Exploring the Spatiotemporal Heterogeneity and Stationarity in the Relationship between Street Vitality and Built Environment. (February 2023)

• Record Type: Journal Article
• Authors: Zhao, Kai; Guo, Jinhan; Ma, Ziying; Wu, Wanshu
• Is Part Of: SAGE Open. Volume 13:Number 1(2023)
• Publication Date: 2023-02
• Subjects: street vitality -- built environment -- spatiotemporal heterogeneity -- geographically and temporally weighted regression -- multisource big data; Social sciences -- Periodicals; 300.5
• DOI: 10.1177/21582440231152226
• Languages: English
• ISSNs: 2158-2440

Abstract

Previous studies substantiate built environment influences street vitality. However, most of them focus on whether the built environmental elements have an influence on the street vitality, and ignore the spatiotemporal heterogeneity of the influences at the district scale. Using multisource big data, we comprehensively measure the street vitalities of different periods and the built environment in different dimensions on Xiamen Island. Geographically and temporally weighted regression (GTWR) models are constructed to systematically analyze the spatiotemporal heterogeneity of the influences of the built environment on the street vitality. Results show that the influence of function remains constant over time. Transit has the strongest effect on the improvement of street vitality during peak hours. The impact of design is strongest in the evening. The effect of accessibility gradually strengthened over time, reaching the highest in the evening. In terms of the spatial dimension, the heterogeneity brought about by the new and old urban areas is significant. The spatial heterogeneity of design's influences is prominently brought about by large green lands and landscape streets. Density, bus station and spatial scale have strong temporal and spatial stability. Length is the most unstable during the weekdays. In order to maintain street vitality and form sustainable traffic, differentiated strategies of vitality enhancement should be formulated according to the locations and attributes of the streets.