Degree of spatial interpretability

Model validation ensures that mathematical models accurately represent real-world phenomena and meet scientific requirements in the Earth sciences. Current validation approaches for spatial modelling mainly use accuracy metrics such as goodness of fit and prediction error to validate regression, machine learning, spatial models and geospatial intelligence models. However, existing spatial validation still faces challenges in assessing models’ capacity for spatial interpretability. This study develops a degree of spatial interpretability (DSI) indicator to evaluate the effectiveness and ability of spatial models to capture spatial characteristics such as spatial autocorrelation and heterogeneity and to support model selection. DSI is implemented to evaluate ten common models for spatial prediction of Australian vascular plant species diversity using nineteen abiotic explanatory variables. The results reveal that strong goodness of fit and low prediction error do not necessarily correspond to high spatial interpretability and demonstrate the need to employ DSI in model evaluation. The experiments confirm the importance of assessing a model’s ability to explain spatial characteristics. The DSI indicator enhances model evaluation by providing a spatially informed assessment perspective and has strong potential to advance model evaluation systems.