accessibility offers a set of fast and convenient functions to calculate multiple transport accessibility measures. 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 active and passive accessibility levels using multiple accessibility measures, such as: cumulative opportunities (using either travel cost cutoffs or intervals), minimum travel cost to closest N number of activities, gravity-based (with different decay functions) and different floating catchment area methods.
Stable version:
install.packages("accessibility")Development version:
# install.packages("remotes")
remotes::install_github("ipeaGIT/accessibility")library(accessibility)
# required data: a travel matrix and some land use data
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"))
cost_closest <- cost_to_closest(
travel_matrix,
land_use_data,
opportunity = "schools",
travel_cost = "travel_time",
n = 1
)
head(cost_closest)
#> id travel_time
#> 1: 89a881a5a2bffff 29
#> 2: 89a881a5a2fffff 24
#> 3: 89a881a5a67ffff 28
#> 4: 89a881a5a6bffff 33
#> 5: 89a881a5a6fffff 32
#> 6: 89a881a5b03ffff 17
cum_cutoff <- cumulative_cutoff(
travel_matrix,
land_use_data,
opportunity = "jobs",
travel_cost = "travel_time",
cutoff = 30
)
head(cum_cutoff)
#> id jobs
#> 1: 89a88cdb57bffff 22239
#> 2: 89a88cdb597ffff 36567
#> 3: 89a88cdb5b3ffff 42372
#> 4: 89a88cdb5cfffff 55571
#> 5: 89a88cd909bffff 26774
#> 6: 89a88cd90b7ffff 36991
cum_interval <- cumulative_interval(
travel_matrix,
land_use_data,
opportunity = "jobs",
travel_cost = "travel_time",
interval = c(20, 30)
)
head(cum_interval)
#> id jobs
#> 1: 89a88cdb57bffff 7649
#> 2: 89a88cdb597ffff 21990
#> 3: 89a88cdb5b3ffff 22282
#> 4: 89a88cdb5cfffff 26841
#> 5: 89a88cd909bffff 14421
#> 6: 89a88cd90b7ffff 25699
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 0.4357418
#> 2: 89a88cdb597ffff 0.3938630
#> 3: 89a88cdb5b3ffff 0.4589910
#> 4: 89a88cdb5cfffff 0.5469433
#> 5: 89a88cd909bffff 0.4358530
#> 6: 89a88cd90b7ffff 0.5271746Please read the vignettes for more details on the usage:
vignette("accessibility").vignette("decay_functions", package = "accessibility").
accessibility is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil.