Against the global imperative to address climate change and accelerate energy transitions, the rapid growth of the electric vehicle (EV) industry has turned public charging infrastructure into a key foundation of urban operations, driven by carbon peaking and carbon neutrality goals. However, effective supply depends not only on scale but on deep association with urban functional spaces. This study compares Gusu District (a historic preservation district) and Industrial Park District (a new development district) in Suzhou. The goal is to reveal how public electric vehicle charging stations (EVCSes) associate with functional spaces under different urban development models. The study employs Standard Deviational Ellipse (SDE), Kernel Density Estimation (KDE), Bivariate Spatial Autocorrelation, and Coupling Coordination Degree (CCD) models to compare layout patterns, clustering features and functional association. The research findings are as follows: (1) The EVCS layout in Gusu District shows strong dependence on roads and administrative boundaries, while EVCSes in Industrial Park District show clear planning intervention, less constrained by its administrative boundary. (2) KDE analysis confirms that Gusu District has continuous clustering centered on the ancient city, but Industrial Park District shows a multi-center layout. (3) Bivariate Spatial Autocorrelation reveals different priorities in facility allocation. In Gusu District, spatial association is mainly driven by high-mobility nodes, while in Industrial Park District, EVCSes are more deeply embedded in social services and daily life scenarios. (4) CCD analysis reveals that the coordination in Gusu District forms a monocentric, spatially continuous gradient centered on the ancient city, whereas in Industrial Park District it displays a polycentric but fragmented pattern, with high coordination areas confined to planned cores. This comparative study reveals the EVCS spatial layout, which is shaped by both administrative boundaries and policy constraints, and the heterogeneity in spatial association between two districts. It provides scientific evidence and decision support for different spatial governance and facility optimization in various types of urban areas.

Street vending plays an important role in supporting urban livelihoods and everyday consumption, while also posing challenges for inclusive urban governance. However, existing governance approaches often overlook the heterogeneity of street vendors and rely on standardized interventions. This study investigates vendor heterogeneity and its implications for inclusive governance based on questionnaire surveys conducted among street vendors in Suzhou, China. An integrated framework combining cluster analysis, multinomial logistic regression, multiple correspondence analysis, and structural equation modeling is developed to identify vendor profiles, explore their socio-economic associations, and examine satisfaction mechanisms. The results reveal substantial heterogeneity among street vendors. Four behavioral profiles are identified, namely High-Income Stable Vendors, Emerging Vendors, Low-Income Stable Vendors, and Long-Term Flexible Vendors, reflecting differences in operational stability, economic returns, and engagement patterns. Four preference-based profiles are further identified, including Low Demand Vendors, Rent-Support Oriented Vendors, Site-Environment Oriented Vendors, and Market-Order Oriented Vendors, demonstrating differentiated governance needs regarding operational support, physical conditions, and management arrangements. Further analyses demonstrate that vendor characteristics and socio-economic conditions are closely associated with profile membership, while physical and social environmental factors significantly influence business satisfaction. Moreover, the effects of environmental conditions on satisfaction differ across behavioral groups, indicating that vendors with different operational modes respond differently to urban environments and governance interventions. These findings suggest that street vendors should not be treated as a homogeneous group in the pursuit of socially sustainable urban development. Instead, inclusive governance requires differentiated strategies that align spatial support, management arrangements, and policy interventions with the diverse needs of vendor groups. This study contributes to sustainable urban governance by highlighting the importance of incorporating livelihood diversity into the planning and management of informal economic activities.

Commercial spaces are essential urban environments where consumer movement is inherently sequential, involving multiple destinations and complex spatial interactions. However, existing studies mainly rely on aggregated flow data and provide a limited understanding of how consumers organize their movements into complete spatial sequences. To address this limitation, this study proposes a framework based on typical routes to investigate sequential movement patterns in commercial spaces. Typical routes are conceptualized in two complementary ways: as representative routes extracted from observed traces and as highly probable routes generated from sequential destination choices. Accordingly, two approaches are integrated. First, route clustering is conducted using Levenshtein-ratio-based similarity measurement and affinity propagation (AP) clustering to identify representative route exemplars with distinct sequential structures. Second, route prediction is performed by combining discrete choice models (DCM), convolutional neural networks (CNN), and beam search to generate high-probability movement sequences. The framework is applied to 323 observed consumer routes collected from a large commercial complex in Shanghai, China. The results reveal several recurring movement patterns, such as task-oriented, fashion-oriented, dining-oriented, and family-oriented routes. Among them, the most dominant pattern is characterized by repeated movement between anchor stores through sequences of intermediate, smaller shops, highlighting the organizing role of anchor destinations in structuring consumer circulation. Both clustering and prediction demonstrate that consumer movement in commercial spaces is not random, but follows relatively stable sequential logics. In the prediction task, CNN achieves substantially higher accuracy than DCM, thereby providing stronger support for route simulation. Overall, the study demonstrates that integrating route clustering and route prediction provides a useful framework for understanding, interpreting, and simulating sequential consumer movement in commercial environments, with practical implications for circulation organization, anchor-store layout, and commercial space design.

Cultural heritage conservation planning in China advocates for differentiated planning measures tailored to different heritage elements with diverse values, functions, and locations. However, limited research has focused on the spatial heterogeneity of these multi-dimensional planning measures and the subjective well-being (SWB) of residents within these protected historic districts. This study investigates the spatial differentiation pattern and mechanism of residents’ SWB in three Historic Conservation Areas of Yangzhou, China, by employing a combination of spatial data digitization and data spatialization methods. The findings reveal: (1) All three cases demonstrate notable spatial differentiation in terms of residents’ SWB; (2) A common feature across the three cases is that strengthening community participation, tourism traffic control, and housing improvement projects can significantly improve residents’ SWB. But proximity to historic buildings has little explanatory power for the spatial differentiation of residents’ SWB; (3) Planning factors of traffic accessibility, public facility accessibility, park service areas, and NIMBY areas of public toilets, have significant spatial heterogeneous effects on residents’ SWB across the three cases, which are closely correlated to the varying degrees of tourism development within each district. The findings of this study provide targeted planning strategies for historic districts with different functional orientations and heritage conservation duties, aimed at more effectively enhancing the well-being of heritage site residents by utilizing limited public resources.

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents.

The mismatch between nursing home provision and older adults’ preferences has become a critical challenge for urban planning, yet limited attention has been paid to how different nursing home attributes are evaluated and whether such preferences vary across population groups. This study investigates heterogeneity in nursing home preferences and its planning implications through a stated choice experiment conducted among pre-elderly and older adults in central Shanghai, China. The analysis was based on 215 valid questionnaires and 2813 choice observations. A multinomial logit model was first employed to identify overall preferences for nursing home attributes, followed by panel latent class models to reveal heterogeneous preference patterns and examine their associations with demographic, socioeconomic, and family characteristics. The estimated preference parameters were further applied to evaluate 109 existing nursing homes in Pudong New Area from both overall and group-specific perspectives. The results indicate that location and healthcare accessibility are the two most influential attributes in the sample’s stated nursing home preferences, followed by care service quality, transportation accessibility, and air quality. However, substantial preference heterogeneity is observed across respondents. The joint latent class model identifies two preference classes: a trade-off class showing relatively moderate resistance to suburban locations and a location-anchored class exhibiting substantially stronger resistance to outer-suburban locations. Age is the most robust individual correlate of class membership, with older respondents more likely to belong to the location-anchored class. Living with children shows a weaker association, whereas gender, education, income, and number of children are not significant in the joint specification. The preference-based evaluation further demonstrates that existing nursing homes vary in their suitability across different preference groups. These findings highlight the importance of incorporating preference heterogeneity into nursing home planning and provide empirical evidence for improving the matching between institutional care supply and diverse care needs.