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MBGapp

MBGapp is an interactive Shiny application for teaching and practising model-based geostatistics (MBG). It guides users through the complete spatial analysis workflow — data exploration, variogram fitting, model estimation, and spatial prediction — without requiring any coding.

Features

Three outcome types — continuous, prevalence (binomial), and count (Poisson)
Interactive maps — leaflet-based exploration and prediction maps with pan/zoom
Empirical variogram — adjustable bins, distance cutoff, and correlation functions
Bayesian estimation — geostatistical model fitting via RiskMap (MCMC) with an optional INLA (fast Bayes) backend when the INLA package is installed
Spatial prediction — mean surface, standard error, exceedance probability, and quantile maps over a user-defined grid
Downloadable report — generate a PDF report with selected outputs
Shapefile support — upload a boundary shapefile to constrain maps and grids
Covariate support — include linear and non-linear covariate effects

Workflow

Tab	What you do
Explore	Upload data, choose data type, inspect the spatial distribution on an interactive map and scatter plots
Variogram	Examine spatial correlation structure; fit theoretical variogram models
Estimation	Fit a geostatistical model; view parameter estimates and 95% confidence intervals
Prediction	Map the predicted surface over the study region
Report	Download a PDF report of selected outputs

Installation

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("olatunjijohnson/MBGapp", ref = "main")

Then launch the app:

library(MBGapp)
run_app()

Run without installing

shiny::runGitHub(
  repo     = "MBGapp",
  username = "olatunjijohnson",
  ref      = "main",
  subdir   = "inst/MBGapp"
)

Online version

Access the app directly in your browser — no R installation needed:

https://olatunjijohnson.shinyapps.io/mbgapp/

Example data

The package ships with the Loa loa prevalence survey dataset from Cameroon (columns: Longitude, Latitude, Positive, Examined). A 10 km prediction grid and covariate rasters for Cameroon are also included.

Additional example files can be downloaded from Google Drive:

Dependencies

MBGapp uses the following R packages:

shiny, shinyjs, sf, terra, leaflet, leafem, tidyterra, stars, ggplot2, dplyr, readr, tidyr, magrittr, splines, geoR, RiskMap (MCMC backend), and optionally INLA (fast Bayes backend).

Authors

Olatunji Johnson, Claudio Fronterre, Emanuele Giorgi CHICAS, Lancaster Medical School, Lancaster University

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.