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 https://h3geo.org/docs/core-library/restable/.
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_type | column | description | values |
| temporal | year | Year of reference | |
| geographic | id_hex | Unique id of hexagonal cell | |
| geographic | abbrev_muni | Abbreviation of city name (3 letters) | |
| geographic | name_muni | City name | |
| geographic | code_muni | 7-digit code of each city | |
| sociodemographic | P001 | Total number of residents | |
| sociodemographic | P002 | Number of white residents | |
| sociodemographic | P003 | Number of black residents | |
| sociodemographic | P004 | Number of indigenous residents | |
| sociodemographic | P005 | Number of asian-descendents residents | |
| sociodemographic | P006 | Number of men | |
| sociodemographic | P007 | Number of women | |
| sociodemographic | P010 | Number of people between 0 and 5 years old | |
| sociodemographic | P011 | Number of people between 6 and 14 years old | |
| sociodemographic | P012 | Number of people between 15 and 18 years old | |
| sociodemographic | P013 | Number of people between 19 and 24 years old | |
| sociodemographic | P014 | Number of people between 25 and 39 years old | |
| sociodemographic | P015 | Number of people between 40 and 69 years old | |
| sociodemographic | P016 | Number of people with 70 years old or more | |
| 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 | Number of formal jobs with primary education | |
| land use | T003 | Number of formal jobs with secondary 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 | M001 | Total number of school enrollments | |
| land use | M002 | Number of school enrollments - early childhood | |
| land use | M003 | Number of school enrollments - elementary schools | |
| land use | M004 | Number of school enrollments - 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 | |
| land use | C001 | Total number of Social Assistance Reference Centers (CRAS) |
| City name | Three-letter abbreviation |
| Belem | bel |
| Belo Horizonte | bho |
| Brasilia | bsb |
| Campinas | cam |
| Campo Grande | cgr |
| Curitiba | cur |
| Duque de Caxias | duq |
| Fortaleza | for |
| Goiania | goi |
| Guarulhos | gua |
| Maceio | mac |
| Manaus | man |
| Natal | nat |
| Porto Alegre | poa |
| Recife | rec |
| Rio de Janeiro | rio |
| Salvador | sal |
| Sao Goncalo | sgo |
| Sao Luis | slz |
| Sao Paulo | spo |
# 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)