基于卡诺模型和多源数据的城市街区步行适宜性评价与优化

科学评价与优化街区步行适宜性对提升城市活力、推动绿色发展具有重要意义。首先从居民实际需求出发，基于马斯洛需求理论(Maslow’s demand theory)和卡诺模型(Kano model)构建包含五个维度共十五个指标的城市街区步行适宜性评价模型，结合多源数据和改进的CRITIC(criteria importance though intercrieria correlation)-熵权赋权法以合肥市为例进行测度；随后结合热点分析识别各维度高、低值的空间聚类情况，并分析产生原因；最后引入街区活力，利用双变量莫兰指数探讨二者的空间匹配关系，进而提出步行适宜性优化方法。结果表明，基于马斯洛需求理论和卡诺模型可从通畅性、便捷性、安全性、丰富性和舒适性五个维度对步行适宜性展开评价；研究区内的步行适宜性具有显著的空间分异性和自相关性，且与街区活力存在较多不匹配区域。在此基础上提出街区步行适宜性优化策略，可为合肥市未来步行空间设计和改造提供参考。

Evaluation and optimization of neighborhood walkability based on Kano model and multi-source data

The scientific evaluation and optimization of neighborhood walkability play a pivotal role in enhancing urban vitality and fostering green development. This study initially constructed a model for evaluating urban neighborhoods walkability, which encompassed five dimensions and eighteen indicators based on Maslow''s demand theory and Kano model. Subsequently, a combination of multi-source data and the improved CRITIC-entropy method was employed to assess the neighborhood walkability of Hefei City. The study Then the hotspot analysis was conducted to identify the spatial clusters of high and low values in each dimension, and the reasons for the creation of these clusters were analyzed. Finally, the concept of neighborhood vitality was introduced to explore the spatial matching relationship between the walkability and vitality using the bivariate Moran’s index, and the methods for optimizing walkability were proposed. The results indicate that walkability can be evaluated based on Maslow''s demand theory and Kano model from five dimensions: accessibility, convenience, safety, richness and comfortableness. What is more, significant spatial heterogeneity and autocorrelation of walkability are observed within the study area, along with disparities between walkability and neighborhood vitality. Building upon these findings, an optimization strategy of neighborhood walkability is proposed, which can provide a reference for the future design and renovation of walking space in Hefei City.