Calculates the number of opportunities accessible under a given specified travel cost cutoff.
This function is generic over any kind of numeric travel cost, such as distance, time and money.
cumulative_cutoff(
travel_matrix,
land_use_data,
opportunity,
travel_cost,
cutoff,
group_by = character(0),
active = TRUE,
fill_missing_ids = TRUE
)A data frame. The travel matrix describing the costs
(i.e. travel time, distance, monetary cost, etc.) between the origins and
destinations in the study area. Must contain the columns from_id, to_id
and any others specified in travel_cost.
A data frame. The distribution of opportunities within
the study area cells. Must contain the columns id and any others
specified in opportunity.
A string. The name of the column in land_use_data
with the number of opportunities/resources/services to be considered when
calculating accessibility levels.
A character vector. The name of the columns in
travel_matrix with the travel costs between origins and destinations to
be considered in the calculation.
Either a numeric vector or a list of numeric vectors, one
for each cost specified in travel_cost. The travel cost cutoffs to
consider when calculating accessibility levels. If a list, the function
finds every single possible cutoff combination and use them to calculate
accessibility (e.g. if one specifies that travel time cutoffs should be 30
and 60 minutes and that monetary cost cutoffs should be 5 and 10 dollars,
the output includes accessibility estimates limited at 30 min & 5 dollars,
30 min & 10 dollars, 60 min & 5 dollars and 60 min & 10 dollars). In these
cases, cost constraints are considered simultaneously - i.e. only trips
that take 30 minutes or less AND 5 dollars or less to be completed, for
example, are included in the accessibility output. The cutoff parameter is
not included in the final output if the input includes only a single cutoff
for a single travel cost.
A character vector. When not character(0) (the default),
indicates the travel_matrix columns that should be used to group the
accessibility estimates by. For example, if travel_matrix includes a
departure time column, that specifies the departure time of each entry in
the data frame, passing "departure_time" to this parameter results in
accessibility estimates grouped by origin and by departure time.
A logical. Whether to calculate active accessibility (the of opportunities that can be reached from a given origin, the default) or passive accessibility (by how many people each destination can be reached).
A logical. Calculating cumulative accessibility may
result in missing ids if the they cannot reach any of the destinations
within the specified travel cost cutoff. For example, using a travel time
cutoff of 20 minutes, when estimating the accessibility of origin A that
can only reach destinations with more than 40 minutes results in id A
not being included in the output. When TRUE (the default), the function
identifies which origins would be left out and fills their respective
accessibility values with 0, which incurs in a performance penalty.
A data frame containing the accessibility estimates for each
origin/destination (depending if active is TRUE or FALSE) in the
travel matrix.
Other cumulative access:
cumulative_interval()
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"))
# active accessibility: number of schools accessible from each origin
df <- cumulative_cutoff(
travel_matrix = travel_matrix,
land_use_data = land_use_data,
cutoff = 30,
opportunity = "schools",
travel_cost = "travel_time"
)
head(df)
#> id schools
#> <char> <int>
#> 1: 89a881a5a2bffff 1
#> 2: 89a881a5a2fffff 10
#> 3: 89a881a5a67ffff 1
#> 4: 89a881a5a6bffff 0
#> 5: 89a881a5a6fffff 0
#> 6: 89a881a5b03ffff 14
df <- cumulative_cutoff(
travel_matrix = travel_matrix,
land_use_data = land_use_data,
cutoff = c(30, 60),
opportunity = "schools",
travel_cost = "travel_time"
)
head(df)
#> id travel_time schools
#> <char> <num> <int>
#> 1: 89a881a5a2bffff 30 1
#> 2: 89a881a5a2bffff 60 76
#> 3: 89a881a5a2fffff 30 10
#> 4: 89a881a5a2fffff 60 91
#> 5: 89a881a5a67ffff 30 1
#> 6: 89a881a5a67ffff 60 82
# passive accessibility: number of people that can reach each destination
df <- cumulative_cutoff(
travel_matrix = travel_matrix,
land_use_data = land_use_data,
cutoff = 30,
opportunity = "population",
travel_cost = "travel_time",
active = FALSE
)
head(df)
#> id population
#> <char> <int>
#> 1: 89a881a5a2bffff 11053
#> 2: 89a881a5a2fffff 31903
#> 3: 89a881a5a67ffff 12488
#> 4: 89a881a5a6bffff 14474
#> 5: 89a881a5a6fffff 15053
#> 6: 89a881a5b03ffff 69582
# using multiple travel costs
pareto_frontier <- readRDS(file.path(data_dir, "pareto_frontier.rds"))
df <- cumulative_cutoff(
pareto_frontier,
land_use_data = land_use_data,
opportunity = "jobs",
travel_cost = c("travel_time", "monetary_cost"),
cutoff = list(c(20, 30), c(0, 5, 10))
)
head(df)
#> id travel_time monetary_cost jobs
#> <char> <num> <num> <int>
#> 1: 89a881a5a2bffff 20 0 397
#> 2: 89a881a5a2bffff 20 5 397
#> 3: 89a881a5a2bffff 20 10 397
#> 4: 89a881a5a2bffff 30 0 846
#> 5: 89a881a5a2bffff 30 5 20923
#> 6: 89a881a5a2bffff 30 10 20923