knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Floating Catchment Area (FCA) methods to Calculate Spatial Accessibility.
Perform various floating catchment area methods to calculate a spatial accessibility index (SPAI) for demand point data. The distance matrix used for weighting is normalized in a preprocessing step using common functions (gaussian, gravity, exponential or logistic).
You can install the released version of fca from CRAN with:
install.packages("fca")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("egrueebler/fca")
This is a basic example which shows you how to calculate a SPAI for demand point data using FCA methods.
Create an example population, supply and distances:
# Population df with column for size pop <- data.frame( orig_id = letters[1:10], size = c(100, 200, 50, 100, 500, 50, 100, 100, 50, 500) ) # Supply df with column for capacity sup <- data.frame( dest_id = as.character(1:3), capacity = c(1000, 200, 500) ) # Distance matrix with travel times from 0 to 30 D <- matrix( runif(30, min = 0, max = 30), ncol = 10, nrow = 3, byrow = TRUE, dimnames = list(c(1:3), c(letters[1:10])) ) D
Normalize distance matrix with gaussian function, apply a threshold of 20 minutes (to compute beta for the function) and formatting input data as named vectors for the FCA method (match IDs of distance weight matrix with demand and supply data).
library(fca) # Normalize distances W <- dist_normalize( D, d_max = 20, imp_function = "gaussian", function_d_max = 0.01 ) # Ensure order of ids pop <- pop[order(pop$orig_id), ] sup <- sup[order(sup$dest_id), ] # Named vectors (p <- setNames(pop$size, as.character(pop$orig_id))) (s <- setNames(sup$capacity, as.character(sup$dest_id)))
Apply FCA method on formatted input, get SPAI for each origin location (p
):
(spai <- spai_3sfca(p, s, W))
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