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Cumulative access based on maximum travel time interval

Source: R/cumulative_interval.R
cumulative_interval.Rd

Calculates the average or median number of opportunities that can be reached considering multiple maximum travel cost thresholds within a given travel cost interval specified by the user. The time interval cumulative accessibility measure was originally proposed by Tomasiello et al. (2023) .

This function is generic over any kind of numeric travel cost, such as distance, time and money.

Usage

cumulative_interval(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  interval,
  interval_increment = 1,
  summary_function = stats::median,
  group_by = character(0),
  active = TRUE,
  fill_missing_ids = TRUE
)

Arguments

travel_matrix

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.

land_use_data

A data frame. The distribution of opportunities within the study area cells. Must contain the columns id and any others specified in opportunity. NA values, and ids in travel_matrix that are missing from this table, are treated as 0.

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.

travel_cost

A string. The name of the column in travel_matrix with the travel cost between origins and destinations. The notion of cost here is generic over any kind of numeric travel cost, such as distance, time and money.

interval

A numeric vector of length 2. Indicates the start and end points of the interval of travel cost thresholds to be used. The first entry must be lower than the second.

interval_increment

A numeric. How many travel cost units separate the cutoffs used to calculate the accessibility estimates which will be used to calculate the summary estimate within the specified interval. Should be thought as the resolution of the distribution of travel costs within the interval. Defaults to 1.

summary_function

A function. This function is used to summarize a distribution of accessibility estimates within a travel cost interval as a single value. Can be any function that takes an arbitrary number of numeric values as input and returns a single number as output. Defaults to stats::median().

group_by

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.

Accessibility is calculated separately for each unique combination of grouping variables. For constrained accessibility measures, each group is treated as an independent land-use-transportation system.

active

A logical. When TRUE, the function calculates active accessibility (the quantity of opportunities that can be reached from a given origin). When FALSE, it calculates passive accessibility (by how many people each destination can be reached), which is equivalent to the notion of market potential.

fill_missing_ids

A logical. Origins (or destinations, when active is FALSE) that cannot reach any opportunity within the interval would not be included in the output. When TRUE (the default), the function includes them with an accessibility of 0.

Value

A data frame containing the accessibility estimates for each origin/destination (depending if active is TRUE or FALSE) in the travel matrix.

References

Tomasiello DB, Herszenhut D, Oliveira JLA, Braga CKV, Pereira RHM (2023). “A Time Interval Metric for Cumulative Opportunity Accessibility.” Applied Geography, 157, 103007. ISSN 0143-6228. doi:10.1016/j.apgeog.2023.103007 .

See also

Other cumulative access: cumulative_cutoff()

Examples

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"))

df <- cumulative_interval(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  interval = c(20, 30),
  opportunity = "schools",
  travel_cost = "travel_time"
)
head(df)
#>                 id schools
#>             <char>   <num>
#> 1: 89a881a5a2bffff       0
#> 2: 89a881a5a2fffff       3
#> 3: 89a881a5a67ffff       0
#> 4: 89a881a5a6bffff       0
#> 5: 89a881a5a6fffff       0
#> 6: 89a881a5b03ffff      11

df <- cumulative_interval(
  travel_matrix = travel_matrix,
  land_use_data = land_use_data,
  interval = c(40, 80),
  opportunity = "jobs",
  travel_cost = "travel_time"
)
head(df)
#>                 id   jobs
#>             <char>  <num>
#> 1: 89a881a5a2bffff 371990
#> 2: 89a881a5a2fffff 395808
#> 3: 89a881a5a67ffff 378286
#> 4: 89a881a5a6bffff 355500
#> 5: 89a881a5a6fffff 347725
#> 6: 89a881a5b03ffff 414109