Balancing Spatial and Non?Spatial Variation in Varying Coefficient Modeling: A Remedy for Spurious Correlation

This study discusses the importance of balancing spatial and non-spatial variation in regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular statistics, non-spatially (NVC) modeling has largely been unexplored fields. Nevertheless, as we will explain, consideration needed not only to improve model accuracy but also reduce spurious correlation among coefficients, a major problem SVC We consider Moran eigenvector approach (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC with existing models suggests both computational efficiency for approach. Beyond that, somewhat surprisingly, identifies true correlations nearly perfectly, even when usual suffer from severe correlations. It implies that should be used analysis purpose SVCs. Finally, employed analyze residential land price data set. Its results suggest existence practice. The now implemented R package spmoran.