There are several notable contributions (Brown & Chung, 2006; Logan & Zhou, 2004; Logan, Zhang, & Alba, 2002) to the residential segregation literature that are the inspiration for the current work. These papers argue that in order to think about the spatial patterning of groups, explicitly spatial approaches, data, and measures should be used rather than aspatial instruments. In particular, I make a case for the use of Moran’s I and local indications of spatial association (LISA). I use Moran’s I (an aggregate or “global” measure) and a measure based on its local decomposition (the LISA cluster typology) as the outcome variables that synthesize the spatial patterning of fourteen different immigrant origin groups across 148 metropolitan areas in the United States. This paper has three substantive findings: destination type is a strong predictor of residential outcomes, that there are unique differences across immigrant origin groups, supporting the move away from panethnic classifications, and that there are spatial patterning differences between foreign-born and native-born groups. This is shown both descriptively and inferentially across 148 metropolitan areas. The paper then moves into an inferential analysis using the Moran’s I and LISA clusters as outcome variables in multivariate models to distinguish group spatial patterning while controlling for metropolitan level variables.