Spatial data analysis and prediction are fundamental in geoscience for mapping the spatial distribution of continuous variables and consequent decision-making. Although geostatistical tools for spatial prediction are available, they often require programming expertise or involve numerous manual steps that can be time-consuming and prone to user subjectivity. Here, we developed an interactive web application that simplifies spatial prediction workflows and reduces subjectivity in statistical analysis, making it accessible to the entire geoscience community. sGs UnMix (available online at https://apps.bo.ingv.it/sgs-unmix/) is built with the shiny package for R studio and is organized into four main panels, which allows data loading and coordinate projection, data sources separation and threshold value definition, modeling spatial continuity with the variogram, and sequential gaussian simulation (sGs) for spatial prediction. Automated variogram fitting and unmixing data populations reduce user bias and enhance reproducibility. Heat maps of predicted values overlaid on satellite or geographic map layers dynamically update in response to input values, allowing quick visualization of spatial patterns and anomalies. The web app can be a standardized method for estimating volcanic volatile fluxes (e.g., soil CO2 emissions) locally and globally. Still, it can also be applied in diverse geoscience fields, including ore deposit and hydrocarbon mapping, environmental monitoring, and climate research. Compared to existing geostatistical tools, the web app offers automated features, interactivity, dynamic and responsive outputs (tables and plots), and the flexibility of being a platform-independent, standalone web-based solution.

Bini G., Tamburello G., Cacciaguerra S., Perfetti P., sGs UnMix: a web application for spatial prediction and mixture modeling with a case study on volcanic soil CO2  fluxes, Environmental Modelling and Software, accepted for publication (2025)

Source code available here:

https://github.com/giancarlotamburello/sGs_UnMix