Abstract
BACKGROUND AND AIM Environmental exposure is a central concept of the health and behavioral sciences needed to study the influence of the environment on the health and behavior of people within a spatial context. While numerous studies measure some form of exposure, including the influence of air quality, noise, crime, the built environment, and fast food outlets in neighborhoods, we lack a common conceptual model of environmental exposure that captures its main components across all this variety. In this article we propose an ontology design pattern that can be used for this purpose. METHODS To develop the pattern, we encoded the content of six scientific articles that each study a different urban environmental health issue. The ontology pattern specifies causal relations between concepts including persons, activities, exposures, environments and health risks on a conceptual level. On this basis, central notions like environmental stressors and active and passive exposure could be defined in Description Logic (DL) and automatically inferred from the content of a paper. Concepts were linked with data models and modelling methods used in a study in RDF. To test our pattern, we ask competency questions about main study characteristics and translate them to SPARQL queries over RDF content. RESULTS Results summarize epidemiological approaches, exposure concepts, findings, modelling methods, and data used for research in a transparent manner. CONCLUSIONS This ontology systematically distinguishes different methodological approaches. This not only helps us better understand and articulate methodological differences (and thus variations and validity of methods) in past studies, but also shows us the potential to better link the content of the vast amount of scientific publications on this topic regarding methods and data in a clear and systematic way. KEYWORDS ontology, epidemiology, Python, RDF, health, GIS, computer science
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