Weekday commuting and weekend recreation are two mobility domains through which urban spatial structure shapes residents’ well-being and urban functioning, yet direct empirical evidence on how they are related remains limited. This study investigates how weekday commuting costs and weekend recreational mobility conditions are related within a jobs–housing–recreation spatial framework, using individual-level location-based services (LBS) data from the central urban area of Chongqing, China. Generalized additive models reveal a nonlinear and range-dependent commuting–recreation relationship. Distance-based and driving-time specifications provide the main evidence for a U-shaped relationship, whereas transit-time specifications do not clearly reproduce this pattern, reflecting short-distance cost overestimation and spatially shared public-transport constraints rather than realised mobility conditions. From a spatial-configuration perspective, this pattern suggests that work-related and recreational mobility conditions are unevenly combined across residential locations, rather than simply aligned or opposed. It also suggests that relatively favourable commuting and recreational mobility conditions can coexist within some residential contexts. Rather than establishing a universal rule, the Chongqing case provides a testable hypothesis that may be relevant to large cities with uneven and partially aligned employment, housing, transport, and recreational opportunities. The study provides an empirical entry point for integrated spatial-performance diagnosis and future evaluation of alternative jobs–housing–recreation configurations.

Urban road networks (URNs) underpin critical urban functions ranging from public service provision to emergency response. However, URN resilience is commonly assessed using aggregate performance metrics or critical-element identification, which offers limited insight into how disruption reshapes spatial accessibility. This limitation is increasingly salient under stock-based urban development, where opportunities for large-scale physical network reconfiguration and segment-level engineering interventions are constrained, and resilience enhancement increasingly depends on facility-based adaptation. To address this gap, drawing on graph theory and percolation theory, this study proposes a disconnection-pattern-based (DPB) analytical approach for mapping spatial configurations of URN vulnerability. Two generic disconnection patterns derived from topological limits of network redundancy are conceptualized: Local Island Disconnection (LID) and Global Structural Fragmentation (GSF). Corresponding quantitative mapping methods are developed and applied to cities with contrasting URN morphologies. Results show that spatial configurations of connectivity vulnerability can be systematically mapped across heterogeneous URNs, yielding spatially explicit information critical to resilience-oriented facility siting. By treating vulnerability as a spatial configuration rather than a single-state metric, the proposed approach extends URN resilience assessment toward facility-planning strategies that adapt to existing road-network risk configurations under stock-based development.

As urban disasters intensify, the relationship between urban spatial form and disaster risk is increasingly important. Different spatial configurations reflect varying levels of resilience to disasters. However existing research offers limited quantitative evidence linking spatial form indicators and disaster risk indices. This study addresses this gap by developing a quantifiable, city-scale framework to analyze the form–risk relationship across 32 Chinese cities. Urban spatial form is quantified using fractal dimension to measure boundary complexity and compactness to assess internal structure, supplemented by a diagrammatic classification of urban patterns. A comprehensive disaster risk index is developed based on four dimensions: hazards, exposure, vulnerability, and resilience. Regression analysis is then applied to quantify the direction and magnitude of correlations between spatial-form indicators and the comprehensive risk index. The results reveal three major findings: (1) Disaster risk increases with fractal dimension, indicating that cities with more complex and irregular boundaries tend to be more vulnerable. In contrast, compactness has no statistically significant effect on disaster risk. (2) Spatial patterns are strongly associated with risk levels: cluster-type and block-type cities generally experience lower risks than radial-type and constellation-type cities. (3) City size and geography also influence risk, as larger cities typically exhibit lower risks, whereas southern cities face higher risks than those in northern regions. These results highlight the critical role of urban spatial structure in shaping disaster resilience. Managing boundary complexity, fostering polycentric and block-based spatial layouts, and improving road-network redundancy can effectively enhance urban adaptive capacity. These insights provide theoretical foundations and practical guidance for resilience-oriented spatial optimization and disaster-risk reduction in vulnerable cities.

Against the backdrop of climate change and rapid urbanization, assessing urban flood resilience requires spatially continuous and interpretable approaches capable of capturing nonlinear interactions between natural and human systems. This study proposes a high-resolution framework for mapping urban flood resilience in the built-up areas of Chongqing, China, grounded in the geography–ecology–society–infrastructure systems (GESIS) concept. A Flood Resilience Index is constructed at a 50 m grid resolution using ten core indicators and objective weighting based on combined entropy and coefficient-of-variation methods. Three machine learning models—multilayer perceptron (MLP), random forest, and XGBoost—are then trained to reproduce the resilience surface by integrating these indicators with additional historical flood-exposure variables, with SHAP used for model interpretation. The MLP model achieves the best performance (R² ≈ 0.78) and generates spatially coherent resilience patterns. Impervious surface fraction and building density exert dominant negative effects, whereas elevation and ecological connectivity contribute positively. The results reveal pronounced nonlinear thresholds in key drivers, indicating that flood resilience cannot be inferred from monotonic factor effects alone. By combining objective weighting, explainable machine learning, and historical exposure information, this framework supports both accurate prediction and policy-relevant interpretation of urban flood resilience for sustainable urban planning in mountainous megacities.

Weekday commuting and weekend recreation are two mobility domains through which urban spatial structure affects residents’ daily experience and urban functioning. Although commuting and recreational mobility are theoretically related, whether their spatial relationship creates scope for coordinated improvement in both domains remains insufficiently understood. A preceding study in Chongqing identified a U-shaped relationship between weekday commuting costs and weekend recreational mobility costs at the individual level, but its proposed spatial-structural interpretation remained untested. This study examines whether the same relationship emerges across residential locations and whether the resulting mobility-cost structure corresponds to the spatial configuration of employment and recreational opportunities. Using location-based services data from the central urban area of Chongqing, individual mobility records were aggregated to residential grids and analysed through a three-stage framework examining the grid-level U-shaped relationship, multivariate dependence between the opportunity and cost structures, and their curve-based correspondence. The U-shaped relationship was reproduced at the grid level. The opportunity and cost structures exhibited multivariate dependence, and the curve-based analysis showed a positive correspondence between residential grids’ positions relative to the fitted opportunity and cost reference points. These findings support the interpretation that the U-shaped relationship partly reflects how residential locations are positioned relative to multiple employment centres and spatially distributed recreational destinations. The findings indicate that commuting and recreational mobility should be considered jointly in jobs–housing–recreation planning and suggest potential scope for coordinated improvement across both domains.