI for Python users
Similar packages for other countries/continents
- Africa: afrimapr
- Argentina: geoAr
- Brazil: geobr
- Canada: cancensus
- Chile: chilemapas
- Czech Republic: RCzechia
- Finland: geofi
- Kazakhstan: (geokz)[https://github.com/arodionoff/geokz]
- Peru: mapsPERU
- Spain: mapSpain
- UK: geographr
- Uruguay: geouy
- USA: tigris
- Global (political administrative boundaries): rgeoboundaries
Credits 
Original shapefiles are created by official government institutions. The geobr package is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil. If you want to cite this package, you can cite it as:
- Pereira, R.H.M.; Barbosa, R.J.; et. all (2026) geobr: Download Official Spatial Data Sets of Brazil. v2.0.0 GitHub repository - https://github.com/ipeaGIT/geobr.