Download data on the spatial distribution of population, jobs, schools, health care and social assitance facilities at a fine spatial resolution for the cities included in the AOP project. See the documentation 'Details' for the data dictionary. The data set reports information for each heaxgon in a H3 spatial grid at resolution 9, with a side of 174 meters and an area of 0.10 km2. More information about H3 at

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



Character. A city name or three-letter abbreviation. If city="all", the function returns data for all cities.


Numeric. A year number in YYYY format. Defaults to 2019.


Logical. If FALSE (the default), returns a regular data.table of aop data. If TRUE, returns 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:

temporalyearYear of reference
geographicid_hexUnique id of hexagonal cell
geographicabbrev_muniAbbreviation of city name (3 letters)
geographicname_muniCity name
geographiccode_muni7-digit code of each city
sociodemographicP001Total number of residents
sociodemographicP002Number of white residents
sociodemographicP003Number of black residents
sociodemographicP004Number of indigenous residents
sociodemographicP005Number of asian-descendents residents
sociodemographicP006Number of men
sociodemographicP007Number of women
sociodemographicP010Number of people between 0 and 5 years old
sociodemographicP011Number of people between 6 and 14 years old
sociodemographicP012Number of people between 15 and 18 years old
sociodemographicP013Number of people between 19 and 24 years old
sociodemographicP014Number of people between 25 and 39 years old
sociodemographicP015Number of people between 40 and 69 years old
sociodemographicP016Number of people with 70 years old or more
sociodemographicR001Average household income per capitaR$ (Brazilian Reais), values in 2010
sociodemographicR002Income quintile group1 (poorest), 2, 3, 4, 5 (richest)
sociodemographicR003Income decile group1 (poorest), 2, 3, 4, 5, 6, 7, 8, 9, 10 (richest)
land useT001Total number of formal jobs
land useT002Number of formal jobs with primary education
land useT003Number of formal jobs with secondary education
land useT004Number of formal jobs with tertiary education
land useE001Total number of public schools
land useE002Number of public schools - early childhood
land useE003Number of public schools - elementary schools
land useE004Number of public schools - high schools
land useM001Total number of school enrollments
land useM002Number of school enrollments - early childhood
land useM003Number of school enrollments - elementary schools
land useM004Number of school enrollments - high schools
land useS001Total number of healthcare facilities
land useS002Number of healthcare facilities - low complexity
land useS003Number of healthcare facilities - medium complexity
land useS004Number of healthcare facilities - high complexity
land useC001Total number of Social Assistance Reference Centers (CRAS)

Cities available

City nameThree-letter abbreviation
Belo Horizontebho
Campo Grandecgr
Duque de Caxiasduq
Porto Alegrepoa
Rio de Janeirorio
Sao Goncalosgo
Sao Luisslz
Sao Paulospo


# 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)