  Recent discussions on the future of metropolitan cities underscore the
pivotal role of (social) equity, driven by demographic and economic trends.
More equal policies can foster and contribute to a city's economic success and
social stability. In this work, we focus on identifying metropolitan areas with
distinct economic and social levels in the greater Los Angeles area, one of the
most diverse yet unequal areas in the United States. Utilizing American
Community Survey data, we propose a Bayesian model for boundary detection based
on income distributions. The model identifies areas with significant income
disparities, offering actionable insights for policymakers to address social
and economic inequalities. Our approach formalized as a Bayesian structural
learning framework, models areal densities through finite mixture models.
Efficient posterior computation is facilitated by a transdimensional Markov
Chain Monte Carlo sampler. The methodology is validated via extensive
simulations and applied to the income distributions in the greater Los Angeles
area. We identify several boundaries in the income distributions which can be
explained in light of other social dynamics such as crime rates and healthcare,
showing the usefulness of such an analysis to policymakers.
