Research on the correlation between pedestrian density and street spatial characteristics of commercial blocks in downtown area: a case study on Shanghai Tianzifang

Abstract

Taking Shanghai Tianzifang as an example, this study attempted to utilize theories, such as the Space Syntax Theory, to build a multivariate model with street spatial characteristics as variables, and investigate the correlation between street spatial characteristics and pedestrian density in commercial blocks using multivariate regression analyses of the variables in this model. This study inspected two aspects of spatial characteristics of street space. First, in terms of commercial use characteristics, this study examined how pedestrian density is affected by three variables: store density, overflow ratio of store-front space. Density of building exits and entrances. Next, in terms of structural characteristics, this study probed the relationship between commercial pedestrian density and three other variables: street integration, height of buildings on both sides of the streets, and the stores' distance from block entrances. Last, a multivariate regression analysis was conducted on the research data of the six variables. The results validated that four of the variables are correlated with commercial pedestrian density, in the order of their degree of influence: street integration, store density, density of building exits and entrances, and height of buildings on both sides of the streets. ARTICLE HISTORY

Stepwise multiple linear regression applied to study the influence of pedestrian volumes according to environmental characteristics

“…In order to contribute specific measures to allow the efficient planning of land uses in cities, the research team proposes a methodology that allows the minimum number of variables that significantly affect pedestrian volumes in a given area to be obtained. To this end, a multivariable spatial regression model was considered (Anselin, 2006) to combine the calculation methodologies put forward by Song (2007), Lee (2017) and Fang (2019), and the variables indicated in points 2 to 4 in the articles by Jacobs (1961), Cervero (1997), Frank (2006), Cerin (2013), Ewing (2013), Sung (2015) and Hamidi (2019).…”

“…However, not all variables can be employed in these models because collinearity exists in some variables (Feuillet, 2016). Therefore, multiple linear regression and principal component analyses are carried out to reduce significant variables (Fang, 2019). In these studies, the variance inflation factor is used as an indicator of collinearity among variables, and any over a high value are ruled out.…”

Characteristics of space network system formed by the constituent elements in urban streets: Tianzifang in Shanghai as a case study

“…In previous work, the authors conducted quantitative research on a specific street (Tianzifang) and examined the association between the spatial characteristics of this street and pedestrian flow density (Kai et al 2019). But in this paper, the authors attempted to focus the research perspective on the street constituent elements at a more microscopic level.…”

Characteristics, correlations of traditional street space elements and tourist density following spontaneous renovation: a case study on Suzhou’s Shantang Street

“…There have been previous studies on multielement correlations in this context. Fang et al (2019) analyzed the spatial characteristics of Tianzifang, Shanghai using such theories as spatial syntax; they assigned values to spatial characteristics including store density, the overflow rate of storefront spaces, building entrance-exit density, the degree of integration, building height, and distance from block entrances to explore the relationship between spatial characteristics and pedestrian density via multiple regression analysis. Zhang, Chen, and Shen (2015) quantitatively analyzed the correlation between the spatial elements of traditional mountain settlements as per the figureground relation, street interface type, vertical site characteristics, street scale, and other factors.…”