论文标题
扩展区域化算法以探索空间过程异质性
Extending regionalization algorithms to explore spatial process heterogeneity
论文作者
论文摘要
在空间回归模型中，可以考虑使用连续或离散规格来考虑空间异质性。后者与在变量之间具有均匀关系的空间连接区域的描述有关（空间制度）。尽管已经在空间分析领域提出并研究了各种区域化算法，但优化空间状态的方法在很大程度上没有探索。在本文中，我们提出了两种用于空间制度描述，两阶段K模型和区域K模型的新算法。我们还将经典的自动分区过程扩展到空间回归环境。提出的算法应用于一系列合成数据集和两个现实世界数据集。结果表明，所有三种算法都具有比现有方法具有出色或可比的性能，而两阶段的K-Models算法在很大程度上优于模型拟合，区域重建和系数估计的现有方法。我们的工作丰富了空间分析工具箱，以探索空间异质过程。
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.
