The CUGA Method: A Reliable Framework for Identifying Public Urban Green Spaces in Metropolitan Regions

Borja Ruiz-Apilánez () and Francesco Pilla
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Borja Ruiz-Apilánez: Escuela de Arquitectura, Universidad de Castilla-La Mancha, 45071 Toledo, Spain
Francesco Pilla: School of Architecture, Planning and Environmental Policy, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland

Land, 2025, vol. 14, issue 9, 1-31

Abstract: This study addresses the challenge of reliably identifying Public Urban Green Spaces (PUGS) in metropolitan areas, a key requirement for advancing equitable access to green infrastructure and monitoring progress toward SDG 11.7 and WHO recommendations. In the absence of consistent local datasets, we propose the Candidate Urban Green Area (CUGA) method, which integrates OpenStreetMap and Copernicus Urban Atlas data through a structured, transparent workflow. The method applies spatial and functional filters to isolate green spaces that are publicly accessible, meet minimum size and usability criteria, and are embedded within the urban fabric. We validate CUGA in the Dublin Region using a stratified random sample of 1-ha cells and compare its performance against five alternative datasets. Results show that CUGA achieves the highest classification accuracy, spatial coverage, and statistical robustness across all counties, significantly outperforming administrative, crowdsourced, and satellite-derived sources. The method also delivers greater net spatial impact in terms of green area, catchment coverage, and residential land intercepted. These findings support the use of CUGA as a reliable and transferable tool for urban green space planning, policy evaluation, and sustainability reporting, particularly in data-scarce or fragmented governance contexts.

Keywords: public urban green spaces; green infrastructure; green areas accessibility; open geospatial data; sustainable development goals (SDG 11.7); urban land use mapping (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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