A knowledge graph-driven method for automated Geo-computations: Illustrated with soil erosion and soil potential productivity cases in China

Geo-computation is a crucial process in geographic information science that selects geo-computational models and matches geographic data based on a geo-computational task for detecting, predicting, and simulating geographic entities, events, and phenomena. However, current geo-computations require expertise from users to effectively configure the models, data, and procedures for specialized tasks, which particularly poses challenges for users, especially novice users, as their attention is often drawn to technical details rather than computational analysis of the task. Therefore, we propose a systematic descriptive and procedural method driven by knowledge graphs to capture, organize, and process the essential features of components, relationships, and dynamic computational procedures in geo-computations, aiming to reduce manual involvement and assist in automating model selection and data matching. Then, an application prototype system is developed to implement automated geo-computations that are driven by knowledge graphs. Two application cases, namely, soil erosion and soil potential productivity, are computed to illustrate the accessibility of automated geo-computations supported by our proposed method. As demonstrated by the cases studied, the proposed knowledge graph-driven method improves the efficiency of model selection and configuration, enhances the value of open data, and advances integration of data and models for automated geo-computations.