Xiaowen Wu

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.

The analysis and evaluation of the service capacity of an urban public space is of great importance for optimizing spatial design and ensuring sustainable regeneration of the space. Point-of-interest (POI) data analysis is a common method for evaluating the performance of public space since it contains various geographical information about specific facilities. However, this method is incapable of providing intuitive and clear feedback on the usage of the space, such as visitor experience and satisfaction levels. In this paper, we present a hybrid approach that combines POI data with questionnaire surveys to comprehensively analyze and evaluate the service capacity of the facilities in a waterfront public space. By taking the Changning section of the Suzhou Creek in Shanghai as an example, we evaluate and verify the utilization rate and satisfaction level of public facilities based on this hybrid approach with three satisfaction factors: accessibility, landscape visual quality, and service functions. The results reveal that the service space that can be reached on foot provides the most satisfaction in terms of accessibility, followed by the space that can be reached by bicycle. When it comes to landscape visual quality, visitors are more concerned with the view around the facility than with the greenery. Regarding service functions, the service facility with beverage outlets, fitness, and small gatherings is more appealing. The proposed approach will be useful for further developing advanced public space evaluation strategies with real-time feedback capabilities, as well as for the intelligent design and long-term regeneration of future public spaces.