Ontology-Driven Integration and Visual Analytics for Geospatial Data

Recent advancements in spatial information technology have facilitated the acquisition of substantial spatial data from diverse sources characterized by varying levels of quality. Addressing the current challenge in geographical information sciences, researchers are focusing on how to effectively integrate these datasets to meet the evolving societal demands for heightened spatial data quality. Modern applications of geospatial data demand efficient, interactive, and real-time data integration. The assessment of semantic similarity plays a crucial role in the integration of ontology and spatial data.The increasing abundance of diverse and extensive geospatial data from various sources underscores the need for inventive solutions in the realms of integration and visualization. This paper introduces a conceptual framework that revolves around ontology-driven methodologies, aiming to seamlessly integrate and visually scrutinize geospatial data. Additionally, a case study implementation based on the conceptual framework is documented.