Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing
Rapid urbanization has made urban forest scenes scarce resources, leading to a surge in the demand for high-quality rural forest scenes as alternative outdoor recreation spaces. Previous studies mainly applied survey methods, focusing on visitors’ feedback for different types of scenes from the pers...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/14/10/1932 |
| _version_ | 1797573822567677952 |
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| author | Chongxiao Wang Jiahui Zou Xinyuan Fang Shuolei Chen Hao Wang |
| author_facet | Chongxiao Wang Jiahui Zou Xinyuan Fang Shuolei Chen Hao Wang |
| author_sort | Chongxiao Wang |
| collection | DOAJ |
| description | Rapid urbanization has made urban forest scenes scarce resources, leading to a surge in the demand for high-quality rural forest scenes as alternative outdoor recreation spaces. Previous studies mainly applied survey methods, focusing on visitors’ feedback for different types of scenes from the perspective of visual quality evaluation. Nevertheless, the explanations of the relationships between various factors of scenes and visitors’ preferences are relatively superficial. This study sought to explore the distribution and characteristics of preferred rural forest scenes based on visitor reviews from social media, and using Geodetector, a geospatial statistics tool, to quantitatively analyzed the reasons for visitors’ preferences in terms of factors obtained from multi-source geospatial data. The findings are that (1) visitors are already satisfied with the natural environment but expect scenes that reflect the culture of tea; (2) spatial factor has a more robust interpretation of visitors’ preference, and although the Normalized Difference Vegetation Index (NDVI) and non-consumption indicators barely explain visitors’ preference solely when each of them is combined with other indicators, they can produce non-linear enhancement effects. Consequently, this study synthesizes visitors’ feedback and factors in rural forest scenes to understand visitors’ preferences, thus providing insights into human-centered planning. |
| first_indexed | 2024-03-10T21:14:28Z |
| format | Article |
| id | doaj.art-308398518ba04cfa8a6632c5095e7277 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T21:14:28Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-308398518ba04cfa8a6632c5095e72772023-11-19T16:31:18ZengMDPI AGForests1999-49072023-09-011410193210.3390/f14101932Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from NanjingChongxiao Wang0Jiahui Zou1Xinyuan Fang2Shuolei Chen3Hao Wang4School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaRapid urbanization has made urban forest scenes scarce resources, leading to a surge in the demand for high-quality rural forest scenes as alternative outdoor recreation spaces. Previous studies mainly applied survey methods, focusing on visitors’ feedback for different types of scenes from the perspective of visual quality evaluation. Nevertheless, the explanations of the relationships between various factors of scenes and visitors’ preferences are relatively superficial. This study sought to explore the distribution and characteristics of preferred rural forest scenes based on visitor reviews from social media, and using Geodetector, a geospatial statistics tool, to quantitatively analyzed the reasons for visitors’ preferences in terms of factors obtained from multi-source geospatial data. The findings are that (1) visitors are already satisfied with the natural environment but expect scenes that reflect the culture of tea; (2) spatial factor has a more robust interpretation of visitors’ preference, and although the Normalized Difference Vegetation Index (NDVI) and non-consumption indicators barely explain visitors’ preference solely when each of them is combined with other indicators, they can produce non-linear enhancement effects. Consequently, this study synthesizes visitors’ feedback and factors in rural forest scenes to understand visitors’ preferences, thus providing insights into human-centered planning.https://www.mdpi.com/1999-4907/14/10/1932rural forest scenesvisitors’ preferencesocial media datamulti-source geospatial dataGeodetectormachine learning |
| spellingShingle | Chongxiao Wang Jiahui Zou Xinyuan Fang Shuolei Chen Hao Wang Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing Forests rural forest scenes visitors’ preference social media data multi-source geospatial data Geodetector machine learning |
| title | Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing |
| title_full | Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing |
| title_fullStr | Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing |
| title_full_unstemmed | Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing |
| title_short | Using Social Media and Multi-Source Geospatial Data for Quantifying and Understanding Visitor’s Preferences in Rural Forest Scenes: A Case Study from Nanjing |
| title_sort | using social media and multi source geospatial data for quantifying and understanding visitor s preferences in rural forest scenes a case study from nanjing |
| topic | rural forest scenes visitors’ preference social media data multi-source geospatial data Geodetector machine learning |
| url | https://www.mdpi.com/1999-4907/14/10/1932 |
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