ABSTRACT Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.

中文翻译：

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基于最优参数的地理检测器模型增强了空间异质性分析解释变量的地理特征：具有不同类型空间数据的案例

摘要 空间异质性代表了空间问题不公平分布的一般特征。空间分层异质性分析通过比较层内和层间的空间方差来考察解释变量各层间的异质性。地理探测器模型是一种广泛使用的空间分层异质性分析技术。在模型中，空间数据离散化和空间尺度效应是基本问题，但在以往的研究中，它们一般是由经验决定的，缺乏准确的定量评估。为了解决这个问题，开发了一种基于最优参数的地理检测器 (OPGD) 模型，以进行更准确的空间分析。探索最佳参数为空间数据离散化方法、空间层间断裂数和空间尺度参数的最佳组合。在研究中，将OPGD模型应用于不同类型空间数据的三个示例案例，包括空间栅格数据、空间点或面统计数据、空间线段数据，并开发了R“GD”包进行计算。结果表明，参数优化过程可以进一步提取地理检测器模型中空间解释变量中包含的地理特征和信息。改进后的模型可灵活应用于各类空间数据的全局和区域空间分析。因此，OPGD模型可以提高空间分层异质性分析的整体能力。