Abstract
Urban Public Green Spaces (UPGS) available at walking distance are a vital component of urban quality of life, of citizens’ health, and ultimately of the right to the city. Their demand has suddenly become even more ostensive due to the measures of “social distancing” and the restrictions of movement imposed in many countries during the COVID-19 outbreak, showing the importance of the public urban parks and green open spaces located near homes and accessible by foot. Hence, the idea of “green self-sufficiency” at the local, neighbourhood and sub-neighbourhood level has emerged as a relevant objective to pursue. For this purpose, we have constructed a high-fidelity evaluation model to assess the walking accessibility of UPGS at the highly granular spatial scale of street network nodes. The evaluation procedure is based on a novel index constructed around the concept of distance-cumulative deficit, scoring nodes with respect to all the available UPGS within their catchment area of slope-corrected walking distance of 2 km. To showcase the possible outputs of the evaluation procedure and their exploratory analyses, we present an application on the city of Cagliari, Italy. In doing that, we argue that the proposed evaluation approach is an advancement over the traditional (density-based) approaches of assessment of green area availability, and that it provides an intuitive, flexible and extendable tool useful to better evaluate and understand the current and the potential accessibility of urban green space, and to support urban planning, policy making and design.
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Blečić, I., Saiu, V., Trunfio, G.A. (2020). Towards a High-Fidelity Assessment of Urban Green Spaces Walking Accessibility. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_39
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