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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorLabib, Labib
dc.contributor.authorKoster, Martijn
dc.date.accessioned2024-03-31T00:02:05Z
dc.date.available2024-03-31T00:02:05Z
dc.date.issued2024
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/46221
dc.description.abstractSystematic reviews show that exposure to greenspaces has a beneficial effect on physical and mental health and well-being. Greenspace exposure is commonly categorised into three core principles; availability, accessibility, and visibility. Although the significance of greenspace exposure is widely acknowledged, concerns arise regarding the quality and comparability of studies within this field. This article proposes the open-source R package GreenExp to overcome these concerns. GreenExp is developed to facilitate robust and transparent analysis in greenspace and health research. It provides researchers with a collection of functionalities that can be applied across multiple countries, enabling comprehensive spatial analysis and enhancing the accuracy of results. The software architecture of the functionalities in GreenExp follows a logical flow encompassing five key components: Data Acquisition, Data Processing, Modelling, Visualisation, and Interface. Green- Exp offers comprehensive functionalities focused on availability, accessibility, and visibility. Each subject encompasses a range of specific functionalities that contribute to analysing greenspaces. GreenExp is a robust, transparent package that adheres to the FAIR(4RS) principles. GreenExp is a solid foundation for enhancing the quality of research in this field.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectGreenEx-R: Developing opensource prototype packages to model multidimensional nature exposure
dc.titleGreenEx-R: Developing opensource prototype packages to model multidimensional nature exposure
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsGreenspace; accessibility; availability; visibility
dc.subject.courseuuApplied Data Science
dc.thesis.id23471


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