Traditional Jiangnan water towns in China serve as important cultural heritage sites and tourist destinations. Existing studies have revealed a potential connection between environmental elements and spatial perception in these towns. However, there remains a lack of research systematically investigating whether and how these environmental elements influence subjective evaluation indicators, such as spatial attractiveness, and the mechanisms underlying the interactions between these elements. To further understand these mechanisms, we used Nanxun Old Town as our study site, employed computer vision techniques to perform semantic segmentation on street-view images, extracted the visual proportions of environmental elements, and conducted quantitative correlation analysis with subjective attractiveness evaluations. The findings indicate that different environmental elements in water towns shape spatial imagery in diverse ways, thereby influencing perceived attractiveness. Firstly, though space-defining elements such as buildings and water generally contribute positively to perceived attractiveness, their proportions should be controlled within a reasonable range to maintain a spatial scale that aligns with the traditional imagery of water towns. Secondly, foreground elements like boats and lanterns, although occupying a smaller proportion, can effectively enhance the space when properly combined. Finally, the influence of elements such as bridges and buildings depends on the specific viewing distance and angle. These findings, based on an interpretable analytical framework, reveal that the effects of environmental elements on spatial attractiveness are context-dependent and nonlinear, varying with their proportions, combinations, and perspectives. This approach offers a more comprehensive understanding of the mechanisms by which environmental elements shape spatial attractiveness, providing a scientific foundation for regulating key visual components and optimizing spatial composition for sustainable traditional water town environment management.

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments.

Timber heritage structures require systematic assessment that integrates multiple methods. This systematic review examines peer-reviewed studies on field-applied assessment of timber heritage structures, excluding laboratory-only tests. Based on 91 papers from the Web of Science database, this study establishes a Setting–Scope–Scheme framework to clarify relationships among assessment targets, conditions, purposes, methods, and indicators. In addition, 124 extended abstracts from the Architectural Institute of Japan literature database are reviewed as supplementary materials for region-specific comparative analysis. The results show that assessment practices are shaped by regional contexts, structural typologies, and environmental conditions. Assessment indicators also show hierarchical and networked characteristics across system, component, and joint levels, involving functional, conditional, and environmental-interaction coupling. Technological integration, including long-term monitoring, numerical simulation, and BIM, supports multi-indicator assessment and less invasive conservation practice. Although limited by database coverage and heterogeneity in indicator reporting, this review provides a structured basis for context-adaptive assessment strategies.