Awesome

accessibility

accessibility offers a set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate accessibility levels and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures).

Installation

Stable version:

install.packages("accessibility")

Development version:

# install.packages("remotes")
remotes::install_github("ipeaGIT/accessibility")

Usage

This section aims to present a very brief overview of some of the packages’ features. Fore more details please read the vignettes:

To calculate accessibility levels, one simply needs a pre-calculated travel matrix and some land use data. Below we showcase some of the available functions:

library(accessibility)

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

cum_cutoff <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  opportunity = "jobs",
  travel_cost = "travel_time",
  cutoff = 30
)
head(cum_cutoff)
#>                 id  jobs
#> 1: 89a881a5a2bffff 14561
#> 2: 89a881a5a2fffff 29452
#> 3: 89a881a5a67ffff 16647
#> 4: 89a881a5a6bffff 10700
#> 5: 89a881a5a6fffff  6669
#> 6: 89a881a5b03ffff 37029

grav <- gravity(
  travel_matrix,
  land_use_data,
  opportunity = "schools",
  travel_cost = "travel_time",
  decay_function = decay_exponential(decay_value = 0.2)
)
head(grav)
#>                 id    schools
#> 1: 89a88cdb57bffff 0.03041853
#> 2: 89a88cdb597ffff 1.15549493
#> 3: 89a88cdb5b3ffff 0.56519126
#> 4: 89a88cdb5cfffff 0.19852152
#> 5: 89a88cd909bffff 0.41378042
#> 6: 89a88cd90b7ffff 0.95737555

fca <- floating_catchment_area(
  travel_matrix,
  land_use_data,
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population",
  method = "2sfca",
  decay_function = decay_binary(cutoff = 50)
)
head(fca)
#>                 id      jobs
#> 1: 89a88cdb57bffff 0.4278111
#> 2: 89a88cdb597ffff 0.3863614
#> 3: 89a88cdb5b3ffff 0.4501725
#> 4: 89a88cdb5cfffff 0.5366707
#> 5: 89a88cd909bffff 0.4280401
#> 6: 89a88cd90b7ffff 0.5176583

Calculating inequality and poverty levels is equally easy. Below we use the previously calculated cumulative accessibility dataset to show some of the available inequality and poverty functions:

palma <- palma_ratio(
  cum_cutoff,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population",
  income = "income_per_capita"
)
palma
#>    palma_ratio
#> 1:    3.800465

poverty <- fgt_poverty(
  cum_cutoff,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population",
  poverty_line = 95368
)
poverty
#>         FGT0      FGT1      FGT2
#> 1: 0.5745378 0.3277383 0.2218769

Related work:

r5r: Rapid Realistic Routing with R5 in R
tracc: Transport accessibility measures in Python
access: Spatial Access for PySAL
aceso: a lightweight Python package for measuring spatial accessibility

Acknowledgement <a href="https://www.ipea.gov.br"><img src="man/figures/ipea_logo.png" alt="IPEA" align="right" width="300"/></a>

accessibility is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil.