The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

中文翻译：

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车辆：冲突事件数据分层集成的验证和探索

对大规模冲突及其原因和影响的探索是社会政治分析的一个重要方面。由于与重大冲突相关的事件数据通常来自不同的来源，研究人员开发了一种半自动匹配算法，使用分层分类法将不同来源的事件数据整合到一个综合数据集中。相应的集成结果的有效性不容易评估，因为结果取决于用户定义的输入参数和原始数据源之间的关系。然而，到目前为止，只使用了基本的可视化技术来分析结果，无法对最终数据集的组成方式进行可靠的验证或探索。为了克服这个问题，我们开发了VEHICLE，一种基于 Web 的工具，用于验证和探索分层集成的结果。在设计中，我们与领域专家合作，确定潜在的领域问题并得出任务和工作流描述。该工具结合了传统和新颖的视觉分析技术，采用了统计和基于地图的描述以及先进的交互技术。我们在两个案例研究中展示了VEHICLE的有用性，并与冲突研究人员一起进行了评估，确认了领域假设并产生了新的见解。