Spatial and temporal resolution of sensor observations

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Observation is crucial in Geographic Information Science, essential for monitoring phenomena like climate change and demographic movements. The resolution of these observations significantly affects the decisions made, as it influences the patterns detected during analysis. Despite its importance, there is currently no established theory of resolution for observations in this field. This work aims to develop such a theory, focusing on the spatial and temporal resolution of individual observations and collections. By employing ontology as a formal specification technique, the study provides theoretical insights into observation resolution and generates computational artifacts relevant to the Sensor Web. It proposes a receptor-based theory for single observations and a theory for collections based on the study area and period. The consistency of these theories is validated using the functional programming language Haskell. Additionally, the work offers two ontology design patterns encoded in the Web Ontology Language, which, when used with SPARQL, facilitate the retrieval of observations at varying resolutions. Overall, this research contributes valuable ideas to the discourse on data quality in Geographic Information Science and the Sensor Web.