Transportation network expansion has accelerated landscape fragmentation, disrupting ecological connectivity across regions. While wildlife crossing structures offer significant ecological and safety benefits, strategic siting decisions remain largely reactive rather than evidence based. This study presents an integrated geospatial and machine learning framework to predict wildlife crossing suitability at landscape scale and identify areas of unmet demand where ecological need is high, yet infrastructure is absent. We developed a methodology combining five national datasets (species density, population density, natural trails, road networks, and existing WCS inventory) standardized into a unified 10 × 10 km grid covering Spain. After applying ecological assumptions to filter zero-presence cells and outlier variable ranges, we trained four supervised machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Machine, and Support Vector Regression) using spatial cross-validation on 1,573 grid cells representing 28% of the complete dataset. Model performance was evaluated using RMSE, MSE, MAPE, and residual diagnostics. The Gradient Boosting Machine emerged as the optimal model, achieving test RMSE = 3.113 and MAPE = 2.103%, with homogeneous error distribution across prediction domains. Applied to Spain's complete territory, predictions identified critical and high-priority zones requiring immediate crossing infrastructure investment. Results demonstrate that fragmentation in Spain exhibits two distinct patterns: structural vulnerability forming continuous barriers in the northern plateau, requiring large-scale corridor restoration; and dispersed fragmentation in the south and east, demanding targeted micro-interventions. This framework provides policy-ready spatial guidance for infrastructure investment prioritization, translating predictive models into actionable design and conservation decisions. The methodology is transferable to other regions with comparable open-access geospatial data, supporting transdisciplinary and landscape-scale connectivity planning.

This paper develops, tests and expands a conceptual framework for digital placemaking tailored to the complex realities of informally structured cities in the Global South. As digital tools become increasingly prominent in urban planning, their deployment often reflects technocentric models rooted in Global North contexts. These approaches tend to prioritize optimization and datafication, neglecting the informal spatial practices and limited civic trust that shape many urban environments.

Grounded in participatory urbanism, this study proposes approaching digital placemaking as a relational and ethical process. To operationalize this framework, a layered, mixed-methods study was conducted with two groups: design-literate students and everyday users of informal micro-public spaces.

Findings reveal that despite near-universal smartphone access, everyday users' engagement with digital civic platforms remains low. This highlights that primary barriers are not technical but rather stem from poor platform legibility and a lack of institutional trust. Furthermore, the data confirmed a persistent epistemic divide: while students overestimated public readiness for digital tools, everyday users engaged more openly in familiar, informal settings.

To address these gaps and operationalize hybrid engagement models, the framework is refined with four new dimensions: participatory legibility, spatial immersion for designers, feedback visibility, and the integration of embedded intermediaries. This research contributes to a contextually grounded, empirically tested model for designing inclusive civic technologies responsive to Global South realities.

University campuses function as daily living environments where the built environment shapes physical activity, mental well-being, and social connection. Yet most campus planning systems lack explicit public health performance criteria that link environmental quality to measurable health outcomes. OBJECTIVE: This paper develops the Outdoor Wellness–Oriented Design Strategy (OWODS), a measurable and scalable preventive-health framework that integrates Logic Model (LM) causal reasoning with the Health-Oriented Environment for Active Living Score (HEALS) to evaluate and guide outdoor environments as components of preventive health infrastructure within campus planning systems. The study addresses three questions: (1) how campus environments can be assessed using standardized health-oriented indicators; (2) how LM clarifies causal pathways linking environmental quality to health outcomes; and (3) how integrating HEALS and LM within OWODS creates a scalable framework for guiding campus planning and preventive health design. METHODOLOGY: A case study at a large public university (>70,000 population) applied HEALS scoring across seven outdoor locations, evaluating shading, safety, pedestrian accessibility, noise exposure, and social-support infrastructure, and mapped findings onto the LM pathway to examine alignment between environmental performance and preventive-health objectives. ACHIEVED OUTCOMES: OWODS demonstrates how measurable built-environment performance patterns correspond to defined behavioral pathways associated with active living, mental well-being, and chronic disease prevention. By integrating HEALS indicators with the Logic Model’s outcome and impact stages, the framework clarifies how environmental design, planning standards, and institutional governance can be systematically aligned to support preventive health objectives. The model shows strong transferability across university campuses and adaptability to broader community outdoor environments, offering a locally actionable framework with global relevance for advancing preventive-health strategies through the built environment.