Cities are characterized by the coexistence of general aggregate patterns, along with many local variations. This poses challenges for analyses of urban phenomena, which tend to be either too aggregated or too local, depending on the disciplinary approach. In this talk, we combine two lenses to recover that hidden structure in the statistics and dynamics of US census income and population data. First, we introduce an information-theoretic measure of neighborhood effects. It compares local income distributions to citywide baselines, quantifying how strongly income is sorted across space. Across 900+ U.S. metros, income sorting is group-dependent—richest and poorest households are most segregated, while middle-income households are most mixed. Second, we introduce a multilevel Price equation decomposition that partitions aggregate growth into selection (reallocation covariances) and transmission (within-unit change) at each geographic level. Applied to the Chicago MSA, this reveals where population churn and income growth align—or trade off— across governance boundaries, and how those interactions amplify or dampen spatial inequality. Together, the information metrics (what is sorted, and how much) and the Price decomposition (where is sorting, and with what effect) provide a unified, scalable toolkit for diagnosing neighborhood change, evaluating policies, and tracking emerging neighbourhood polarization over time.