read_landuse {aopdata}R Documentation

Download land use and population data


Download data on the spatial distribution of population, schools and healthcare facilities at a fine spatial resolution for cities included in the study. The data comes aggregated on a hexagonal grid based on the global H3 index at resolution 8, with a size of 357 meters (short diagonal) and an area of 0.74 km2. More information about H3 at

See documentation 'Details' for the data dictionary.


read_landuse(city = "bel", year = 2019, geometry = FALSE, showProgress = TRUE)



Character. A city name or three-letter abbreviation. If city="all", results for all cities are loaded.


Numeric. A year number in YYYY format. Default set to 2019, the only year currently available.


Logical. If FALSE (the default), returns a regular data.table of aop data. If TRUE, returns a an sf data.frame with simple feature geometry of spatial hexagonal grid H3. See details in read_grid.


Logical. Defaults to TRUE display progress bar


A data.frame object or an sf data.frame object

Data dictionary:

Data type column Description Value
geographic abbrev_muni Abbreviation of city name (3 letters)
geographic name_muni City name
geographic code_muni 7-digit code of each city
geographic id_hex Unique id of hexagonal cell
sociodemographic P001 Total number of residents
sociodemographic P002 Number of white residents
sociodemographic P003 Number of black residents
sociodemographic P004 Number of indiginous residents
sociodemographic P005 Number of asian-descendents residents
sociodemographic R001 Average household income per capita R$ (Brazilian Reais), values in 2010
sociodemographic R002 Income quintile group 1 (poorest), 2, 3, 4, 5 (richest)
sociodemographic R003 Income decile group 1 (poorest), 2, 3, 4, 5, 6, 7, 8, 9, 10 (richest)
land use T001 Total number of formal jobs
land use T002 Total number of formal jobs with primary education
land use T003 Number of formal jobs with secundary education
land use T004 Number of formal jobs with tertiary education
land use E001 Total number of public schools
land use E002 Number of public schools - early childhood
land use E003 Number of public schools - elementary schools
land use E004 Number of public schools - high schools
land use S001 Total number of healthcare facilities
land use S002 Number of healthcare facilities - low complexity
land use S003 Number of healthcare facilities - medium complexity
land use S004 Number of healthcare facilities - high complexity


# a single city
bho <- read_landuse(city = 'Belo Horizonte', year = 2019, showProgress = FALSE)
bho <- read_landuse(city = 'bho', year = 2019, showProgress = FALSE)

# all cities
all <- read_landuse(city = 'all', year = 2019)

[Package aopdata version 0.2.2 Index]