geobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology (see detailed list of available data sets below).

The package is currently available in R and Python.

R Python Repo

## Installation R

# From CRAN
install.packages("geobr")
library(geobr)

# or use the development version with latest features
utils::remove.packages('geobr')
devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
library(geobr)

obs. If you use Linux, you need to install a couple dependencies before installing the libraries sf and geobr. More info here.

## Installation Python

pip install geobr

Windows 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 geobr

# 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

# Read all municipalities of given state at a given year
mun <- read_municipality(code_muni=33, year=2010) # or

# Read all municipalities in the country at a given year
mun <- read_municipality(code_muni="all", year=2018)

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

# Read all municipalities of given state at a given year
mun = read_municipality(code_muni=33, year=2010) # or

# Read all municipalities in the country at a given year
mun = read_municipality(code_muni="all", year=2018)

More examples here

# Available datasets:

point_right: All datasets use geodetic reference system “SIRGAS2000”, CRS(4674).
Function Geographies available Years available Source
read_country Country 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_region Region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_state States 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_meso_region Meso region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_micro_region Micro region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_intermediate_region Intermediate region 2017, 2019, 2020 IBGE
read_immediate_region Immediate region 2017, 2019, 2020 IBGE
read_municipality Municipality 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2005, 2007, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_municipal_seat Municipality seats (sedes municipais) 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2010 IBGE
read_weighting_area Census weighting area (área de ponderação) 2010 IBGE
read_census_tract Census tract (setor censitário) 2000, 2010, 2017, 2019, 2020 IBGE
read_statistical_grid Statistical Grid of 200 x 200 meters 2010 IBGE
read_metro_area Metropolitan areas 1970, 2001, 2002, 2003, 2005, 2010, 2013, 2014, 2015, 2016, 2017, 2018 IBGE
read_urban_area Urban footprints 2005, 2015 IBGE
read_amazon Brazil’s Legal Amazon 2012 MMA
read_biomes Biomes 2004, 2019 IBGE
read_conservation_units Environmental Conservation Units 201909 MMA
read_disaster_risk_area Disaster risk areas 2010 CEMADEN and IBGE
read_indigenous_land Indigenous lands 201907, 202103 FUNAI
read_semiarid Semi Arid region 2005, 2017 IBGE
read_health_facilities Health facilities 2015 CNES, DataSUS
read_health_region Health regions and macro regions 1991, 1994, 1997, 2001, 2005, 2013 DataSUS
read_neighborhood Neighborhood limits 2010 IBGE
read_schools Schools 2020 INEP
read_comparable_areas Historically comparable municipalities, aka Areas minimas comparaveis (AMCs) 1872,1900,1911,1920,1933,1940,1950,1960,1970,1980,1991,2000,2010 IBGE
read_urban_concentrations Urban concentration areas (concentrações urbanas) 2015 IBGE
read_pop_arrangements Population arrangements (arranjos populacioanis) 2015 IBGE

## Other 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
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).

## Coming soon:

Geography Years available Source
read_census_tract 2007 IBGE
Longitudinal Database* of micro regions various years IBGE
Longitudinal Database* of Census tracts various years IBGE

’*’ Longitudinal Database refers to áreas mínimas comparáveis (AMCs)

# 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.

As of today, there is another R package with similar functionalities: simplefeaturesbr. The geobr package has a few advantages when compared to simplefeaturesbr, including for example: - A same syntax structure across all functions, making the package very easy and intuitive to use - Access to a wider range of official spatial data sets, such as states and municipalities, but also macro-, meso- and micro-regions, weighting areas, census tracts, urbanized areas, etc - Access to shapefiles 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 - Stable version published on CRAN for R users, and on PyPI for Python users

# 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.; Gonçalves, C.N.; et. all (2019) geobr: Loads Shapefiles of Official Spatial Data Sets of Brazil. GitHub repository - https://github.com/ipeaGIT/geobr.