Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China
Four regression techniques, including two global models (i.e., Poisson and negative binominal) and two geographically weighted regression (GWR) models (i.e., geographically weighted Poisson regression (GWPR) and geographically weighted negative binominal regression (GWNBR)) were used to explore whic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2021.1884609 |
| _version_ | 1818295118521696256 |
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| author | Qianqian Cao Lianjun Zhang Zhangwen Su Guangyu Wang Shuaichao Sun Futao Guo |
| author_facet | Qianqian Cao Lianjun Zhang Zhangwen Su Guangyu Wang Shuaichao Sun Futao Guo |
| author_sort | Qianqian Cao |
| collection | DOAJ |
| description | Four regression techniques, including two global models (i.e., Poisson and negative binominal) and two geographically weighted regression (GWR) models (i.e., geographically weighted Poisson regression (GWPR) and geographically weighted negative binominal regression (GWNBR)) were used to explore which was the most suitable method for predicting the number of forest fires and to investigate the spatially varying relationships between forest fires and environmental factors in Fujian province, in the Southeast of China. Our results showed that the GWR models fitted the fire count data better than the global models, and yielded more realistic spatial distributions of model predictions. Particularly, GWNBR was superior for addressing overdispersion in the fire count data because it estimated the dispersion parameter at a local level. Additionally, our study indicated that more forest fires occurred in areas of lower elevation, flatter terrain, and higher population density. The global models showed that precipitation had positive impacts on fire occurrence in the study area. In contrast, the GWR models revealed that precipitation was positively related to the forest fires in the western regions of Fujian, but negatively related in the eastern coastal regions. Our study could provide better insight into forest fire management based on local environmental characteristics. |
| first_indexed | 2024-12-13T03:42:34Z |
| format | Article |
| id | doaj.art-f823f990d75743b69c354a060267fb02 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-12-13T03:42:34Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-f823f990d75743b69c354a060267fb022022-12-22T00:00:55ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132021-01-0112149952110.1080/19475705.2021.18846091884609Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, ChinaQianqian Cao0Lianjun Zhang1Zhangwen Su2Guangyu Wang3Shuaichao Sun4Futao Guo5Department of Sustainable Resources Management, College of Environmental Science and Forestry, State University of New York (SUNY-ESF)Department of Sustainable Resources Management, College of Environmental Science and Forestry, State University of New York (SUNY-ESF)College of Forestry, Northeast Forestry UniversityAsia Forest Research Centre, Faculty of Forestry, University of British ColumbiaCollege of Forestry, Fujian Agriculture and Forestry UniversityCollege of Forestry, Fujian Agriculture and Forestry UniversityFour regression techniques, including two global models (i.e., Poisson and negative binominal) and two geographically weighted regression (GWR) models (i.e., geographically weighted Poisson regression (GWPR) and geographically weighted negative binominal regression (GWNBR)) were used to explore which was the most suitable method for predicting the number of forest fires and to investigate the spatially varying relationships between forest fires and environmental factors in Fujian province, in the Southeast of China. Our results showed that the GWR models fitted the fire count data better than the global models, and yielded more realistic spatial distributions of model predictions. Particularly, GWNBR was superior for addressing overdispersion in the fire count data because it estimated the dispersion parameter at a local level. Additionally, our study indicated that more forest fires occurred in areas of lower elevation, flatter terrain, and higher population density. The global models showed that precipitation had positive impacts on fire occurrence in the study area. In contrast, the GWR models revealed that precipitation was positively related to the forest fires in the western regions of Fujian, but negatively related in the eastern coastal regions. Our study could provide better insight into forest fire management based on local environmental characteristics.http://dx.doi.org/10.1080/19475705.2021.1884609geographically weighted poisson regressiongeographically weighted negative binominal regressionforest fire countover-dispersionspatial autocorrelation and heterogeneity |
| spellingShingle | Qianqian Cao Lianjun Zhang Zhangwen Su Guangyu Wang Shuaichao Sun Futao Guo Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China Geomatics, Natural Hazards & Risk geographically weighted poisson regression geographically weighted negative binominal regression forest fire count over-dispersion spatial autocorrelation and heterogeneity |
| title | Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China |
| title_full | Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China |
| title_fullStr | Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China |
| title_full_unstemmed | Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China |
| title_short | Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China |
| title_sort | comparing four regression techniques to explore factors governing the number of forest fires in southeast china |
| topic | geographically weighted poisson regression geographically weighted negative binominal regression forest fire count over-dispersion spatial autocorrelation and heterogeneity |
| url | http://dx.doi.org/10.1080/19475705.2021.1884609 |
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