Evaluation of The Spatial Quality of Sunken Plazas Based on Multi-source Time-spatial Data

Abstract

Large-scale and fine-grained evaluations of spatial quality are made possible by the introduction and growth of multi-source big data. The spatial analysis method, visual semantic segmentation method, and field measurement method are used to construct a spatial quality evaluation system for urban sunken plazas based on the multi-source Spatio-temporal data, fusing urban road network data, Baidu API data, street view image data, POI data, public review data, and field measurement data. Based on the results of the spatial quality evaluation, the spatial quality measurement and effectiveness are achieved by. The evaluation suggests optimization steps to achieve effective measurement of spatial quality based on the findings of the evaluation of spatial quality and the existing state of spatial construction. The findings demonstrate that Tianjin Mingyuan Square has some fundamental construction in terms of visual, sensory, and use experience. Its space construction is also evenly distributed, and its aesthetic, comfortable, and functional construction is better, but its construction in terms of completeness is relatively subpar. In order to improve the spatial quality of the sunken plaza and encourage its effective and healthy development, we suggest optimization approaches to enhance the spatial landscape, increase the spatial facilities, and optimize the spatial environment.