Abstract
Geospatial information plays an indispensable role in various interdisciplinary and spatially informed analyses. However, the use of geospatial information often entails many semantic intricacies relating to, among other issues, data integration and visualization. For the integration of data from different domains, merely using ontologies is inadequate for handling subtle and complex semantic relations raised by the multiple representations of geospatial data, as the domains have different conceptual views for modelling the geographic space. In addition, for geospatial data visualization - one of the most predominant ways of utilizing geospatial information - semantic intricacies arise as the visualization knowledge is difficult to interpret and utilize by non-geospatial experts. In this paper, we propose a knowledge-based approach using semantic technologies (coupling ontologies, semantic constraints, and semantic rules) to facilitate geospatial data integration and visualization. A traffic spatially informed study is developed as a case study: visualizing urban bicycling suitability. In the case study, we complement ontologies with semantic constraints for cross-domain data integration. In addition, we utilize ontologies and semantic rules to formalize geospatial data analysis and visualization knowledge at different abstraction levels, which enables machines to infer visualization means for geospatial data. The results demonstrate that the proposed framework can effectively handle subtle cross-domain semantic relations for data integration, and empower machines to derive satisfactory visualization results. The approach can facilitate the sharing and outreach of geospatial data and knowledge for various spatially informed studies. (Less)
Highlights
IntroductionThe massive use of geospatial information in various application areas (e.g., traffic analysis and energy simulation) has gradually revealed the indispensable role of geospatial information for interdisciplinary spatially informed research
Over the last decades, the massive use of geospatial information in various application areas has gradually revealed the indispensable role of geospatial information for interdisciplinary spatially informed research
The rationale for developing the multi-tier ontologies is that we model a part of visualization knowledge at the level of service (LOS) level, i.e., the cartographic rules apply to all LOS indexes, including the level of traffic stress (LTS)
Summary
The massive use of geospatial information in various application areas (e.g., traffic analysis and energy simulation) has gradually revealed the indispensable role of geospatial information for interdisciplinary spatially informed research. Geospatial information is a key enabler for solving societal problems across disciplinary boundaries [1], and one of the most powerful information integrators to bridge diverse sources of information [2]. Today’s geospatial data analysis heavily relies on data synthesis, as data from a single source usually does not suffice [3]. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of the data [4]. Other domains, which are not geospatial per se, usually hold different conceptual views of the space that is delineated by geospatial data.
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