The application of big data technology in rural tourism landscape planning under the background of the intelligent era
In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means to promote rural transformation and development and farmers’ employment and income. Firstly, a clustering algorithm is proposed to be applied...
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00283 |
| _version_ | 1797340811062411264 |
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| author | Chen Lexu |
| author_facet | Chen Lexu |
| author_sort | Chen Lexu |
| collection | DOAJ |
| description | In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means to promote rural transformation and development and farmers’ employment and income. Firstly, a clustering algorithm is proposed to be applied to rural tourism landscape planning based on big data, and the clustering algorithm mainly consists of heat measurement, spatial analysis method, kernel density estimation and hot spot identification method. Then, based on the current situation of rural tourism landscape development in China, the rural tourism network structure evaluation index system is constructed, and the evaluation index system consists of network density, central potential, average path, and clustering coefficient. Finally, to verify the accurate prediction performance of the method in this paper, rural tourism in Guizhou province is selected as the research object, and the experiment consists of 100 statistical iterations of the accurate prediction performance of the two methods. The results show that the method of this paper: with the increase of the number of iterations, the prediction accuracy increases from the initial 78.62 to 99.3%, the average prediction accuracy is 93.56%, and the accuracy of rural tourism passenger flow demand prediction by the method of this paper is higher. This study guides the efficient flow of rural tourism and promotes the high-quality development of rural tourism, which is of great significance to the regional cooperation and coordinated development of rural tourism, spatial optimization and traffic diversion, planning and construction and marketing. |
| first_indexed | 2024-03-08T10:08:45Z |
| format | Article |
| id | doaj.art-6a88a8db8dde4dd596b546bf05cc52a0 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:08:45Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-6a88a8db8dde4dd596b546bf05cc52a02024-01-29T08:52:30ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00283The application of big data technology in rural tourism landscape planning under the background of the intelligent eraChen Lexu01Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, 421005, China.In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means to promote rural transformation and development and farmers’ employment and income. Firstly, a clustering algorithm is proposed to be applied to rural tourism landscape planning based on big data, and the clustering algorithm mainly consists of heat measurement, spatial analysis method, kernel density estimation and hot spot identification method. Then, based on the current situation of rural tourism landscape development in China, the rural tourism network structure evaluation index system is constructed, and the evaluation index system consists of network density, central potential, average path, and clustering coefficient. Finally, to verify the accurate prediction performance of the method in this paper, rural tourism in Guizhou province is selected as the research object, and the experiment consists of 100 statistical iterations of the accurate prediction performance of the two methods. The results show that the method of this paper: with the increase of the number of iterations, the prediction accuracy increases from the initial 78.62 to 99.3%, the average prediction accuracy is 93.56%, and the accuracy of rural tourism passenger flow demand prediction by the method of this paper is higher. This study guides the efficient flow of rural tourism and promotes the high-quality development of rural tourism, which is of great significance to the regional cooperation and coordinated development of rural tourism, spatial optimization and traffic diversion, planning and construction and marketing.https://doi.org/10.2478/amns.2023.2.00283big data technologyclustering algorithmrural tourism landscapeevaluation index systemprediction accuracy performance68m01 |
| spellingShingle | Chen Lexu The application of big data technology in rural tourism landscape planning under the background of the intelligent era Applied Mathematics and Nonlinear Sciences big data technology clustering algorithm rural tourism landscape evaluation index system prediction accuracy performance 68m01 |
| title | The application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| title_full | The application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| title_fullStr | The application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| title_full_unstemmed | The application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| title_short | The application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| title_sort | application of big data technology in rural tourism landscape planning under the background of the intelligent era |
| topic | big data technology clustering algorithm rural tourism landscape evaluation index system prediction accuracy performance 68m01 |
| url | https://doi.org/10.2478/amns.2023.2.00283 |
| work_keys_str_mv | AT chenlexu theapplicationofbigdatatechnologyinruraltourismlandscapeplanningunderthebackgroundoftheintelligentera AT chenlexu applicationofbigdatatechnologyinruraltourismlandscapeplanningunderthebackgroundoftheintelligentera |