A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide Scenes

Juan Li, Jin Zhang (), Li Wang and Ao Zhao
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Juan Li: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jin Zhang: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Li Wang: College of Computer Science and Technology (Data Science), Taiyuan University of Technology, Taiyuan 030600, China
Ao Zhao: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Sustainability, 2024, vol. 16, issue 23, 1-17

Abstract: Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale spatiotemporal process within landslide scenes. To address these issues, we proposed a hierarchical spatiotemporal data model, named as HSDM, to enhance the representation for geographic scenes. Specifically, we introduced a spatiotemporal object model that integrates both their structural and process information of objects. Furthermore, we extended the process definition to capture complex spatiotemporal processes. We sorted out the relationships used in HSDM and defined four types of spatiotemporal correlation relations to represent the connections between spatiotemporal objects. Meanwhile, we constructed a three-level graph model of geographic scenes based on these concepts and relationships. Finally, we achieved representation and modeling of a dynamic landslide scene in Heifangtai using HSDM and implemented complex querying and reasoning with Neo4j’s Cypher language. The experimental results demonstrate our model’s capabilities in modeling and reasoning about complex multi-scale information and spatio-temporal processes with landslide scenes. Our work contributes to landslide knowledge representation, inventory and dynamic simulation.

Keywords: spatiotemporal data model; GIS; landslide inventory; knowledge graph; dynamic simulation; emergency management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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