Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

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 o...

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Autores principales:	Haiqi Wang, Liuke Li, Lei Che, Haoran Kong, Qiong Wang, Zhihai Wang, Jianbo Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	spatial weight matrix spatial prediction least squares support vector regression (LS-SVR) Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/be39807fcb5346718d5249d85a7031f0
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spelling	oai:doaj.org-article:be39807fcb5346718d5249d85a7031f02021-11-25T17:52:40ZGeospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix10.3390/ijgi101107142220-9964https://doaj.org/article/be39807fcb5346718d5249d85a7031f02021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/714https://doaj.org/toc/2220-9964Due 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.Haiqi WangLiuke LiLei CheHaoran KongQiong WangZhihai WangJianbo XuMDPI AGarticlespatial weight matrixspatial predictionleast squares support vector regression (LS-SVR)Geography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 714, p 714 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spatial weight matrix spatial prediction least squares support vector regression (LS-SVR) Geography (General) G1-922
spellingShingle	spatial weight matrix spatial prediction least squares support vector regression (LS-SVR) Geography (General) G1-922 Haiqi Wang Liuke Li Lei Che Haoran Kong Qiong Wang Zhihai Wang Jianbo Xu Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
description	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.
format	article
author	Haiqi Wang Liuke Li Lei Che Haoran Kong Qiong Wang Zhihai Wang Jianbo Xu
author_facet	Haiqi Wang Liuke Li Lei Che Haoran Kong Qiong Wang Zhihai Wang Jianbo Xu
author_sort	Haiqi Wang
title	Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
title_short	Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
title_full	Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
title_fullStr	Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
title_full_unstemmed	Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
title_sort	geospatial least squares support vector regression fused with spatial weight matrix
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/be39807fcb5346718d5249d85a7031f0
work_keys_str_mv	AT haiqiwang geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT liukeli geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT leiche geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT haorankong geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT qiongwang geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT zhihaiwang geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix AT jianboxu geospatialleastsquaressupportvectorregressionfusedwithspatialweightmatrix
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Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

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