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.

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. In this study, we apply machine learning to elucidate these biases within urban spatial perception research, utilizing a three-step methodology: objective mapping, subjective perception analysis, and perception deviation assessment. Our findings reveal that machine learning can expose hidden patterns within this research field, bearing substantial implications for urban planning and design. Of particular note, 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. Moreover, 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. These findings underscore the importance of integrating both objective mapping and subjective perspectives in urban architectural functionality classification.

In light of contemporary energy and environmental objectives, a pivotal transformation of the energy system, encompassing biomass energy, is imperative. A notable challenge in biomass energy facility layout planning is the trade-off between high-efficiency production and the associated investment costs. To harmonize energy efficiency with economy viability, a hybrid layout with the simultaneous construction of centralized and decentralized biomass energy facilities has emerged as a crucial strategic solution. However, the delineation methods for these two layouts lack explicit data support. This study established a population density threshold (PDT) suitable for selecting the distributed layout and employed population density as the criterion for delineating the two layouts. Taking Fuxin City as an example, hybrid layout planning schemes were generated under different PDTs, and a cost and energy benefit analysis framework was developed for these schemes. The results indicated that the scheme with a PDT of 145 person/km² exhibited the highest energy and economic comprehensive benefits. Compared to a single layout, the planning strategy proposed in this study could achieve nearly the same energy surplus level while saving an investment cost ranging from 2403.9 million CNY to 25,000.23 million CNY. The findings are applicable to other regions with similar conditions, and the analysis framework proposed in this study can be utilized in formulating biomass development strategies for other countries and regions.

Based on the concept of integrated communities, this study focuses on improving public service facilities and perfecting convenient service facilities within integrated communities. Taking the central area of Nanchang’s old city as the research subject, the paper analyzes the configuration of public service facilities in 186 communities within this area and examines the correlation of configuration indicators. On the basis of data analysis, five communities with different allocation levels were selected for verification. The verification results show that field research and data analysis mutually confirm each other, and there is a positive correlation between the allocation level and the overall community environment. Combining data analysis and community verification, this study proposes two paths for integrated community construction oriented toward facility optimization and allocation level improvement, providing more operable theoretical support and practical guidance for the construction of integrated communities.

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection.