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Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

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  • Jiaxin Zhang

    (Architecture and Design College, Nanchang University, No. 999, Xuefu Avenue, Honggutan New District, Nanchang 330031, China)

  • Zhilin Yu

    (School of Architecture, Southeast University, Nanjing 210096, China)

  • Yunqin Li

    (Architecture and Design College, Nanchang University, No. 999, Xuefu Avenue, Honggutan New District, Nanchang 330031, China)

  • Xueqiang Wang

    (Architecture and Design College, Nanchang University, No. 999, Xuefu Avenue, Honggutan New District, Nanchang 330031, China)

Abstract

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city’s overall functionality. This research scrutinizes the perceptual biases and disparities in architectural function distribution within urban spaces, with a particular focus on Shanghai’s central urban district. The study employs machine learning to clarify these biases within urban spatial perception research, utilizing a tripartite methodology: objective mapping, subjective perception analysis, and perception deviation assessment. The study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Furthermore, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. Our findings underscore the need for urban planning strategies that align with human perceptual needs, thereby enhancing the quality of the urban environment and fostering a more habitable and sustainable urban space. The study’s implications suggest that a deeper understanding of perceptual needs can optimize architectural function distribution, enhancing the urban environment’s quality.

Suggested Citation

  • Jiaxin Zhang & Zhilin Yu & Yunqin Li & Xueqiang Wang, 2023. "Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception," Land, MDPI, vol. 12(7), pages 1-20, June.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1322-:d:1184056
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    References listed on IDEAS

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    1. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    3. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
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    Cited by:

    1. Abdulrazaq Zamil Menshid Al-saedi & Hoshyar Qadir Rasul, 2024. "New Roadmap toward Social Sustainability, from Physical Structures to Perceived Spaces," Sustainability, MDPI, vol. 16(17), pages 1-24, September.
    2. Jiacheng Shi & Yu Yan & Mingxuan Li & Long Zhou, 2024. "Measuring the Convergence and Divergence in Urban Street Perception among Residents and Tourists through Deep Learning: A Case Study of Macau," Land, MDPI, vol. 13(3), pages 1-29, March.

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