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 in...
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MDPI AG
2025-04-01
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| Series: | World |
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| Online Access: | https://www.mdpi.com/2673-4060/6/2/54 |
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| author | Yen-Khang Nguyen-Tran Aliffi Majiid Riaz-ul-haque Mian |
| author_facet | Yen-Khang Nguyen-Tran Aliffi Majiid Riaz-ul-haque Mian |
| author_sort | Yen-Khang Nguyen-Tran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-086734bba2034e1e8d5ecab9a3d851af |
| institution | Kabale University |
| issn | 2673-4060 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World |
| spelling | doaj-art-086734bba2034e1e8d5ecab9a3d851af2025-08-20T03:26:56ZengMDPI AGWorld2673-40602025-04-01625410.3390/world6020054Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space AttractivenessYen-Khang Nguyen-Tran0Aliffi Majiid1Riaz-ul-haque Mian2Interdisciplinary Faculty of Science and Technology, Shimane University, Matsue 690-0823, JapanInterdisciplinary Faculty of Science and Technology, Shimane University, Matsue 690-0823, JapanInterdisciplinary Faculty of Science and Technology, Shimane University, Matsue 690-0823, JapanRevitalizing 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.https://www.mdpi.com/2673-4060/6/2/54visual attractivenessspatial perceptionnatural language processing (NLP)data-driven insightstemporary event space |
| spellingShingle | Yen-Khang Nguyen-Tran Aliffi Majiid Riaz-ul-haque Mian Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness World visual attractiveness spatial perception natural language processing (NLP) data-driven insights temporary event space |
| title | Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness |
| title_full | Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness |
| title_fullStr | Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness |
| title_full_unstemmed | Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness |
| title_short | Data-Driven Spatial Analysis: A Multi-Stage Framework to Enhance Temporary Event Space Attractiveness |
| title_sort | data driven spatial analysis a multi stage framework to enhance temporary event space attractiveness |
| topic | visual attractiveness spatial perception natural language processing (NLP) data-driven insights temporary event space |
| url | https://www.mdpi.com/2673-4060/6/2/54 |
| work_keys_str_mv | AT yenkhangnguyentran datadrivenspatialanalysisamultistageframeworktoenhancetemporaryeventspaceattractiveness AT aliffimajiid datadrivenspatialanalysisamultistageframeworktoenhancetemporaryeventspaceattractiveness AT riazulhaquemian datadrivenspatialanalysisamultistageframeworktoenhancetemporaryeventspaceattractiveness |