Multi-Source Data-Driven Quality Assessment of Waterfront Public Spaces in Urban Contexts

Abstract

Quality assessment of public spaces is critical in sustainably enhancing urban spatial quality. Existing studies focus on fundamental indicators (e.g., geographic accessibility, planning rationality, and social benefits) and often rely on geographic big data as the primary research input. However, multidimensional interactive analyses that integrate users, places, and spatial configurations are often lacking, making it hard to capture actual user needs and satisfaction levels precisely. To address this issue, we propose a multi-source data-driven approach for spatial quality evaluation, where three types of data (user satisfaction (people), points of interest (places), and urban morphology (space)) are combined. Through a comprehensive comparison of nine representative high-quality waterfront public spaces with multi-source data, common characteristics that are crucial for assessing waterfront public space quality in urban contexts have been analyzed and discussed. The results reveal three key factors significantly influencing waterfront public space quality: the spatial distribution, land-use attributes, and proportion of types of service facilities. First, service facilities with the highest user satisfaction tend to exhibit clustered spatial patterns. Second, municipal and commercial functions play a leading role in spatial quality. Thirdly, a suitable ratio of service facility types is advantageous for enhancing user satisfaction. Therefore, a comprehensive evaluation requires consideration of facility distributions, urban morphologies, and surrounding land-use functions. The proposed multi-source data-driven approach holds great potential for developing innovative and sustainable waterfront design strategies.