To support equitable planning, model-based analyses can be used to explore inequality patterns arising from different scenarios. Scenario discovery is increasingly used to extract insights from ensembles of simulation. Here, we apply two scenario discovery approaches for unraveling inequality patterns and their drivers, with an application to spatial inequality of farms profitability in the Vietnam Mekong Delta. First, we follow an established sequential approach where we begin with clustering the inequality patterns from the simulation results and next identify model input subspaces that best explain each cluster. Second, we propose a novel concurrent approach using Multivariate Regression Trees to simultaneously classify inequality patterns and identify their corresponding input subspaces. Both approaches have comparable output space separability performance. The concurrent approach yields significantly better input space separability, but this comes at the expense of having a larger number of subspaces, requiring analysts to make extra effort to distill policy-relevant insights.

为了支持公平规划，可以使用基于模型的分析来探索不同情景中产生的不平等模式。场景发现越来越多地用于从模拟集合中提取洞察力。在这里，我们应用两种情景发现方法来解开不平等模式及其驱动因素，并将其应用于越南湄公河三角洲农场盈利能力的空间不平等。首先，我们遵循既定的顺序方法，首先从模拟结果中对不等式模式进行聚类，然后确定最能解释每个聚类的模型输入子空间。其次，我们提出了一种新的并发方法，使用多元回归树来同时对不平等模式进行分类并识别它们相应的输入子空间。两种方法都具有可比的输出空间可分离性能。并发方法产生了明显更好的输入空间可分离性，但这是以拥有更多子空间为代价的，需要分析师付出额外的努力来提炼与政策相关的见解。