Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

ISPRS International Journal of Geo-Information

Lei Che¹  Haiqi Wang²  Zhihai Wang²  Haoran Kong²  Liuke Li²  Qiong Wang²  Jianbo Xu² 

[1] China Research Institute of Radio Propagation, Qingdao 266580, China;College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China;

关键词: spatial weight matrix;    spatial prediction;    least squares support vector regression (LS-SVR);   

DOI  :  10.3390/ijgi10110714

来源: DOAJ

【 摘 要 】

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

【 授权许可】

Unknown