Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example
The comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for the development of tourism and the...
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2024-12-01
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| author | Zihao Feng Runjie Wang Xianglei Liu Ming Huang Liang Huo |
| author_facet | Zihao Feng Runjie Wang Xianglei Liu Ming Huang Liang Huo |
| author_sort | Zihao Feng |
| collection | DOAJ |
| description | The comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for the development of tourism and the dissemination of red culture. Temperature is one of the most important indicators for climate comfort studies. Thus, in this paper, the average air temperature in Jiangxi Province in 2018 was studied. Three interpolation methods of Kriging interpolation, the inverse distance weight method, and the spline function method were used to spatially interpolate the data from 26 weather stations to obtain the spatial distribution map of air temperature for comparative study. At the same time, the method of cross-validation was adopted, and the average error and the root-mean-square error were quoted as the evaluation indexes for accuracy assessment. The conclusions of this paper are as follows: (1) the ME of IDW and spline method can reach 0.02–1.82 °C and the RMSE can reach 1.22–2.72 °C; (2) Kriging interpolation improves the RMSE by 27% and 55% compared to IDW and spline function methods, respectively; (3) considering the relatively sparse distribution of meteorological stations in Jiangxi Province, Kriging interpolation can avoid the extreme value phenomenon due to the influence of distance by reasonably choosing the shape and size associated with the surface space in the process of solving. Moreover, the results of this experimental study show that the accuracy of the kriging interpolation method is higher, so this method is more suitable for the spatial interpolation of the temperature in Jiangxi Province. In conclusion, this study provides a reference for the study of temperature comfort in Jiangxi Province. |
| format | Article |
| id | doaj-art-4df3b4a2078d41019520cbc0cc7d1249 |
| institution | DOAJ |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-4df3b4a2078d41019520cbc0cc7d12492025-08-20T02:43:02ZengMDPI AGProceedings2504-39002024-12-0111011410.3390/proceedings2024110014Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an ExampleZihao Feng0Runjie Wang1Xianglei Liu2Ming Huang3Liang Huo4School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaThe comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for the development of tourism and the dissemination of red culture. Temperature is one of the most important indicators for climate comfort studies. Thus, in this paper, the average air temperature in Jiangxi Province in 2018 was studied. Three interpolation methods of Kriging interpolation, the inverse distance weight method, and the spline function method were used to spatially interpolate the data from 26 weather stations to obtain the spatial distribution map of air temperature for comparative study. At the same time, the method of cross-validation was adopted, and the average error and the root-mean-square error were quoted as the evaluation indexes for accuracy assessment. The conclusions of this paper are as follows: (1) the ME of IDW and spline method can reach 0.02–1.82 °C and the RMSE can reach 1.22–2.72 °C; (2) Kriging interpolation improves the RMSE by 27% and 55% compared to IDW and spline function methods, respectively; (3) considering the relatively sparse distribution of meteorological stations in Jiangxi Province, Kriging interpolation can avoid the extreme value phenomenon due to the influence of distance by reasonably choosing the shape and size associated with the surface space in the process of solving. Moreover, the results of this experimental study show that the accuracy of the kriging interpolation method is higher, so this method is more suitable for the spatial interpolation of the temperature in Jiangxi Province. In conclusion, this study provides a reference for the study of temperature comfort in Jiangxi Province.https://www.mdpi.com/2504-3900/110/1/14spatial interpolationtemperatureskriging interpolationGIS |
| spellingShingle | Zihao Feng Runjie Wang Xianglei Liu Ming Huang Liang Huo Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example Proceedings spatial interpolation temperatures kriging interpolation GIS |
| title | Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example |
| title_full | Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example |
| title_fullStr | Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example |
| title_full_unstemmed | Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example |
| title_short | Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example |
| title_sort | spatial interpolation methods of temperature data based on geographic information system taking jiangxi province as an example |
| topic | spatial interpolation temperatures kriging interpolation GIS |
| url | https://www.mdpi.com/2504-3900/110/1/14 |
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