Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness

Revitalizing Japan’s remote areas has become an urgent challenge, particularly in regions with aging populations. Despite their rich cultural and natural resources, these areas struggle to attract younger demographics, including young families and children. To address this, local governments have introduced temporary events to enhance urban vibrancy and create inclusive spaces. However, research on optimizing event design faces significant challenges due to the vast amount of data required for comprehensive analysis, making it difficult to gain deeper insights into user experience. Recent advancements in natural language processing (NLP) and AI have opened new possibilities for analyzing large-scale, multi-person interview data. While models like ChatGPT-4 have enhanced data-driven decision-making, structuring user metadata and identifying shared themes across events remain key challenges. This research integrates visual segmentation, spatial perception analysis, and NLP-driven keyword extraction into a novel, scalable approach. Using Matsue City as a case study, the method enhances the visual attractiveness of temporary event spaces by optimizing spatial layout, product visibility, and user engagement, ensuring they remain appealing and inclusive despite demographic challenges. From a data perspective, the proposed model improves the analysis of complex qualitative datasets and supports a more accurate interpretation of public event experiences. This integrated approach not only bridges spatial design and participant engagement but also establishes a replicable AI-assisted framework for systematically enhancing temporary event spaces, overcoming current limitations in large-scale data processing.