Artificial Geographically Weighted Neural Network: A Novel Framework for Spatial Analysis with Geographically Weighted Layers

Geographically Weighted Regression (GWR) is a widely recognized technique for modeling spatial heterogeneity. However, it is commonly assumed that the relationships between dependent and independent variables are linear. To overcome this limitation, we propose an Artificial Geographically Weighted Neural Network (AGWNN), a novel framework that integrates geographically weighted techniques with neural networks to capture complex nonlinear spatial relationships. Central to this framework is the Geographically Weighted Layer (GWL), a specialized component designed to encode spatial heterogeneity within the neural network architecture. To rigorously evaluate the performance of AGWNN, we conducted comprehensive experiments using both simulated datasets and real-world case studies. Our results demonstrate that AGWNN significantly outperforms traditional GWR and standard Artificial Neural Networks (ANNs) in terms of model fitting accuracy. Notably, AGWNN excels in modeling intricate nonlinear relationships and effectively identifies complex spatial heterogeneity patterns, offering a robust and versatile tool for advanced spatial analysis.