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OFF-SETT: A Semantic Framework for Annotating Trends in Spatiotemporal Data
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DOI：10.3390/ijgi15030132.png)
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Abstract
En 中文
The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union's Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement.
Keywords:
semantic web
ontology
knowledge graphs
trends annotation
change detection
time series
spatiotemporal data
Journal
I
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503
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0