Spatial stratified heterogeneity, revealing the disparity mechanisms across spatial strata, can be effectively quantified using the geographical detector (GD). GD requires reasonable spatial discretization strategies to investigate the spatial association between the target variable and numerical independent variables. In previous studies, the Robust Geographical Detector (RGD) optimized spatial strata for examining the power of determinants (PD) of individual variables, which demonstrate more robust spatial discretization than other models. However, the GD's interaction detector that explores PD of the interaction of two variables still needs to be enhanced by the robust spatial discretization. This study develops a Robust Interaction Detector (RID), an improved interaction detector, using change detection algorithms for the robust spatial stratified heterogeneity analysis with multiple explanatory variables. RID is applied in a road life expectancy analysis in Western Australia. Results show that RID presents higher PD values than previous GD models, ensuring the growth of PD value with more spatial strata. The RID model indicates that the interactions between various transport variables and elevation are strongly associated with road life expectancy from the perspective of spatial patterns. The developed RID model provides significant potential for enhanced geospatial factor analysis across diverse fields.

空间分层异质性揭示了空间各层的差异机制，可以使用地理检测器（GD）进行有效量化。GD需要合理的空间离散策略来研究目标变量与数值自变量之间的空间关联。在之前的研究中，鲁棒地理检测器（RGD）优化了空间层，用于检查单个变量的决定因素（PD）的功效，这表明比其他模型更鲁棒的空间离散化。然而，探索两个变量相互作用的PD的GD相互作用检测器仍然需要通过鲁棒的空间离散化来增强。本研究开发了鲁棒交互检测器（RID），这是一种改进的交互检测器，使用变化检测算法进行具有多个解释变量的鲁棒空间分层异质性分析。RID 应用于西澳大利亚州的道路预期寿命分析。结果表明，RID 比之前的 GD 模型呈现出更高的 PD 值，保证了 PD 值随更多空间层数的增长。RID模型表明，从空间格局的角度来看，各种交通变量和海拔之间的相互作用与道路预期寿命密切相关。开发的 RID 模型为跨不同领域的增强地理空间因素分析提供了巨大的潜力。