Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform those in another, etc.? Answers to these questions require explanations of the model structure, the choice of parameters, and contextualization of the findings with respect to their geographic context. This is particularly true for local forms of regression models which are focused on the role of locational context in determining human behavior. In this paper, we present GeoExplainer, a visual analytics framework designed to support analysts in creating explanative documentation that summarizes and contextualizes their spatial analyses. As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results. As analysts explore the model results, all visualizations and annotations can be captured in an interactive report generation widget. We demonstrate our framework using a case study modeling the determinants of voting in the 2016 US Presidential Election.

翻译：地理回归模型常被用于识别空间分布观测数据中决定因素的规律与异常。此类分析旨在回答关于潜在空间现象的“为什么”问题，例如：为何某区域犯罪率更高？为何某学区儿童学业表现优于其他学区？回答这些问题需要解释模型结构、参数选择，以及结合地理背景对研究结果进行语境化分析。对于关注位置背景对人类行为影响的局部回归模型而言，这一点尤为重要。本文提出GeoExplainer——一个支持分析师创建解释性文档的可视分析框架，该文档可总结并语境化其空间分析过程。当分析师构建空间模型时，本框架会标记模型参数选择的潜在问题，利用基于模板的文本生成方法总结模型输出，并与外部知识库关联以提供解释模型结果的注释。分析师探索模型结果时，所有可视化与注释均可通过交互式报告生成组件捕获。我们通过2016年美国总统选举中投票行为决定因素的建模案例研究，展示了本框架的有效性。