Abstract
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.
Highlights
IntroductionAcademic Editors: Thomas Blaschke and Wolfgang Kainz
We proposed the geospatial least squares support vector regression (LS-SVR) model, which is an LS-SVR model integrating the spatial dependence among geospatial objects, to perform regression prediction of geospatial data
Comparing the R2 index values between the 0–1 type and numeric-type Geo LSSVR models, it is found that the numeric-type fusion method yields a good fitting effect
Summary
Academic Editors: Thomas Blaschke and Wolfgang Kainz. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Spatial statistics analysis refers to the description and analysis of spatial phenomena from the perspective of geography. As a research object of spatial statistics analysis, spatial lattice data are data retrieved from spatially random processes in which the number of collected sites is countable [1]. It has a geographical location and is a digital description of the spatial and attribute characteristics of geospatial objects. The data may not obey a normal distribution, and there may exist complex nonlinear relationships among the variables
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