Hospital gardens are increasingly recognized as therapeutic environments; however, many Post-Occupancy Evaluation (POE) studies remain limited to perception-based assessments without explicitly linking spatial characteristics to user experience. This study develops an evidence-based evaluation framework integrating spatial analysis with user-centered data. A mixed-methods approach was employed, combining systematic observations, spatial mapping, and a structured short-form questionnaire administered to 350 users. The study area was classified into three micro-climatic zones—hot, moderate, and cool/shaded—based on solar exposure, vegetation density, and surface characteristics. User experience was evaluated through composite indices of comfort, satisfaction, and perceived restorativeness (Cronbach’s α = 0.83). The results indicate a clear spatial gradient: mean comfort scores increased from 2.8 in hot zones to 3.5 in moderate zones and 4.2 in cool/shaded areas. These differences were statistically significant (p < 0.05) and supported by meaningful effect sizes (Cohen’s d). Furthermore, multiple linear regression analysis demonstrated that natural components—specifically mature vegetation and clean air—are strongly associated with psychological recovery (β = 0.54, p < 0.001). Spatial analysis also revealed a mismatch between design configuration and environmental performance. The findings provide a transferable, spatially grounded framework for optimizing therapeutic landscapes in healthcare settings.

This study develops a GIS-based, heritage-sensitive urban lighting framework for Niğde, Türkiye, integrating Sentinel-2 MSI Level-2A imagery (10 m), ASTER DEM, and municipal cadastral data. Five spatial criteria—land cover, parks, protected heritage assets, population distribution, and government institutions—were classified through supervised mapping, visibility analysis, and architectural integrity assessment. All layers were standardized and combined using a weighted-overlay approach, supported by sensitivity testing across three weighting scenarios to ensure model robustness. Priority zones are concentrated in the historic core, where cultural landmarks, central parks, and high-density residential areas overlap. Peripheral agricultural and rural zones exhibited minimal lighting needs. Field verification and expert consultation demonstrated 82% correspondence between modeled and observed priority and visibility patterns, while a structured nighttime audit and ecological checklist provided additional empirical grounding for lighting sufficiency, glare risks, and biodiversity considerations. Results emphasize context-specific lighting that strengthens cultural identity, improves pedestrian comfort and nighttime legibility, and reduces unnecessary energy use and light pollution. This approach offers a replicable workflow aligned with CIE 150:2017 and IES RP-8-18 guidance. Future work may incorporate dynamic population mobility, AHP-based weighting, and adaptive smart-lighting systems to scale the methodology across similar medium-sized heritage cities seeking balanced aesthetic, cultural, and ecological nighttime environments.

Urban parks can promote equitable access to recreation, nature and social interaction when their design responds to diverse physical, sensory, cognitive, social and environmental needs. However, existing park-accessibility assessments often focus on spatial proximity, regulatory compliance or individual facilities and rarely integrate these dimensions with climatic support within a transparent, multidimensional evaluation framework. Addressing this gap is important for identifying design conditions that may restrict equitable park use even where formal access is available. This study adapts and extends the Park Inclusive Design Index as the Enhanced Park Inclusive Design Index (E-PIDI) and applies it comparatively to two intentionally selected, very large multifunctional parks in Ankara, Türkiye. The framework comprises 38 indicators across Motor Accessibility, Sensory Perceptibility, Cognitive and Wayfinding Support, Social Inclusivity and Thermal Comfort Potential. Two authors independently completed the field ratings, and their unrounded arithmetic means were used in the analysis; pooled linearly weighted Cohen’s kappa was 0.675. Dimension weights were derived from pairwise comparisons completed by eight experienced academics using the Analytic Hierarchy Process. The aggregated matrix was highly consistent (CR = 0.0177). Motor Accessibility received the highest weight (38.09%), followed by Cognitive and Wayfinding Support (22.31%), Social Inclusivity (22.29%), Thermal Comfort Potential (9.35%) and Sensory Perceptibility (7.95%). The National Botanical Garden obtained a higher composite score (0.7393) than the Capital National Garden (0.6671), with higher Motor Accessibility, Sensory Perceptibility and Thermal Comfort Potential scores; Cognitive and Wayfinding Support and Social Inclusivity were equal. The ranking remained stable across equal, expert-specific, leave-one-expert-out and rater-specific sensitivity scenarios. The application demonstrates E-PIDI’s use as an intensive diagnostic comparison tool for large, spatially complex parks, complementing its potential use for broader multi-park benchmarking.

Metropolitan flood-resilience planning requires understanding how development and blue–green landscapes intersect with low-lying terrain. This study compared flood-susceptibility contexts in Shanghai, mainland Tokyo, and Istanbul using a harmonized geospatial framework integrating 2021 land-cover and impervious-surface baselines with elevation, hydrography, surface water, 2020–2025 satellite rainfall, and administrative population data. The analysis distinguished landscape coverage, vegetation configuration, proximity to development, and development–terrain overlap. Of the impervious surface mapped for 2021, 96.31% occurred below the elevation product’s 10 m threshold in Shanghai, compared with 23.24% in Tokyo and 4.12% in Istanbul. In Tokyo, this share exceeded the metropolitan low-elevation share of 15.76%, revealing a concentration of development in lowlands obscured by citywide terrain summaries. Shanghai had less non-agricultural vegetation than Istanbul (25.15% versus 64.50%), yet greater built-up proximity to vegetation (94.27% versus 89.10% within 100 m). Despite this proximity, only 23.04% of Shanghai’s vegetation qualified as interior vegetation, compared with 79.60% in Tokyo and 82.06% in Istanbul. Within-city analysis at 2 km showed strong vegetation coverage–adjacency associations (r = 0.803–0.936), alongside substantial variation in adjacency and built-up proximity within comparable coverage bands; association directions persisted at 5 km. Shanghai’s widespread low-elevation development calls for finer differentiation of drainage and storage conditions, whereas Tokyo’s terrain contrast supports distinguishing lowland development from upland areas. Joint assessment of blue–green structure and development–terrain overlap thus provides a basis for targeting detailed drainage assessment and sustainable metropolitan landscape planning.