Perhaps the most commonly used datasets from Brazilian censuses are the microdata of individuals and households. Nonetheless, IBGE also makes available some extremely data on population and environmental characteristics aggregated at the census tract level. In this vignette, we show how to use the {censobr} package to easily access census tract-level data using the read_tracts() function.

At the moment, this function includes data from the censuses of 2000, 2010 and 2022.

Data structure

Aggregated data at the census tract level are split into different datasets, and some of them are scattered across several files. For the sake of convenience, we have gathered all of the files into different datasets:

"Basico" "Entorno" "Domicilio" "Pessoa" "Responsavel" "PessoaRenda" "DomicilioRenda" "ResponsavelRenda"

2000	2010	2022
Basico	Basico	Basico
Domicilio	Domicilio	Domicilio
Pessoa	Pessoa	Pessoas
Responsavel	Responsavel
Instrucao	Entorno	Entorno
Morador	ResponsavelRenda	ResponsavelRenda
	DomicilioRenda	Indigenas
	PessoaRenda	Quilombolas
		Obitos
		Preliminares

All of the data aggregated at census tracts are organized following the same logic.

In the cases when there are multiple files in the same dataset, we add a reference to the number of the file as a prefix to the variable name. To illustrate this, let’s have a look at the "Domicilio" dataset. This dataset is based on three separate tables: Domicilio01, Domicilio02 and Domicilio03. So the names of the columns in this dataset are organized as follows:

library(censobr)

dom <- read_tracts(
  year = 2022,
  dataset = 'Domicilio', 
  showProgress = FALSE
  )
#> ℹ Downloading data and storing it locally for future use.

names(dom)[c(30:33,119:121, 526:528)]
#>  [1] "domicilio01_V00001" "domicilio01_V00002" "domicilio01_V00003"
#>  [4] "domicilio01_V00004" "domicilio02_V00090" "domicilio02_V00091"
#>  [7] "domicilio02_V00092" "domicilio03_V00497" "domicilio03_V00498"
#> [10] "domicilio03_V00499"

Dictionary of variables

To check the meaning of each variable, users can run the data_dictionary(), which will open the file with the dictionary of variables in each dataset. The dictionary file is either an .html, a .pdf or an .xlsx file, depending on the data set and year.

data_dictionary(
  year = 2022, 
  dataset = 'tracts'
  )
#> NULL

Reproducible examples

Now let’s use a couple reproducible examples to illustrate how to work with census tract-level data. First, we need to load the libraries we’ll be using in this vignette.

library(arrow)
library(dplyr)
library(geobr)
library(ggplot2)

In these examples below, example we’ll use the city of Belo Horizonte for demonstration purposes. So we can start by downloading the the geometries of the census tracts in the area. First, we need to download the geometries of all census tracts in the state of Minas Gerais (MG), and then keep only the ones in the municipality of Belo Horizonte. We’ll also download the municipality borders of BH.

muni_bh <- geobr::read_municipality(
  code_muni = 'MG', 
  year = 2010, 
  showProgress = FALSE
  ) |>
  filter(name_muni == "Belo Horizonte")
#> Using year/date 2010

tracts_sf <- geobr::read_census_tract(
  code_tract = "MG",
  simplified = FALSE,
  year = 2010,
  showProgress = FALSE
  )
#> Using year/date 2010

tracts_sf <- filter(tracts_sf, name_muni == "Belo Horizonte")

ggplot() + 
  geom_sf(data=tracts_sf, fill = 'gray90', color='gray60') + 
  theme_void()

Example 1:

In this first example we are going to map the proportion of households with the presence of trees in their surroundings. To do this, we need to download the "Entorno" dataset and sum the variables domicilios_V05031 + domicilios_V05032 + domicilios_V05033.

# download data
tract_entorno <- censobr::read_tracts(
  year = 2022,
  dataset = "Entorno", 
  showProgress = FALSE
  )
#> ℹ Downloading data and storing it locally for future use.

# filter observations and calculate indicator
df_trees <- tract_entorno |>
  filter(code_tract %in% tracts_sf$code_tract) |>
  group_by(code_tract) |>
  mutate(total_households = domicilios_V05000,
         trees = domicilios_V05031 + domicilios_V05032+ domicilios_V05033,
         trees_prop = trees / total_households) |>
  select(code_tract, total_households, trees, trees_prop) |>
  collect()

head(df_trees)
#> # A tibble: 6 × 4
#> # Groups:   code_tract [6]
#>   code_tract      total_households trees trees_prop
#>   <chr>                      <dbl> <dbl>      <dbl>
#> 1 310620005620001              222   222      1    
#> 2 310620005620002              158   158      1    
#> 3 310620005620004              294   294      1    
#> 4 310620005620005              131   131      1    
#> 5 310620005620006              142   138      0.972
#> 6 310620005620007              218   218      1

Now we can merge the spatial data with our indicator and see how the presence of trees in the surroundings of households varies spatially.

bh_tracts <- left_join(tracts_sf, df_trees, by = 'code_tract')

ggplot() +
  geom_sf(data = bh_tracts, aes(fill = trees_prop), color=NA) +
  geom_sf(data = muni_bh, color='gray10', fill=NA) +
  labs(subtitle = 'Share of households with trees in their surroundings.\nBelo Horizonte, 2010') +
  scale_fill_distiller(palette = "Greens", direction = 1, 
                       name='Share of\nhouseholds', 
                       na.value = "gray90",
                       labels = scales::percent) +
  theme_void()

Example 2: Spatial distribution of income

In this second example, we are going to create a map of the spatial distribution of average income per capita. Unfortunately, this information was not collected in the 2022 census at the census tract level, so this example is based on the 2010 data.

In the 2010 census, we can find the information on the the total number of residents in each census tract in the "Basico" dataset, variable "V002". Meanwhile, the information on income can be found in the "DomicilioRenda" dataset, variable "V003".

Using the code below, we download the data and calculate the income per capita of all census tracts in Brazil.

# download data
tract_basico <- censobr::read_tracts(
  year = 2010,
  dataset = "Basico", 
  showProgress = FALSE
  )
#> ℹ Downloading data and storing it locally for future use.

tract_income <- censobr::read_tracts(
  year = 2010,
  dataset = "DomicilioRenda", 
  showProgress = FALSE
  )
#> ℹ Downloading data and storing it locally for future use.

# select columns
tract_basico <- tract_basico |> select('code_tract','V002')
tract_income <- tract_income |> select('code_tract','V003')

# merge
tracts_df <- left_join(tract_basico, tract_income) |> collect()

# calculate income per capita
tracts_df <- tracts_df |> mutate(income_pc = V003 / V002)
head(tracts_df)
#> # A tibble: 6 × 4
#>   code_tract       V002   V003 income_pc
#>   <chr>           <dbl>  <dbl>     <dbl>
#> 1 110001505000001   956 522231      546.
#> 2 110001505000002   859 406191      473.
#> 3 110001505000003   661 241587      365.
#> 4 110001505000004   266  88502      333.
#> 5 110001505000005  1206 262860      218.
#> 6 110001505000006   816 262968      322.

Finally, we can merge the spatial data with our per capita income estimates and map the results.

bh_tracts <- left_join(tracts_sf, tracts_df, by = 'code_tract')

ggplot() +
  geom_sf(data = bh_tracts, aes(fill = income_pc), color=NA) +
  geom_sf(data = muni_bh, color='gray10', fill=NA) +
  labs(subtitle = 'Avgerage income per capita.\nBelo Horizonte, 2010') +
  scale_fill_viridis_c(name = "Income per\ncapita (R$)",
                       labels = scales::number_format(),
                       option = 'cividis',
                       breaks = c(0, 500, 1e3, 5e3, 1e4, 2e4),
                       trans = "pseudo_log", na.value = "gray90") +
  theme_void()

Census tract-level data

2025-07-06

Data structure

Dictionary of variables

Reproducible examples

Example 1:

Example 2: Spatial distribution of income