accessibility

An R package for calculating and visualizing spatial accessibility to facilities using cost-distance analysis. The package performs accessibility modeling by computing accumulated travel costs from facility locations to population centers and generates comprehensive visualizations including maps and summary statistics.

Features

• Cost-distance analysis using realistic travel impedances
• Motorized friction surfaces from the Malaria Atlas Project
• Publication-ready visualizations with maps and summary charts
• Default Kenya datasets included (population, boundaries, friction surface)
• Flexible input options for custom data and study areas
• Multiple output formats (PNG maps, CSV summaries)

Installation

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

# install.packages("pak")
pak::pak("cema-uonbi/accessMap")

Or using devtools:

# install.packages("devtools")
devtools::install_github("cema-uonbi/accessMap")

Quick Start

Basic Example with Default Kenya Data

library(accessMap)

# Define facility coordinates (longitude, latitude)
health_facilities <- matrix(c(
  36.8219, -1.2921,  # Nairobi
  39.6682, -4.0435,  # Mombasa  
  35.2834, 0.5143    # Eldoret
), ncol = 2, byrow = TRUE)

# Run accessibility analysis
result <- accessibility(
  points = health_facilities,
  label = "Health Facilities",
  name = "kenya_health_access"
)

# View summary statistics
print(result$national_summary)

# Display combined map and chart
plot(result$combined_plot)

Custom Analysis with Your Own Data

# Use your own datasets
result <- accessibility(
  points = my_facility_coordinates,
  transitionMatrix = my_friction_surface,
  populationRaster = my_population_data,
  adm0 = my_country_boundary,
  adm1 = my_admin_boundaries,
  label = "Veterinary Clinics",
  path = "./outputs",
  name = "custom_analysis"
)

High-Resolution Outputs

# Generate high-quality maps for publication
result <- accessibility(
  points = facilities,
  saving_list = list(
    dpi = 2000,      # High resolution
    width = 2,       # Larger size
    height = 2
  ),
  name = "publication_maps"
)

Output Files

The function automatically generates:

• {name}_combined_plot.png - Integrated map and summary chart
• {name}_map_plot.png - Accessibility map only
• {name}_bar_plot.png - Summary bar chart only
• {name}_national_summary.csv - Population statistics by travel time

Travel Time Categories

The analysis classifies population accessibility into five bands:

• ≤30 minutes: High accessibility
• 31-60 minutes: Moderate accessibility
• 61-90 minutes: Low accessibility
• 91-120 minutes: Very low accessibility
• >120 minutes: Extremely low accessibility

Data Requirements

Default Data (Kenya)

The package includes ready-to-use datasets for Kenya:

• Population: WorldPop gridded population data
• Friction surface: Motorized travel friction from Malaria Atlas Project
• Administrative boundaries: Country and county boundaries

Custom Data

For other countries/regions, provide:

• Facility coordinates: Matrix or data frame with longitude/latitude

• Population raster: High-resolution gridded population

• Transition matrix: Cost-distance friction surface

• Administrative boundaries: Country and subdivision boundaries

Important: When using custom transition matrices, ensure they are properly cropped and masked with your study area boundaries to avoid computational errors.

Methodology

The accessibility analysis is based on established frameworks from:

• Weiss et al. (2018, 2020) - Global travel time mapping methodologies
• Pfeffer et al. (2018) - Malaria Atlas Project data access
• van Etten (2017) - Spatial distance calculations in R

The default friction surface uses motorized transport assumptions optimized for healthcare accessibility analysis.

Use Cases

• Healthcare accessibility - Hospital and clinic catchment analysis
• Veterinary services - Animal health facility coverage
• Education access - School accessibility mapping
• Emergency services - Response time analysis
• Infrastructure planning - Facility placement optimization

Advanced Features

Access Raw Outputs

result <- accessibility(points = facilities, name = "analysis")

# Travel time raster (minutes to nearest facility)
travel_times <- result$access

# Gridded data with coordinates and population
detailed_data <- result$temp_df

# Customize further analysis
library(ggplot2)
ggplot(result$temp_df, aes(x = x, y = y, fill = travel_time)) +
  geom_raster() +
  scale_fill_viridis_c(name = "Minutes")

Custom Saving Options

accessibility(
  points = facilities,
  saving_list = list(
    dpi = 1500,
    width = 3,
    height = 2,
    units = "in"
  ),
  path = "~/Desktop/maps"
)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

References

• Pfeffer, D. A., et al. (2018). malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malaria Journal, 17(1), 352.

• van Etten, J. (2017). R Package gdistance: Distances and Routes on Geographical Grids. Journal of Statistical Software, 76(13), 1-21.

• Weiss, D. J., et al. (2018). A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688), 333-336.

• Weiss, D. J., et al. (2020). Global maps of travel time to healthcare facilities. Nature Medicine, 26(12), 1835-1838.