geobr
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.
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
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 and 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:
: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 | IBGE |
read_region |
Region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | 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 | IBGE |
read_meso_region |
Meso region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | IBGE |
read_micro_region |
Micro region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | IBGE |
read_intermediate_region |
Intermediate region | 2017, 2019 | IBGE |
read_immediate_region |
Immediate region | 2017, 2019 | IBGE |
read_municipality |
Municipality | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019 | 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 | 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 | FUNAI |
read_semiarid |
Semi Arid region | 2005, 2017 | IBGE |
read_health_facilities |
Health facilities | 2015 | CNES, DataSUS |
read_health_region |
Health regions | 1991, 1994, 1997, 2001, 2005, 2013 | DataSUS |
read_neighborhood |
Neighborhood limits | 2010 | IBGE |
read_schools |
Schools | 2020 | INEP |
read_comparable_areas (dev) |
Historically comparable municipalities, aka Areas minimas comparaveis (AMCs) | 1872,1900,1911,1920,1933,1940,1950,1960,1970,1980,1991,2000,2010 | 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)