Evaluating the suitability of urban development land with a Geodetector

被引:52
|
作者
Wang, Haiying [1 ,2 ,5 ]
Qin, Fen [1 ,2 ,5 ]
Xu, Chengdong [3 ]
Li, Bin [1 ,2 ]
Guo, Linping [4 ]
Wang, Zhe [1 ]
机构
[1] Henan Univ, Coll Environm & Planning, Kaifeng 475004, Peoples R China
[2] Henan Univ, Minist Educ, Key Lab Geospatial Technol Middle & Lower Yellow, Kaifeng 475004, Peoples R China
[3] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[4] Henan Univ, Collaborat Innovat Ctr Yellow River Civilizat, Kaifeng 475001, Peoples R China
[5] Henan Univ, Inst Urban Big Data, Kaifeng 475004, Peoples R China
基金
美国国家科学基金会;
关键词
Geodetector; K-means clustering; Logistic regression; Principal component analysis; Suitability evaluation; Urban development land; GIS; AGRICULTURE; HANGZHOU; DECISION; AREA;
D O I
10.1016/j.ecolind.2021.107339
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.
引用
收藏
页数:13
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