Street centrality and vitality of a healthy catering industry: A case study of Jinan, China

Abstract

In the context of an overall improvement in the national economy, residents' demand for nutrition and health has been increasing. An industry that provides healthy eating plays an increasingly important role in urban development. Few studies, however, have focused on the relationship between the urban road network structure and the vitality of the healthy catering industry (VHCI). Based on social media data and urban traffic network data, in this study, we explored the impact mechanism of street centrality on the VHCI through a case study of Jinan, China, using geographic detectors and multiscale geographically weighted regression (MGWR) methods. The results show the following: First, the vitality of the catering industry in the main urban area of Jinan has multicore spatial distribution characteristics, and the high-value areas of the vitality of the catering industry are highly matched with the main business districts in the city in space. Second, we found clear spatial differences in street centrality between the walking and driving modes. With an increase in the search radius, the trend of high-value areas closely gathering in the urban center became clearer. The distribution of betweenness was observed from sheet to grid, and the multicenter structure of straightness was more prominent. Third, differences in the residents' perception of the road network structure caused by different travel modes affected their choice of dining places. In the driving mode, betweenness and straightness had a greater impact on the vitality of the catering industry, and the effect of closeness in the walking mode was more obvious. Fourth, the influence of street centrality on the vitality of the healthy catering industry had obvious spatial heterogeneity. In the walking mode, the spatial heterogeneity of straightness was the strongest, followed by betweenness, and closeness was the weakest; in the driving mode, the spatial heterogeneity of closeness was the strongest, followed by straightness, and betweenness was the weakest. From the perspective of residents' travel, the results of this study revealed the influence mechanism of urban road network characteristics on the VHCI. This information can aid planning for urban space optimization and improve residential living quality.

The Interactive Relationship between Street Centrality and Land Use Intensity—A Case Study of Jinan, China

“…The statistical results of the significant influence coefficient of LUI on SC are shown in Table 6 . The influence coefficients of CLUI on closeness centrality and straightness centrality were both positive, while the influence coefficient on betweenness centrality was negative, indicating that improving CLUI was conducive to improving the accessibility and traffic efficiency of the transport network, but could lead to a decline in the mediating effect of the transport network, which is consistent with the research results of Chen et al [ 45 ]. The impact of CLUI on SC was as follows: straightness centrality > closeness centrality > betweenness centrality.…”

“…A large number of karst caves are distributed underground in Jinan, which causes great difficulties for urban subway construction and leads to the lagging development of local rail transit. In addition, restricted by the topographical conditions, the Jinan urban area presents a narrow and long belt space form from east to west and short from north to south [ 45 ]. This often leads to serious traffic congestion in Jinan.…”

Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model

“…Local spatial models were developed by an open-source platform ( https://sgsup.asu.edu/sparc/mgwr ) and GWmodel packages in R 4.1.0 (R Development Core Team, 2021). We used the corrected Akaike information criterion (AICc) to optimize the bandwidth and selected the fixed Gaussian kernel [ 43 , 44 ]. The model performances were evaluated by calculating the coefficient of determination (R 2 ) and AICc [ 44 , 45 ].…”

Exploring the Spatio-Temporal Evolutionary Characteristics of Paomo Restaurants in Xi’an’s Central Urban Area through POI Data Analysis

“…The catering industry is integral to the interplay between urban economics and social dynamics, making the spatial distribution and locational attributes of dining establishments key subjects of interdisciplinary research across economics, sociology, statistics, architecture, and urban-rural planning [7][8][9]. This research aims to elucidate the characteristics of urban spatial structures, analyze the mechanisms that shape them, and propose strategies to optimize urban layouts.…”