An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

Abstract

Urban vitality is an important indicator of urban development capacity. Streets’ metrics can depict intro-urban fabrics and physiognomy in detail, and thus street vitality affected by street metrics is a concrete manifestation of urban vitality. However, few studies have evaluated dynamic vitality or explored how it is influenced by land use. To bridge this gap, we fully evaluated street dynamic vitality and explored how to enhance the street dynamic vitality by changing the distribution and combination of land use. Specifically, we examined the street dynamic vitality and land use diversity in the main urban zone of Xining city in China using mobile communication and point of interest data, adopted optimized K-means clustering to identify street dynamic vitality types, evaluated the classification result based on vitality intensity and vitality stability, and explored the link between land use and dynamic vitality. Since vitality intensity limitations were found in describing street dynamic vitality, it was necessary to introduce vitality stability. We also found a positive correlation between the vitality intensity and land use density, there were outstanding traffic facilities in high-intensity vitality streets, and improving the abundance and uniformity of land use was beneficial to increase vitality stability. Overall, describing street vitality from a dynamic perspective can improve resource utilization efficiency and rationally plan layouts.

A New Insight into Understanding Urban Vitality: A Case Study in the Chengdu-Chongqing Area Twin-City Economic Circle, China

“…However, different from this paper, they did not identify the influence path of UV. In addition, [20] and [80] have drawn conclusions on the GMUV that are similar to this paper.…”

“…It is certain that these theoretical findings have laid a solid foundation for understanding vitality. However, there is a lack of consensus on both the definition of vitality [16,17] and of quantitative studies concerning the GMUV from a macroscopic level [3,18], although a few scholars have explored the driving mechanism of vitality [19,20]. In practice, the demand for urban space increases with the growing economy, which has motivated an increasing number of scholars to begin to study the role of vitality in urban planning and urban space design [21,22].…”

Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

“…It should be noticed that the mechanism of OMI is comprehensive including not only individual influence of each factor, but also the interactive effects between factors (Figure 4). Based on the literature [37][38][39][40][41] and the situation of Guangzhou, six types of potential driving factors were selected to represent the built environment from multi-perspectives including accessibility, buildings, cultural inclusiveness, and land use. First, Road density (Road) [37] and distance to metro stations (Metro) were calculated in ArcMap 10.2 to represent accessibility.…”

“…However, the mechanism of a geographical phenomenon (residents' spatio-temporal distribution in this study) is comprehensive and complex, this is, it is not caused by a single aspect [21]. Therefore, few existing studies were able to analyze the coupling or interactive effects between factors [37][38][39][40][41], which was essential to the exploration of the mechanism. A popular quantitative mechanism uncovering technique called geographical detector model (GeoDetector) was adopted in this study to explore the individual influences and interactive effects of potential driving factors [49][50][51][52].…”

“…Tu et al, proposed a framework integrating cellphone data, POIs (point of interests) and check-in data to portray urban vibrancy in Shenzhen, and its mechanism was explored with global and local regressions [38]. Guo et al, evaluated the dynamic vitality on the street scale at different times with POIs and cellphone data in Xining [39]. Meng et al, explored the association between urban vibrancy and landscape characteristics with review data via a regression model in Shenzhen [40].…”