Calculates accessibility accounting for the competition of resources using a measure from the floating catchment area (FCA) family. Please see the details for the available FCA measures.

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

floating_catchment_area(
  travel_matrix,
  land_use_data,
  opportunity,
  travel_cost,
  demand,
  method,
  decay_function,
  group_by = character(0),
  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.

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.

demand

A string. The name of the column in land_use_data with the number of people in each origin that will be considered potential competitors.

method

A string. Which floating catchment area measure to use. Current available options are "2sfca" and "bfca". More info in the details.

decay_function

A fuction that converts travel cost into an impedance factor used to weight opportunities. This function should take a numeric vector and also return a numeric vector as output, with the same length as the input. For convenience, the package currently includes the following functions: decay_binary(), decay_exponential(), decay_power() and decay_stepped(). See the documentation of each decay function for more details.

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.

fill_missing_ids

A logical. When calculating grouped accessibility estimates (i.e. when by_col is not NULL), some combinations of groups and origins may be missing. For example, if a single trip can depart from origin A at 7:15am and reach destination B within 55 minutes, but no trips departing from A at 7:30am can be completed at all, this second combination will not be included in the output. When TRUE (the default), the function identifies which combinations would be left out and fills their respective accessibility values with 0, which incurs in a performance penalty.

Value

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

Details

The package currently includes two built-in FCA measures:

  • 2SFCA - the 2-Step Floating Catchment Area measure was the first accessibility metric in the FCA family. It was originally proposed by Luo and Wang (2003) .

  • BFCA - the Balanced Floating Catchment Area measure calculates accessibility accounting for competition effects while simultaneously correcting for issues of inflation of demand and service levels that are present in other FCA measures. It was originally proposed by Paez et al. (2019) and named in Pereira et al. (2021) .

References

Luo W, Wang F (2003). “Measures of Spatial Accessibility to Health Care in a GIS Environment: Synthesis and a Case Study in the Chicago Region.” Environment and Planning B: Planning and Design, 30(6), 865--884. ISSN 0265-8135, 1472-3417, doi:10.1068/b29120 .

Paez A, Higgins CD, Vivona SF (2019). “Demand and Level of Service Inflation in Floating Catchment Area (FCA) Methods.” PLOS ONE, 14(6), e0218773. ISSN 1932-6203, doi:10.1371/journal.pone.0218773 .

Pereira RH, Braga CKV, Servo LM, Serra B, Amaral P, Gouveia N, Paez A (2021). “Geographic Access to COVID-19 Healthcare in Brazil Using a Balanced Float Catchment Area Approach.” Social Science & Medicine, 273, 113773. ISSN 02779536, doi:10.1016/j.socscimed.2021.113773 .

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"))

# 2SFCA with a step decay function
df <- floating_catchment_area(
  travel_matrix,
  land_use_data,
  method = "2sfca",
  decay_function = decay_binary(cutoff = 50),
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population"
)
head(df)
#>                 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


# BFCA with an exponential decay function
df <- floating_catchment_area(
  travel_matrix,
  land_use_data,
  method = "bfca",
  decay_function = decay_exponential(decay_value = 0.5),
  opportunity = "jobs",
  travel_cost = "travel_time",
  demand = "population"
)
head(df)
#>                 id       jobs
#> 1: 89a88cdb57bffff 0.10280082
#> 2: 89a88cdb597ffff 0.30930287
#> 3: 89a88cdb5b3ffff 0.07288551
#> 4: 89a88cdb5cfffff 0.09759117
#> 5: 89a88cd909bffff 0.07390234
#> 6: 89a88cd90b7ffff 1.22525579