[1]Mu Fengyun,Huang Qi,Chen Lin.Eco-environmental Quality Driving Force Detection Using Optimized Geographic Detector[J].Research of Soil and Water Conservation,2024,31(01):440-449.[doi:10.13869/j.cnki.rswc.2024.01.010]
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Eco-environmental Quality Driving Force Detection Using Optimized Geographic Detector
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
31
Number of periods:
2024 01
Page number:
440-449
Column:
Public date:
2024-02-20
- Title:
- Eco-environmental Quality Driving Force Detection Using Optimized Geographic Detector
- Keywords:
- geographic detector; ecological environment quality; driving force analysis; optimal discrete method; Chongqing City
- CLC:
- S284
- DOI:
- 10.13869/j.cnki.rswc.2024.01.010
- Abstract:
- [Objective] This study arms to overcome the randomness and subjectivity of ecological environment quality(EQI)evolution drivers in the discretization process, and to optimize the traditional geographic detector. [Methods] This study discretized 22 EQI evolutionary driving factors in Chongqing by using five discretization methods, namely equal interval method, quantile method, natural breakpoint method, geometric interval method and standard deviation method. Geographic detector was combined to determine the reasonable discrete method and classification number of each factor, so as to explore the driving force size of each driving factor and the interaction model of each driving factor. [Results](1)From 2005 to 2020, the number of districts and counties in grade I, Ⅲ and Ⅳ decreased by 0.211, while the number of districts and counties in Grade Ⅱ and V increased by 0.211, showing an overall downward trend and spatial gradient distribution from northwest to southeast.(2)The order of applicability of each discrete method in this study was: natural break point method>geometric interval method>quantile method>equal interval method>standard deviation method. The discretization methods applicable to different factors were different, so the discretization methods should be selected according to the characteristics of factor data.(3)Four key driving factors(0.37～0.49), 13 main driving factors(0.14～0.33), four minor driving factors(0.05～0.13)and one other factor(0.04)were identified, and the interaction among all driving factors was doubly enhanced or nonlinear enhanced. [Conclusion] Reasonable discretization can overcome randomness and subjectivity in the process of continuous data discretization to a certain extent, so as to optimize the detection results of EQI evolution driving forces in Chongqing.
- References:
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Last Update:
2024-02-20