geobr (version 1.9.1)

read_pop_arrangements: Download population arrangements in Brazil

Description

This function reads the official data on population arrangements (Arranjos Populacionais) of Brazil. Original data were generated by the Institute of Geography and Statistics (IBGE) For more information about the methodology, see details at https://www.ibge.gov.br/apps/arranjos_populacionais/2015/pdf/publicacao.pdf

Usage

read_pop_arrangements(
  year = 2015,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Value

An "sf" "data.frame" object

Arguments

year

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

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.

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_region(), read_schools(), read_semiarid(), read_state(), read_statistical_grid(), read_urban_area(), read_urban_concentrations(), read_weighting_area()

Examples

if (FALSE) { # identical(tolower(Sys.getenv("NOT_CRAN")), "true")
# Read urban footprint of Brazilian cities in an specific year
uc <- read_pop_arrangements(year=2015)
}