In old urban districts with tenement buildings, street markets address the working class’s social and economic needs. They are pivotal in fostering supportive communities, in particular for low-income residents in sub-standard housing. However, their outdated overall environment impedes the delivery of adequate support to residents. Prior research on street markets has focused on those in the Americas and Europe, and has revealed the societal support values of these markets. However, studies on street markets in high-density cities like Hong Kong are lacking. This study introduces an evaluation framework to assess the impact of street markets on the development of supportive communities for Hong Kong’s sub-divided apartment residents. Employing the Pei Ho Street market in Sham Shui Po as a case study, an importance-performance analysis highlights critical but underperforming factors including public facilities, activity venues, commerce, and employment. Based on these findings, the study proposes four improvement strategies for the renewal of street markets in high-density communities, which could apply to other cities with similar high-density urban environments. It concludes that street markets in Hong Kong display unique spatial characteristics shaped by low-income lifestyles in a high-density urban environment, enriching daily social life and promoting sustainable community development.

Street markets play a vital role in revitalizing cities, especially within urban villages, which are unique neighborhoods characterized by dense migrant populations and complex social dynamics. This study addresses the underexplored relationship between street market vitality and the built environment, aiming to promote sustainable urban growth and inclusivity. A comprehensive framework embraces both macro-structural and micro-experiential perspectives, integrating urban morphology, functionality, and human perception indicators, to investigate the relationship between multidimensional vitality and the built environment in Shangxia Sha urban village street markets, Shenzhen, China. Utilizing multi-source data, geographic information systems, street view semantic segmentation, and space syntax, the study applies Geographically and Temporally Weighted Regression (GTWR) and Multiscale Geographically Weighted Regression (MGWR) models to explore spatiotemporal and multi-scale spatial heterogeneity. Findings reveal the pivotal role of urban functionality in shaping multidimensional vitality. Specifically, social vitality is most positively correlated with bus convenience and most negatively with street length. In terms of commercial vitality, point of interest (POI) density shows a strong positive relationship, while life service facility density is most negatively associated. This research provides insights into how the living street scenes shaped by low-income and migrant populations interact with the urban environment, offering guidance for fostering urban inclusivity and diverse communities.

Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. This study introduces a comprehensive, multi-perspective framework that integrates housing attributes, living convenience, competition, location, accessibility, and quantified perceptual metrics using multimodal machine learning. Advanced techniques, including XGBoost, SHAP, Partial Dependence Plots (PDPs), Interpretative Structural Modeling (ISM), and Bayesian Network (BN), capture nonlinearities, interactions, and hierarchical dependencies among rent determinants. Housing attributes and living convenience indicators exert the strongest cumulative influence on rents, while perceptual variables rank third, providing significant, threshold-dependent contributions and explaining up to 21.66% of rent variation. Notable interactions are identified between accessibility, facility density, and perceptual quality. The ISM–BN analysis uncovers multi-level pathways, demonstrating how both environmental features and human perceptions jointly influence rents. This framework offers actionable insights for equitable housing and urban planning policies, supporting data-driven decisions in complex urban rental markets.

Understanding spatial competition between retail formats is vital for sustaining urban commercial vitality. In China’s rapidly evolving urban economy, the contrast between light‑asset and capital‑intensive formats within traditional retail has been increasingly shaped by urban redevelopment and changing consumer behavior. However, prior studies largely confine themselves to linear assumptions, neglecting nonlinear dynamics and complex interdependencies. They also lack systematic comparisons between light-asset and capital-intensive formats, especially when attempting to integrate objective urban features with perceptual dimensions. This study investigates how objective urban environment indicators and human perception jointly shape the spatial density of two traditional retail formats, light‑asset (convenience stores) and capital‑intensive (large-scale supermarkets and shopping malls), in Shenzhen, China. Using multi‑source geospatial data and an integrated framework combining XGBoost, SHAP, Partial Dependence Plots (PDP), Interpretive Structural Modeling (ISM), and Bayesian Networks (BN), we quantify nonlinear effects, interaction patterns, and probabilistic association pathways. Results reveal distinct locational logics: light‑asset retail thrives through widespread penetration, responding strongly to competition intensity, service‑function proximity, and perceptual synergies; capital‑intensive retail concentrates in high‑yield hubs, driven by facility networks, accessibility, and high‑quality aesthetic environments above defined thresholds. Human perception emerges as a core determinant, accounting for up to 28.95% of explanatory importance, often exerting threshold‑dependent effects. The ISM–BN analysis uncovers multi‑entry vs. sequential pathways to spatial clustering across formats. Findings advance retail location theory by linking built‑environment metrics with street-level perceptual attributes, offering actionable guidance for urban planners to tailor format‑specific, perception‑oriented, and threshold‑sensitive strategies for sustainable retail ecosystems.