Abstract
<title>Abstract</title> <p>The transition toward data-driven smart cities increasingly relies on an integrated sensing-communication-computing continuum—a paradigm shift that embeds intelligent connectivity directly into the physical urban fabric. However, achieving pervasive and capillary coverage in morphologically complex environments remains a major challenge, as traditional wireless planning often treats signal dynamics in isolation from urban spatial analytics. To bridge this gap, this work presents a context-aware urban data science framework designed for adaptive infrastructure planning in dense built environments. Utilizing an open-source software pipeline, the proposed methodology fuses high-resolution spatial datasets—including building footprints, elevation models, and street morphology—with advanced radio propagation analytics. The framework’s spatial simulation engine models complex urban clutter, diffraction, and Line-of-Sight transitions directly derived from physical city geometries. The proposed framework is validated using a dense historic center as a benchmark case study, effectively predicting optimal gateway placement and candidate site selection before physical deployment. By transforming heterogeneous spatial open data into actionable infrastructure insights, this study demonstrates how urban informatics can optimize the spatial deployment of next-generation digital coverage in complex urban landscapes.</p>