An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data
Abstract Urban parks provide essential benefits that enhance human well-being and quality of life. This study integrates big data (online reviews, images) and small data (survey results) with large language models (LLMs) and object detection algorithms to analyze public perceptions of urban parks in...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Discover Cities |
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| Online Access: | https://doi.org/10.1007/s44327-025-00075-1 |
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| author | Siqi Lai Brian Deal |
| author_facet | Siqi Lai Brian Deal |
| author_sort | Siqi Lai |
| collection | DOAJ |
| description | Abstract Urban parks provide essential benefits that enhance human well-being and quality of life. This study integrates big data (online reviews, images) and small data (survey results) with large language models (LLMs) and object detection algorithms to analyze public perceptions of urban parks in Stockholm, New York, and Shanghai. LLMs were employed for sentiment analysis and keyword extraction from 62,724 online reviews, enabling the identification of key environmental features influencing visitor satisfaction. Concurrently, the YOLO v11 object detection model analyzed 111,469 images to quantify the presence of natural and built features, such as greenery, water bodies, and recreational facilities. Findings reveal that online satisfaction scores are lower than survey-based scores in Stockholm (0.661 vs. 0.774) and Shanghai (0.626 vs. 0.845), indicating a negativity bias in online reviews. In contrast, New York’s online (0.612) and survey (0.610) scores align closely, suggesting a broader representation of perspectives. Feature analysis shows greenery, flowers, and recreational facilities consistently enhance satisfaction, while noise and uncleanliness reduce it. Cultural differences emerge: small animals significantly improve perceptions in New York, whereas excessive liveliness lowers satisfaction in Shanghai due to crowding concerns. By examining 102 parks across these cities, this study highlights the effectiveness of AI-driven, multi-source data analysis in identifying universal and culturally specific park preferences. These insights can guide data-informed urban park management strategies, improving accessibility and visitor experiences globally. |
| format | Article |
| id | doaj-art-5e13122f74fc408ba996aaa3dee539e8 |
| institution | OA Journals |
| issn | 3004-8311 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Cities |
| spelling | doaj-art-5e13122f74fc408ba996aaa3dee539e82025-08-20T01:53:15ZengSpringerDiscover Cities3004-83112025-03-012112110.1007/s44327-025-00075-1An innovative approach to urban parks and perception: a cross-cultural analysis using big and small dataSiqi Lai0Brian Deal1Department of Landscape Architecture, University of Illinois at Urbana-ChampaignDepartment of Landscape Architecture, University of Illinois at Urbana-ChampaignAbstract Urban parks provide essential benefits that enhance human well-being and quality of life. This study integrates big data (online reviews, images) and small data (survey results) with large language models (LLMs) and object detection algorithms to analyze public perceptions of urban parks in Stockholm, New York, and Shanghai. LLMs were employed for sentiment analysis and keyword extraction from 62,724 online reviews, enabling the identification of key environmental features influencing visitor satisfaction. Concurrently, the YOLO v11 object detection model analyzed 111,469 images to quantify the presence of natural and built features, such as greenery, water bodies, and recreational facilities. Findings reveal that online satisfaction scores are lower than survey-based scores in Stockholm (0.661 vs. 0.774) and Shanghai (0.626 vs. 0.845), indicating a negativity bias in online reviews. In contrast, New York’s online (0.612) and survey (0.610) scores align closely, suggesting a broader representation of perspectives. Feature analysis shows greenery, flowers, and recreational facilities consistently enhance satisfaction, while noise and uncleanliness reduce it. Cultural differences emerge: small animals significantly improve perceptions in New York, whereas excessive liveliness lowers satisfaction in Shanghai due to crowding concerns. By examining 102 parks across these cities, this study highlights the effectiveness of AI-driven, multi-source data analysis in identifying universal and culturally specific park preferences. These insights can guide data-informed urban park management strategies, improving accessibility and visitor experiences globally.https://doi.org/10.1007/s44327-025-00075-1Urban parksPublic perceptionLarge language modelsCultural preferencesCross-cultural comparison |
| spellingShingle | Siqi Lai Brian Deal An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data Discover Cities Urban parks Public perception Large language models Cultural preferences Cross-cultural comparison |
| title | An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data |
| title_full | An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data |
| title_fullStr | An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data |
| title_full_unstemmed | An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data |
| title_short | An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data |
| title_sort | innovative approach to urban parks and perception a cross cultural analysis using big and small data |
| topic | Urban parks Public perception Large language models Cultural preferences Cross-cultural comparison |
| url | https://doi.org/10.1007/s44327-025-00075-1 |
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