Multiscale Continuous and Discrete Spatial Heterogeneity Analysis: The Development of a Local Model Combining Eigenvector Spatial Filters and Generalized Lasso Penalties

Abstract

Two types of spatial heterogeneity can exist simultaneously: continuous variations across an entire space and significant changes that occur only in specific spatial units. Moreover, each of these can act across multiple spatial scales. To effectively detect both continuous and discrete spatial heterogeneity across different scales, this study proposes a novel approach that combines the random effects eigenvector spatially filtering-based spatially varying coefficient (RE-ESF-SVC) model and the generalized lasso (GL) technique. Additionally, a restricted maximum likelihood estimation (REML)-based two-step iterative algorithm is developed for parameter estimation. Simulation experiments and an empirical application using rental price data confirm the ability of the proposed model to identify multiscale continuous and discrete spatial heterogeneity.