Work

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

Year: 2025

Type: article

Abstract: Revitalizing Japan’s remote areas has become an urgent challenge, particularly in regions with ageing 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.On the other hand, recent advancements in natural language processing (NLP) and AI, particularly models like ChatGPT-4, have significantly enhanced data-driven decision-making. However, a major challenge in multi-person and multi-event analysis remains the need for structured user metadata, which is a critical gap. By integrating visual segmentation, spatial perception analysis, and NLP, this research introduces a novel, scalable approach to refining the design of temporary event spaces, using Matsue City as a case study.From an architectural perspective, this 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.On the data-driven side, a specialized model has been developed to process multi-person interview data, addressing the complexities of analyzing multi-dimensional datasets. Furthermore, advancements in keyword extraction algorithms improve the identification of shared themes from interviews, enabling more accurate and insightful analysis 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.

Source: World

DOI: 10.3390/world6020054

Authors: Yen-Khang Nguyen-Tran, Aliffi Majiid, Riaz-Ul-Haque Mian

Institutions: Shimane University, University of Shimane

Language: English

Topic: Urban Development and Cultural Heritage

Subfield: Urban Studies

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,207