Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China
While urban green spaces (UGSs) are important places for residents’ leisure activities, studies describing the long-term daily UGS usage of residents (including the total number of activities, the types of activities, and the touring experience) have not been conducted due to difficulties in data co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/8/1569 |
| _version_ | 1797584710899073024 |
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| author | Chenjing Fan Shiqi Li Yuxin Liu Chenxi Jin Lingling Zhou Yueying Gu Zhenyu Gai Runhan Liu Bing Qiu |
| author_facet | Chenjing Fan Shiqi Li Yuxin Liu Chenxi Jin Lingling Zhou Yueying Gu Zhenyu Gai Runhan Liu Bing Qiu |
| author_sort | Chenjing Fan |
| collection | DOAJ |
| description | While urban green spaces (UGSs) are important places for residents’ leisure activities, studies describing the long-term daily UGS usage of residents (including the total number of activities, the types of activities, and the touring experience) have not been conducted due to difficulties in data collection. Based on social media text data (SMTD), in this study, the total number of daily activities, the intensities of optional and social activities, and the daily touring experience in 100 UGSs in Xiamen, China, were inferred based on the ERNIE 3.0 text pre-training semantic classification model. Based on this, linear regression modeling was applied to analyze the internal environmental factors of the effects of places and external urban form factors regarding daily UGS usage. The research results revealed the following. (1) A descriptive study was conducted on the total numbers, types, and touring experience of activities using SMTD, and the results were verified by line transect surveys, management statistics, and a publicly available dataset. (2) The number of human activities in UGSs was found to be significantly influenced by historical and cultural facilities, nighttime lighting, population density, and the proportion of the floating population. (3) During the daytime, optional activities were found to be significantly influenced by the park type and historical and cultural facilities, and social activities were found to be significantly influenced by historical and cultural facilities and population density. In the evening, optional activities were found to be significantly influenced by the park type, historical and cultural facilities, nighttime lighting, and the proportion of the floating population, and social activities were found to be influenced by the proportion of the floating population. (4) Regarding the touring experience, in the daytime, the park type, green space ratio, and proportion of the floating population had significant effects on the touring experience. In the evening, the park type, historical and cultural facilities, and security factors were found to have significant effects on the touring experience. The methodology and findings of this study aid in the understanding of the differences in daytime and nighttime activities, and in the discovery of planning tools to promote human leisure activities in UGSs. |
| first_indexed | 2024-03-10T23:56:27Z |
| format | Article |
| id | doaj.art-957557dd3c4b466cba1ac3c1febb95ef |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T23:56:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-957557dd3c4b466cba1ac3c1febb95ef2023-11-19T01:08:34ZengMDPI AGForests1999-49072023-07-01148156910.3390/f14081569Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, ChinaChenjing Fan0Shiqi Li1Yuxin Liu2Chenxi Jin3Lingling Zhou4Yueying Gu5Zhenyu Gai6Runhan Liu7Bing Qiu8College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaWhile urban green spaces (UGSs) are important places for residents’ leisure activities, studies describing the long-term daily UGS usage of residents (including the total number of activities, the types of activities, and the touring experience) have not been conducted due to difficulties in data collection. Based on social media text data (SMTD), in this study, the total number of daily activities, the intensities of optional and social activities, and the daily touring experience in 100 UGSs in Xiamen, China, were inferred based on the ERNIE 3.0 text pre-training semantic classification model. Based on this, linear regression modeling was applied to analyze the internal environmental factors of the effects of places and external urban form factors regarding daily UGS usage. The research results revealed the following. (1) A descriptive study was conducted on the total numbers, types, and touring experience of activities using SMTD, and the results were verified by line transect surveys, management statistics, and a publicly available dataset. (2) The number of human activities in UGSs was found to be significantly influenced by historical and cultural facilities, nighttime lighting, population density, and the proportion of the floating population. (3) During the daytime, optional activities were found to be significantly influenced by the park type and historical and cultural facilities, and social activities were found to be significantly influenced by historical and cultural facilities and population density. In the evening, optional activities were found to be significantly influenced by the park type, historical and cultural facilities, nighttime lighting, and the proportion of the floating population, and social activities were found to be influenced by the proportion of the floating population. (4) Regarding the touring experience, in the daytime, the park type, green space ratio, and proportion of the floating population had significant effects on the touring experience. In the evening, the park type, historical and cultural facilities, and security factors were found to have significant effects on the touring experience. The methodology and findings of this study aid in the understanding of the differences in daytime and nighttime activities, and in the discovery of planning tools to promote human leisure activities in UGSs.https://www.mdpi.com/1999-4907/14/8/1569social media text dataoptional activitysocial activityurban formnighttime lighting |
| spellingShingle | Chenjing Fan Shiqi Li Yuxin Liu Chenxi Jin Lingling Zhou Yueying Gu Zhenyu Gai Runhan Liu Bing Qiu Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China Forests social media text data optional activity social activity urban form nighttime lighting |
| title | Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China |
| title_full | Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China |
| title_fullStr | Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China |
| title_full_unstemmed | Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China |
| title_short | Using Social Media Text Data to Analyze the Characteristics and Influencing Factors of Daily Urban Green Space Usage—A Case Study of Xiamen, China |
| title_sort | using social media text data to analyze the characteristics and influencing factors of daily urban green space usage a case study of xiamen china |
| topic | social media text data optional activity social activity urban form nighttime lighting |
| url | https://www.mdpi.com/1999-4907/14/8/1569 |
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