spax: Spatial Accessibility Analysis in R

spax is an R package designed for advanced spatial accessibility analysis, focusing on Two-Step Floating Catchment Area (2SFCA) methods and their derivatives. The package offers a fresh perspective on spatial accessibility through its raster-based computational approach and modular design.

Features

• Computational Simplicity: Raster-based operations translate complex spatial relationships into simple matrix calculations
• Modular Design: Functions work like LEGO bricks - mix and match components to build custom analysis workflows
• Complex Demand Handling: Support for continuous population surfaces, letting you work with high-resolution demand data
• Monte Carlo Integration: Built-in tools for uncertainty analysis and stochastic demand modeling

Installation

You can install the development version of spax from GitHub with:

# install.packages("pak")
pak::pak("Songyosr/spax")

Example

Here’s how spax’s modular design works in practice, analyzing healthcare accessibility in Thailand’s Region 12:

library(spax)
library(terra)
library(sf)

# Load example data (already included in package)
pop <- read_spax_example("u5pd.tif") # Under-5 population density
distance <- read_spax_example("hos_iscr.tif") # Travel times
hospitals <- hc12_hos # Hospital locations and capacity

# Calculate accessibility using Enhanced 2SFCA
result <- spax_e2sfca(
  demand = pop, # Population density
  supply = hospitals |> st_drop_geometry(), # Hospital capacity
  distance = distance, # Travel times
  decay_params = list(
    method = "gaussian",
    sigma = 30 # 30-minute characteristic distance
  ),
  demand_normalize = "standard", # Prevent demand inflation
  id_col = "id",
  supply_cols = c("s_doc", "s_nurse") # Analyze both doctors and nurses
)

# Plot results
plot(result,   main = c("Access to Doctors", "Access to Nurses"),
     fun = function() lines(bound0)
)

result
#> Spatial Accessibility Analysis (E2SFCA)
#> 
#> Spatial Properties:
#> - Resolution: 520.404 x 520.404
#> - Dimensions: 509 x 647
#> 
#> Measures: 2
#> - s_doc
#> - s_nurse
#> 
#> Facilities: 77
#> 
#> Parameters:
#> - Decay function: gaussian (sigma = 30)
#> - Demand normalization: standard
summary(result)
#> 
#> Summary of Spatial Accessibility Analysis (E2SFCA)
#> --------------------------------------------- 
#> 
#> Accessibility Measures:
#> 
#> s_doc:
#>   Range: 2.14e-10 to 0.1818
#>   Mean (SD): 0.06002 (0.03767)
#>   Quantiles:
#>     Min.:    2.14e-10
#> 
#>   Coverage: 33.2% (109362 of 329323 cells)
#> 
#> s_nurse:
#>   Range: 4.172e-10 to 0.2736
#>   Mean (SD): 0.1066 (0.06332)
#>   Quantiles:
#>     Min.:    4.172e-10
#> 
#>   Coverage: 33.2% (109362 of 329323 cells)
#> 
#> Facility Statistics:
#> -------------------
#> Total Facilities: 77
#> 
#> s_doc:
#>   Total Supply: 4669
#>   Mean (SD): 60.64 (80.09)
#>   Range: 0 to 522
#> 
#> s_nurse:
#>   Total Supply: 8125
#>   Mean (SD): 105.5 (159.1)
#>   Range: 0 to 973

This example demonstrates key features of spax:

• Working with continuous population surfaces (demand)

• Handling multiple supply indicators simultaneously

• Gaussian distance decay for more realistic accessibility modeling

Getting Started

Check out our vignettes for detailed guidance:

See Also

• Getting Started with spax
• Data Preparation Guide
• Dissecting spax: Understanding the package’s inner workings
• Understanding Raster Trade-offs

Contributing

Truth is, I’m just getting started with spax. I’d love to have you on board to help shape the future of spatial accessibility analysis in R. Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.