The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Analyzing inequality in access to opportunities

2026-01-07

Social and racial inequalities in access to opportunities

# load libraries
library(aopdata)
library(sf)
library(ggplot2)
library(data.table)
library(units)

Download accessibility data

df <- aopdata::read_access(
  city = 'Curitiba',
  mode = 'public_transport',
  peak = TRUE,
  year = 2019,
  showProgress = FALSE
  )

Inequality in access to job opportunities by income decile

ggplot() +
  geom_boxplot(data=subset(df, !is.na(R003)),
               aes(x = factor(R003), y=CMATT60/1000, color=factor(R003))) +
  scale_color_brewer(palette = 'RdBu') +
  labs(title='Distribution of the number of jobs accessible', color="Income\ndecile",
       subtitle='by public transport in less than 60 min. by income decile',
       x='Income decile', y="N. of jobs accessible\n(thousands)") +
  scale_x_discrete(labels=c("D1 Poorest", paste0('D', 2:9), "D10 Wealthiest")) +
  theme_minimal()

Palma ratio

# average access of the wealthiest 10%
avg_access_10p_wealthiest <- df[ R003==10, weighted.mean(x=CMATT60, w=P001, na.rm=T)]

# average access of the poorest 40%
avg_access_40p_poorest <- df[ R003<=4, weighted.mean(x=CMATT60, w=P001, na.rm=T)]

# Palma ratio
palma_ratio <- avg_access_10p_wealthiest / avg_access_40p_poorest                
palma_ratio 
#> [1] 2.609378

This means that the 10% wealthiest population could access by public transport on average 2.6 times more job opportunites than the 40% poorest people in less than 60 min.

Inequality in travel time to closes hospital

# replace Inf travel time with 120
df[, TMISA := fifelse(TMISA==Inf, 120, TMISA)]

# calculate average travel time by race
df[, .(average = weighted.mean(x=TMISA, w=P001, na.rm=T),
       white   = weighted.mean(x=TMISA, w=P002, na.rm=T),
       black   = weighted.mean(x=TMISA, w=P003, na.rm=T))]
#>    average    white    black
#>      <num>    <num>    <num>
#> 1:  37.124 35.75722 43.29197

# calculate average travel time by income
temp <- df[, .(average = weighted.mean(x=TMISA, w=P001, na.rm=T)), by=R003]
temp <- na.omit(temp)

ggplot() + 
  geom_point(data=temp, aes(y=average, x=factor(R003))) +
  labs(x='Income decile', y='Avg. travel time to\nclosest hospital') +
  theme_minimal()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.