Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing

Xinyu Hu (), Ruxia Bai, Chen Li, Beixiang Shi and Hui Wang
Additional contact information
Xinyu Hu: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
Ruxia Bai: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
Chen Li: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
Beixiang Shi: School of Architecture, Southeast University, Nanjing 210037, China
Hui Wang: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China

Land, 2024, vol. 13, issue 10, 1-20

Abstract: Urban high-density development has led to the emergence of complex three-dimensional pedestrian networks. As a crucial component of city centers, these networks significantly influence the spatial distribution of service industries. Understanding the correlation between pedestrian networks and service industry layouts is vital for effective planning and development. This study proposes a technical framework for analyzing the relationship between three-dimensional pedestrian networks and service industry layouts. Using the Xinjiekou central area in Nanjing as a case study, we constructed a three-dimensional pedestrian network model using the sDNA method. Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. The results indicate that pedestrian network closeness is significantly positively correlated with the number and average spending of catering formats, while betweenness shows almost no correlation. High-closeness areas, due to their traffic convenience and walkability, are more conducive to the concentration of catering formats and higher spending levels. Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.

Keywords: three-dimensional pedestrian network; spatial design network analysis; graph convolutional neural network; catering formats; urban central area; POI (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/10/1553/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/10/1553/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:10:p:1553-:d:1485228

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().