Associations between social and neighborhood characteristics and health outcomes are well known but remain poorly understood owing to complex, multidimensional factors that vary across geographic space. Growing interest in quantifying social determinants of health (SDOH) at a small-area resolution must account for such complexity. In a recent cross-sectional study, a Kolak-led team developed multidimensional SDOH indices and a regional typology of the continental U.S. at a small-area level using dimension reduction and clustering machine learning techniques, spatializing results at each stage. The modeling of SDOH as multivariate, geographic phenomena may better capture the complexity and spatial heterogeneity underlying SDOH and associated disparities in health outcomes. Extensions of this work may also characterize and define risk landscapes in complex environments, from the opioid epidemic to COVID-19 pandemic. For example, the US Covid Atlas Project integrates regional contextual factors within a dynamic hotspot surveillance application. During a time of increased attention to SDOH, a spatially explicit approach may provide actionable information for key stakeholders with respect to the focus of interventions -- and better understand what constitutes, drives, and sustains resilient communities.