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Download spatial data of urbanized areas in Brazil

Source: R/read_urban_area.R
read_urban_area.Rd

This function reads the official data on the urban footprint of Brazilian cities in the years 2005 and 2015. Original data were generated by the Institute of Geography and Statistics (IBGE) For more information about the methodology, see details at https://biblioteca.ibge.gov.br/visualizacao/livros/liv100639.pdf

Usage

read_urban_area(
  year = 2015,
  code_state = "all",
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2015.

code_state

The two-digit code of a state or a two-letter uppercase abbreviation (e.g. 33 or "RJ"). If code_state="all" (the default), the function downloads all states.

simplified

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.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

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.

Value

An "sf" "data.frame" object

See also

Other area functions: read_amazon(), read_biomes(), read_capitals(), read_comparable_areas(), 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_concentrations(), read_weighting_area()

Examples

# Read urban footprint of Brazilian cities in an specific year
d <- read_urban_area(year=2005)
#> Using year/date 2005