  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.
