Abstract
Living streets are a relevant research and practice field for the connotation development and stock renewal of built environments. The quantitative analysis and impact assessment of scene attention utility in living street landscapes are deemed an essential foundation and technical approach. Electroencephalogram (EEG) rhythm analysis and mathematical modeling to assess the deep structural logic and coupling utility of attention in living street scenes provide pioneering research value. This study begins with landscape elements associated with a living street, considering the Jingzhou Street in Xiangyang, China, as an example, and then employs EEG rhythm fitting, principal component regression (PCR), and Kriging spatial interpolation (KSI) algorithms to construct a model for measuring the utility of scene attention. Set up 30 living street scene points, process 20 street elements for an EEG experiment, and collect EEG data from 50 subjects. Develop an attention utility model using PCR, geographically map principal component factors (PCFs) with KSI, and generate weighted scene attention utility evidence-based evaluations in a GIS platform. The results indicate that six PCFs—humanistic characteristics, color schemes, public facilities, spatial connectivity, ornamental plants, and service locations—play a significant role in the scene attention utility of the living street. Moreover, the PCFs of scenes continuously distributed in living street spaces and at intersections help achieve high attention utility. This study develops a “scene–brain” joint attention utility measurement model suitable for living street landscapes, aiming to provide a research foundation and reference cases for urban street landscape quantitative assessment and evidence-based design.
Authors
Wang, L., Li, Z., Zhao, Y., Ding, H., & Wu, X.
10.1016/j.cities.2025.106135