期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 34, 期 7, 页码 1353-1377出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2019.1707834
关键词
Geographically neural network weighted regression; Geographically weighted regression; Spatial non-stationarity; Neural network; Ordinary least squares
类别
资金
- National Key Research and Development Program of China [2018YFB0505000]
- National Natural Science Foundation of China [41871287, 41922043]
- Fundamental Research Funds for the Central Universities [2019QNA3013]
Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.
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