A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions
In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number o...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/5/196 |
| _version_ | 1797599877940641792 |
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| author | Chengkun Zhang Yiran Zhang Jiajun Zhang Junwei Yao Hongjiu Liu Tao He Xinyu Zheng Xingyu Xue Liang Xu Jing Yang Yuanyuan Wang Liuchang Xu |
| author_facet | Chengkun Zhang Yiran Zhang Jiajun Zhang Junwei Yao Hongjiu Liu Tao He Xinyu Zheng Xingyu Xue Liang Xu Jing Yang Yuanyuan Wang Liuchang Xu |
| author_sort | Chengkun Zhang |
| collection | DOAJ |
| description | In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition. |
| first_indexed | 2024-03-11T03:40:33Z |
| format | Article |
| id | doaj.art-6ad981edce8344db9aeb9a536ea606fb |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T03:40:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-6ad981edce8344db9aeb9a536ea606fb2023-11-18T01:36:02ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-05-0112519610.3390/ijgi12050196A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive RegionsChengkun Zhang0Yiran Zhang1Jiajun Zhang2Junwei Yao3Hongjiu Liu4Tao He5Xinyu Zheng6Xingyu Xue7Liang Xu8Jing Yang9Yuanyuan Wang10Liuchang Xu11School of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Education, Zhejiang University of Technology, Hangzhou 310014, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaIn recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.https://www.mdpi.com/2220-9964/12/5/196cognitive regiontourist attraction name extractionmulti-distance spatial clustering |
| spellingShingle | Chengkun Zhang Yiran Zhang Jiajun Zhang Junwei Yao Hongjiu Liu Tao He Xinyu Zheng Xingyu Xue Liang Xu Jing Yang Yuanyuan Wang Liuchang Xu A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions ISPRS International Journal of Geo-Information cognitive region tourist attraction name extraction multi-distance spatial clustering |
| title | A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions |
| title_full | A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions |
| title_fullStr | A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions |
| title_full_unstemmed | A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions |
| title_short | A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions |
| title_sort | deep transfer learning toponym extraction and geospatial clustering framework for investigating scenic spots as cognitive regions |
| topic | cognitive region tourist attraction name extraction multi-distance spatial clustering |
| url | https://www.mdpi.com/2220-9964/12/5/196 |
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