R/read_comparable_areas.R
read_comparable_areas.RdThis function downloads the shape file of minimum comparable area of
municipalities, known in Portuguese as 'Areas minimas comparaveis (AMCs)'.
The data is available for any combination of census years between 1872-2010.
These data sets are generated based on the Stata code originally developed by
Ehrl (2017) doi:10.1590/0101-416147182phe
, and translated into R by the
geobr team.
read_comparable_areas(
start_year = 1970,
end_year = 2010,
simplified = TRUE,
showProgress = TRUE,
cache = TRUE
)Numeric. Start year to the period in the YYYY format.
Defaults TO 1970.
Numeric. End year to the period in the YYYY format. Defaults
to 2010.
Logic FALSE or TRUE, indicating whether the function
should return the data set with 'original' spatial resolution or a data set
with 'simplified' geometry. Defaults to TRUE. For spatial analysis and
statistics users should set simplified = FALSE. Borders have been
simplified by removing vertices of borders using st_simplify{sf} preserving
topology with a dTolerance of 100.
Logical. Defaults to TRUE display progress bar.
Logical. Whether the function should read the data cached
locally, which is faster. Defaults to cache = TRUE. By default,
geobr stores data files in a temporary directory that exists only
within each R session. If cache = FALSE, the function will download
the data again and overwrite the local file.
An "sf" "data.frame" object
These data sets are generated based on the original Stata code developed by Philipp Ehrl. If you use these data, please cite:
Ehrl, P. (2017). Minimum comparable areas for the period 1872-2010: an aggregation of Brazilian municipalities. Estudos Econômicos (São Paulo), 47(1), 215-229. https://doi.org/10.1590/0101-416147182phe
Other area functions:
read_amazon(),
read_biomes(),
read_capitals(),
read_country(),
read_disaster_risk_area(),
read_health_facilities(),
read_health_region(),
read_immediate_region(),
read_indigenous_land(),
read_intermediate_region(),
read_meso_region(),
read_metro_area(),
read_micro_region(),
read_municipal_seat(),
read_municipality(),
read_neighborhood(),
read_pop_arrangements(),
read_region(),
read_schools(),
read_semiarid(),
read_state(),
read_statistical_grid(),
read_urban_area(),
read_urban_concentrations(),
read_weighting_area()
amc <- read_comparable_areas(start_year=1970, end_year=2010)