Understanding how local environments influence individual behaviors, such as voting patterns or suicidal tendencies, is crucial in social science to reveal and reduce spatial disparities and promote social well-being. With the increasing availability of large-scale individual-level census data, new analytical opportunities arise for social scientists to explore human behaviors (e.g., political engagement) among social groups at a fine-grained level. However, traditional statistical methods mostly focus on global, aggregated spatial correlations, which are limited to understanding and comparing the impact of local environments (e.g., neighborhoods) on human behaviors among social groups. In this study, we introduce a new analytical framework for analyzing multi-variate neighborhood effects between social groups. We then propose NeighVi, an interactive visual analytics system that helps social scientists explore, understand, and verify the influence of neighborhood effects on human behaviors. Finally, we use a case study to illustrate the effectiveness and usability of our system.

翻译：摘要：理解局部环境如何影响个体行为（如投票模式或自杀倾向），对于社会科学揭示和减少空间差异、促进社会福祉至关重要。随着大规模个体级人口普查数据的日益普及，社会科学家获得了新的分析机会，能够在细粒度层面探索社会群体中的人类行为（例如，政治参与）。然而，传统统计方法主要关注全局性的聚合空间相关性，这限制了在理解和比较局部环境（如邻里）对社会群体人类行为影响方面的能力。在本研究中，我们引入了一个新的分析框架，用于分析社会群体间的多变量邻里效应。随后，我们提出了NeighVi，一个交互式可视化分析系统，帮助社会科学家探索、理解和验证邻里效应对人类行为的影响。最后，我们通过一个案例研究展示了我们系统的有效性和可用性。