Old residential communities are integral parts of urban areas, with their environmental quality affecting residents’ well-being. Spontaneous informal spaces (SIS) often emerge within these communities. These are predominantly crafted by the elderly using discarded materials and negatively impact the environmental quality of communities. Understanding SIS emergence patterns is vital for enhancing the environmental quality of old communities; however, methodologies fall short in terms of the quantification of these emergence patterns. This study introduces a groundbreaking approach, merging drone oblique photography technology with space syntax theory, to thoroughly analyze SIS types, functions, and determinants in five Tianjin communities. Utilizing drones and the Depthmap space syntax tool, we captured SIS characteristics and constructed topological models of residences and traffic patterns. We further explored the intrinsic relationships between architectural layout, road traffic, and SIS characteristics via clustering algorithms and multivariate correlation analysis. Our results reveal that architectural layout and road traffic play decisive roles in shaping SIS. Highly accessible regions predominantly feature social-type SIS, while secluded or less trafficked zones lean towards private-type SIS. Highlighting the elderly’s essential needs for greenery, interaction, and basic amenities, our findings offer valuable insights into the revitalization of outdoor spaces in aging communities, into the fostering of urban sustainability and into the nurturing of a balanced relationship between humans and their surroundings.

In the context of accelerating worldwide urbanization and ecosystem decline, deciphering the interactions between landscape patterns and habitat quality is essential for biodiversity preservation, particularly within ecologically sensitive zones like the Yellow River Basin. This research investigates the spatiotemporal dynamics, spatial linkages, and nonlinear relationships connecting landscape patterns and habitat quality across the basin. Utilizing land use datasets spanning 1980–2023, we combined the InVEST model, landscape pattern indices, spatial autocorrelation analysis, the XGBoost algorithm, and SHAP interpretability methods. The results show that: (1) Landscape patterns underwent a clear transition around 1995, shifting from regularization and connectivity toward fragmentation and heterogeneity, evidenced by increases in PD, LSI, and SHEI, alongside decreases in LPI and CONTAG. (2) Mean habitat quality progressively declined, exhibited a spatial distribution characterized by “higher in the west, lower in the east.” Low-quality habitat areas expanded from 2.12% to 3.76%, whereas high-quality areas decreased from 23.12% to 22.45%, with better habitats largely maintained in western headwaters and the Qinling Mountains. (3) Significant spatial correlations were observed: LPI positively correlated with habitat quality, while PD, LSI, SHEI, and CONTAG showed negative correlations. Two dominant spatial aggregations emerged—namely “high connectivity–high quality” in the west and “high fragmentation–low quality” in the east. (4) CONTAG was identified as the dominant factor influencing habitat quality, with all landscape indices exhibiting distinct threshold effects. The proposed framework, which integrates spatial statistics, machine learning, and interpretability methods, offers a novel approach for deciphering complex ecological processes. Moreover, the identified thresholds and zonal management strategies offer a scientific foundation for ecological conservation and spatial planning in the Yellow River Basin and other vulnerable river systems worldwide.

Under the dual pressures of heritage conservation and tourism growth, improving inclusive access to cultural tourism opportunities in historic urban areas has become an urgent planning issue under Sustainable Development Goal 11 and the Historic Urban Landscape approach. Taking the central urban area of Suzhou, China, as a case study, this study evaluates time-budgeted walkable accessibility, spatial equity, local mismatch, and accessibility-generating conditions from a 15 min city perspective. An integrated analytical framework was developed by combining kernel density analysis, GIS-based network accessibility modelling, Lorenz–Gini equity assessment, bivariate Local Indicators of Spatial Association (LISA), and XGBoost–SHAP interpretation. The results show that cultural tourism opportunities exhibit a clear core polarisation–peripheral attenuation pattern. Within the 15 min threshold, Gusu District records SACR and AAR values of 80.18% and 95.23%, respectively, indicating a pronounced historic-core accessibility advantage. Accommodation-tier differences do not form a simple monotonic relationship with accessibility, but are shaped by the spatial embedding of different accommodation-market segments within the cultural tourism opportunity field. HL units, namely high-tier accommodation near low accessibility, emerge as priority diagnostic areas of local mismatch, while delayed accessibility beyond 30 min becomes particularly evident among elderly visitors. The SHAP interpretation further indicates that leisure-strolling attractions show a more balanced supply–accommodation structure, whereas commercial–cultural mixed and heritage-core attractions are more strongly supply-led. By linking accessibility measurement, equity assessment, local mismatch diagnosis, and mechanism-based explanation, this study provides an operational basis for zonal and typology-oriented optimisation of cultural tourism accessibility in historic urban areas.