Extending regionalization algorithms to explore spatial process heterogeneity
This work addresses the need for better methods to explore spatial heterogeneity in processes, providing incremental improvements to the spatial analytics toolbox.
The paper tackled the problem of optimizing spatial regimes in regression models by proposing two new algorithms and extending an existing one, achieving superior or comparable performance to existing approaches, with the two-stage K-Models algorithm largely outperforming others in model fitting, region reconstruction, and coefficient estimation.
In spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction, and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.