Integrating Streetscape Images, Machine Learning, and Space Syntax to Enhance Walkability: A Case Study of Seongbuk District, Seoul

Zhongshan Huang, Bin Wang, Shixian Luo (), Manqi Wang, Jingjing Miao and Qiyue Jia
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Zhongshan Huang: Graduate School of Techno Design (TED), Kookmin University, Seoul 02707, Republic of Korea
Bin Wang: College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Shixian Luo: School of Architecture, Southwest Jiaotong University, Chendu 611756, China
Manqi Wang: Graduate School of Techno Design (TED), Kookmin University, Seoul 02707, Republic of Korea
Jingjing Miao: Graduate School of Techno Design (TED), Kookmin University, Seoul 02707, Republic of Korea
Qiyue Jia: Graduate School of Techno Design (TED), Kookmin University, Seoul 02707, Republic of Korea

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

Abstract: As urbanization rapidly progresses, streets have transitioned from mere transportation corridors to crucial spaces for daily life and social interaction. While past research has examined the impact of physical street characteristics on walkability, there is still a lack of large-scale quantitative assessments. This study systematically evaluates street walkability in Seongbuk District, Seoul, through the integration of streetscape images, machine learning, and space syntax. The physical characteristics of streets were extracted and analyzed in conjunction with space syntax to assess street accessibility, leading to a combined analysis of walkability and accessibility. The results reveal that the central and western regions of Seongbuk District outperform the eastern regions in overall street performance. Additionally, the study identifies four distinct street types based on their spatial distribution: high accessibility–high overall score, high accessibility–low overall score, low accessibility–high overall score, and low accessibility–low overall score. The findings not only provide a scientific basis for street development in Seongbuk District but also offer valuable insights for assessing and enhancing walkability in cities globally.

Keywords: walkability; streetscape images; machine learning; space syntax; physical characteristics; Seongbuk District; Seoul (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)

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