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Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

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  • Kerun Li

    (Faculty of Innovation and Design, City University of Macau, Macau 999078, China)

Abstract

Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, the application of advanced technologies, such as deep learning combined with street view image analysis, has certain limitations, especially in the context of high-density urban streets. This study focuses on the street space quality within the urban fabric of the Macau Peninsula, exploring the characteristics of the street space quality within the context of high-density urban environments. By leveraging street view imagery and multi-source urban data, this research employs principal component analysis (PCA) and deep-learning techniques to conduct a comprehensive analysis and evaluation of the key indicators of street space quality. Utilizing semantic segmentation and ArcGIS technology, the study quantifies 16 street space quality indicators. The findings reveal significant variations in service-related indicators such as the DLS, ALS, DCE, and MFD, reflecting the uneven distribution of service facilities. The green coverage index and color richness index, along with other service-related indicators, are notably influenced by tourism and commercial activities. Correlation analysis indicates the presence of land-use conflicts between green spaces and service facilities in high-density urban settings. Principal component analysis uncovers the diversity and complexity of the indicators, with cluster analysis categorizing them into four distinct groups, representing different combinations of spatial quality characteristics. This study innovatively provides a quantitative assessment of street space quality, emphasizing the importance of considering multiple key factors to achieve coordinated urban development and enhance spatial quality. The results offer new perspectives and methodologies for the study of street space quality in high-density urban environments.

Suggested Citation

  • Kerun Li, 2024. "Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis," Land, MDPI, vol. 13(8), pages 1-26, July.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1161-:d:1445341
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    References listed on IDEAS

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