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

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.

Uncovering Perceptual Differences in Street Greenery Based on Residents Different Accessibility of Travel Mode: A Case Study of the Central District of Chengdu

“…[7] . Urban Points of Interest (POI) data has also emerged as a component of the urban big data mosaic [8] . Open-source urban road network data, along with various satellite remote sensing datasets, have made detailed investigations into the spatial layout of streets possible.…”

Measuring the Convergence and Divergence in Urban Street Perception among Residents and Tourists through Deep Learning: A Case Study of Macau

“…Few studies pay attention to people's perception of urban functional zones closely related to their lives. Traditional urban architectural classifications can easily lead to biases, and the differences between objective mapping and subjective perception may result in urban planning and infrastructure failing to fully meet the needs and preferences of urban residents [49]. Therefore, distinctive perceptual research on different urban functional zones becomes particularly crucial.…”

Automatic Estimation for Visual Quality Changes of Street Space Via Street-View Images and Multimodal Large Language Models

“…Recent years have marked the introduction of advanced technologies into the realm of urban space assessment. The utilization of Street View Imagery (SVI) [7,34,35] and deep learning algorithms [4,13,14,36], for instance, has allowed for large-scale quantitative analyses [37]. This integration has propelled the field from qualitative descriptions to data-driven, automated evaluations, as evidenced in works [38] that leveraged SVI for extensive urban analysis [39].…”