High-dimensional spatiotemporal visual analysis of the air quality in China
Abstract Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interact...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-31645-1 |
| _version_ | 1797850103326703616 |
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| author | Jia Liu Gang Wan Wei Liu Chu Li Siqing Peng Zhuli Xie |
| author_facet | Jia Liu Gang Wan Wei Liu Chu Li Siqing Peng Zhuli Xie |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | Abstract Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method. |
| first_indexed | 2024-04-09T18:56:01Z |
| format | Article |
| id | doaj.art-7670cff1df924c6388e1d796de2534c5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T18:56:01Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7670cff1df924c6388e1d796de2534c52023-04-09T11:12:24ZengNature PortfolioScientific Reports2045-23222023-04-0113111310.1038/s41598-023-31645-1High-dimensional spatiotemporal visual analysis of the air quality in ChinaJia Liu0Gang Wan1Wei Liu2Chu Li3Siqing Peng4Zhuli Xie5School of Space Information, Space Engineering UniversitySchool of Space Information, Space Engineering UniversitySchool of Space Information, Space Engineering UniversitySchool of Space Information, Space Engineering UniversitySchool of Space Information, Space Engineering UniversitySchool of Space Information, Space Engineering UniversityAbstract Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method.https://doi.org/10.1038/s41598-023-31645-1 |
| spellingShingle | Jia Liu Gang Wan Wei Liu Chu Li Siqing Peng Zhuli Xie High-dimensional spatiotemporal visual analysis of the air quality in China Scientific Reports |
| title | High-dimensional spatiotemporal visual analysis of the air quality in China |
| title_full | High-dimensional spatiotemporal visual analysis of the air quality in China |
| title_fullStr | High-dimensional spatiotemporal visual analysis of the air quality in China |
| title_full_unstemmed | High-dimensional spatiotemporal visual analysis of the air quality in China |
| title_short | High-dimensional spatiotemporal visual analysis of the air quality in China |
| title_sort | high dimensional spatiotemporal visual analysis of the air quality in china |
| url | https://doi.org/10.1038/s41598-023-31645-1 |
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