read_landuse {aopdata}

Download land use and population data

Description

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/.

Usage

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

Arguments

city

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

year

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

geometry

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.

showProgress

Logical. Defaults to TRUE display progress bar.

Value

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

Data dictionary:

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)

Cities available

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

Examples

# 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)