Isovist graph generating method for structural expression of a floor plan

This study explores enhancing spatial analysis by integrating Space Syntax with deep learning methods for images and graphs. While Space Syntax traditionally focuses on geometry and topology, it overlooks visual data like texture and color. As image content varies by location, capturing spatial context is essential, especially in irregular or open spaces where identifying central points is challenging. To address this, the study introduces the concept of an “isovist graph” - a spatial model that minimally covers a closed plane with isovists while preserving centrality and connectivity. A method is proposed to compute this model rigorously in discrete settings. Experiments show that exact solutions can be efficiently derived for many spatial cases, aligning with the intended objectives.