This article examines the differences in diversity between a new urbanist subdivision and a standard suburban subdivision. We examine diversity at the macro level in terms of demographic characteristics such as age, sex, and income. We then examine diversity at the micro level, using a network sample to analyze diversity at the level of the social interaction. We find at the macro level that the new urbanist community is more diverse in a number of factors including education, income, and especially age. However, we find this increased diversity does not translate to diverse social interactions at the micro level.

The study examined spatial bridging ties that develop between members of a subdivision, and the relationship of such ties to different aspects of the built environment. We suggest that spatial bridging ties are important to the development of social capital within a community, as they help to overcome a tendency for ties to be spatially proximate (the propinquity effect). Using ego-based network data, we examined the formation of spatial bridging ties within a new urbanist subdivision (NUS) and also identified specific design features of the NUS that were associated with such ties. We found an association between spatial bridging ties and the use of mixed-use amenities, suggesting that mixed-use amenities (e.g., local shops and restaurants) may be facilitating spatial bridging ties between residents in NUS communities. The results also provide some evidence of a positive relationship between spatial bridging ties and social capital.

Mass shootings are becoming a more common occurrence in the United States. Data show that mass shootings increased steadily over the past nearly 50 years. Crucial is that the wide-ranging adverse effects of mass shootings generate negative mental health outcomes on millions of Americans, including fear, anxiety, and ailments related to such afflictions. This study extends previous research that finds a strong positive relationship between income inequality and mass shootings by examining the effect of household income as well as the interaction between inequality and income. To conduct our analyses, we compile a panel dataset with information across 3,144 counties during the years 1990 to 2015. Mass shootings was measured using a broad definition of three or more victim injuries. Income inequality was calculated using the post-tax version of the Gini coefficient. Our results suggest that while inequality and income alone are both predictors of mass shootings, their impacts on mass shootings are stronger when combined via interaction. Specifically, the results indicate areas with the highest number of mass shootings are those that combine both high levels of inequality and high levels of income. Additionally, robustness checks incorporating various measures of mass shootings and alternative regression techniques had analogous results. Our findings suggest that to address the mass shootings epidemic at its core, it is essential to understand how to stem rising income inequality and the unstable environments that we argue are created by such inequality.

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