The Cartography of Opportunity: Spatial Data Science for Equitable Urban Policy

As evidence of the contextual effects of place upon individual outcomes has become increasingly solid over time, so too have urban policies and programs designed to connect underserved people with access to spatial opportunity. To this end, many attempts have been made to quantify the geography of opportunity and quite literally plot it on a map by combining evidence from studies on neighborhood effects with spatial data resources and geographic information systems (GIS) technology. Recently, these opportunity maps have not only become increasingly common but their preparation has been encouraged and facilitated by the U.S. Department of Housing and Urban Development. A closer look at the foundations and methods that underlie these exercises offers important lessons I examine the practice of opportunity mapping from both theoretical and methodological perspectives, highlighting several weaknesses of the common methods. Following this, I outline a theoretical framework based on Galster's categorization of the mechanisms of neighborhood effects. Using data from the Baltimore metropolitan region, I use confirmatory factor analysis to specify a measurement model that verifies the validity of the proposed theoretical framework. The model provides estimates of four latent variables conceived as the essential dimensions of spatial opportunity: social-interactive, environmental, geographic, and institutional. Finally, I develop a neighborhood typology using unsupervised machine learning applied to the four dimensions of opportunity. Results suggest that opportunity mapping can be improved substantially through a better connection to the empirical literature on neighborhood effects, a multivariate statistical framework, and more direct relevance to public policy interventions.