Interpreting differences in access and accessibility to urban greenspace through geospatial analysis

Abstract

Access to urban greenspace is a fundamental requirement in providing critical ecosystem services, improving health and well-being across all ages, fostering social cohesion, and addressing prevalent health disparities in an increasingly urbanised society. Access refers to the availability of urban greenspace, while accessibility indicates the ease of reaching and enjoying these greenspaces and the quality of greenspace services. However, quantitative studies to interpret such difference between accessibility and access are limited. To contribute to this gap, this study developed a Spatial Delta Model (SDM) to quantify the difference between accessibility and access to greenspace and assess its spatial characteristics. The study examines the block-level access, accessibility, and their difference in Perth, Australia, using the SDM with a series of high-resolution greenspace and socio-economic spatial data. Access was calculated as the total greenspace near residential blocks and accessibility was derived using a modified Gaussian two-step floating catchment area (MG2SFCA) approach. Once they were quantified, a set of residential, morphological, and greenspace related factors were utilised to explain the spatial patterns of the difference between accessibility and access using a machine learning geographical detector model. The findings on the measure of the greenspace usability and the user experience further contribute to develop a green city classification system (GCCS), which is useful to informing urban planning and greenspace management.

Keywords

• Spatial accessibility
• Geospatial machine learning
• Urban greenspace
• Spatial morphology
• Geocomplexity
• Spatial statistics