Knowledge-based geospatial data integration and visualization with Semantic Web technologies

Geospatial information is indispensable for various spatially-informed analysis and decision-making, e.g. traffic analysis and built environment processes. Geospatial data often must be integrated for meaningful analysis, whereas such integration is challenging due to siloed data organization, semantic heterogeneity and multiple representation of geospatial data. Moreover, the visualization of geospatial data is one of the most prominent ways of utilizing geospatial data, however how to properly visualize the data is sometime difficult, as it pertains to a wide range of visualization (cartographic) knowledge. Semantic Web technologies unveil a promising way to mitigate these issues, as they provide means of data integration on the Web, and knowledge representation capacity to formally represent the visualization knowledge. In this PhD project, we investigate the potential values of Semantic Web technologies for geospatial data integration (particularly for geospatial data with multiple representation) and visualization in several cases, where the integration and visualization knowledge is formalized using Semantic Web technologies. All the case studies embody realworld meaning and entail data integration and visualization challenge, which have been addressed by state-of-the-art solutions inadequately. Preliminary results demonstrate great yet not fully unlocked potential of Semantic Web technologies for geospatial data, and also disclose challenges that need to be addressed.