IISDIA : An interpolation and anomaly detection algorithm especially adapted to cases where data is sparse (〈30), clustered and/or uncertain

A new method of interpolation and anomaly detection especially designed for sparse, clustered or imprecise environmental data (SIC). Such data cannot be processed by current state of the art spatial methods and models, including the most widely used, such as kriging. Indeed, the statistics obtained on SIC data (on the order of 5–30) do not allow us to define a covariance or to calibrate the numerous hyper-parameters of sophisticated Bayesian or deep image prior models. We therefore adapted an information dissemination algo- rithm to handle SIC data. This probabilistic model has been enriched (anisotropy, de-clustering, auto-variog- raphy, multi-support, treatment of covariates, and censored data) in a way that fully meets the needs for environmental SIC data and can be used in conjunction with hybrid propagation of epistemic and aleatoric uncertainties and anomaly detection, whatever their mathematical form. Programmed as a R package, this software has been used in Belbeze et al. 2025 article.