Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix
Bibliographic Data
| ID | 22033666 |
|---|---|
| Authors | Haiqi Wang (0000-0002-9684-1722, China University of Petroleum, East China), Liuke Li (0000-0002-7710-3935, China University of Petroleum, East China, corresponding author), Lei Che (0000-0002-7391-9515, China Research Institute of Radio Propagation, Qingdao 266580, China), Haoran Kong (0000-0001-9007-7770, China University of Petroleum, East China), Qiong Wang (0000-0003-3376-1925, China University of Petroleum, East China), Zhihai Wang (0009-0003-4917-5143, China University of Petroleum, East China), Jianbo Xu (0000-0001-9143-168X, China University of Petroleum, East China) |
| Year | 2021 |
| Volume | 10 |
| Issue | 11 |
| Pages | 714 |
| Publication date | 2021-10-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10110714 |
| OpenAlex | W3207083903 |
| Language | EN |
| References cited | 36 |
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
Data mining · Geographic information system · Geography · Geospatial analysis · Machine learning · Regression · Regression analysis · Remote sensing · Spatial analysis · Spatial database · Statistics · Support vector machine · Computer Science · Mathematics · Regional Economic and Spatial Analysis · Remote-Sensing Image Classification · Spatial and Panel Data Analysis · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |