A Justified Plan Graph Analysis of a Typical Apartment in China: Focusing on Interior Traffic Spaces and a Binary Filtration System for Neural Network

In the situation of shifting urban residential needs in China, existing studies overlook both interior space redesign of ordinary apartments and the integration methodology of space syntax and artificial intelligence in this domain. This study aims to optimize residential space utilization and advance AI-driven design by analyzing interior traffic spaces. It applies the justified plan graph (JPG) method of space syntax to a typical three-bedroom apartment with its seven configurations and introduces a binary filtration system for AI, identifying an L-shaped multifunctional core interior traffic space and filtering valid designs from all possible binary ones. Findings show that integrating JPG and binary filtration offers novel insights for AI deep learning in spatial design.