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Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset
- 1 Reading Academy, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 2 School of Computer Science and Technology, Nanjing University, Nanjing, 210008, China
- 3 Shenyuan College, Beihang University, Beijing, 100191, China
- 4 College of Computer Science and Technology, Zhejiang University, Hangzhou, 310058, China
* Author to whom correspondence should be addressed.
Abstract
The GDP and unemployment rates of two geographic units are influenced by their population density and economic characteristics, as well as their distance and spatial relationship. This work aim to use Geographically Weighted Regression (GWR), a classic and widely used method for modeling spatial heterogeneity, to analyze the correlation of these multiple factors. However, GWR does not precisely express its weighting kernel, making it insufficient to estimate complex geographic processes. Therefore, the work employed the Geographically Weighted Neural Network Regression (GNNWR) model, which combines Ordinary Least Squares (OLS) and neural networks, to estimate spatial heterogeneity based on GWR. This work collected various indicators, such as population density and GDP of first-level administrative units of the top 20 countries by GDP at the end of 2022, along with the Euclidean distance and geographic topology between these units. Using GNNWR, the work analyzed the correlation of their GDP growth. The results show that GNNWR outperforms OLS and GWR in fitting accuracy and provides more accurate predictions of the economic correlation between two geographic units.
Keywords
Geographically neural network weighted regression, Spatial non-stationarity, Geospatial topological relationships, Correlation of regional GDP growth rates, spatial autocorrelation
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Cite this article
Zhou,Y.;Wu,J.;Liu,R.;Yu,Z. (2025). Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset. Applied and Computational Engineering,132,55-69.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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