Space has always been a critical component of the sociological study of racial inequality, yet it has rarely been the central focus of empirical projects. Studies of segregation, an inherently spatial concept, have relied on techniques that are aspatial introduce an unknown amount of error into their results. This project extends standard spatial analytic techniques to the sociological study of racial segregation, using Philadelphia as its case study. By introducing non-euclidean kernel density analysis to the study of racial segregation, the project explores how a more visual and more spatially informed approach changes the geography of racial segregation. A more visual approach to segregation more readily identiiesy locations of racial turnover compared to traditional measures such as indices dissimilarity, entropy, and isolation. Incorporating physical barriers into a spatial measure of segregation also complicates the finding that segregation is decreasing over time at a substantial rate and is able to identify and locate specific areas within the city where segregation is uniquely persistent or uniquely transitory, and that racial boundaries such as major roads or railroad tracks are more strongly associated with protecting white neighborhoods from non-white residents rather than isolating black or Hispanic populations. A spatial sociology of inequality offers a novel lens through which to study racial inequality, segregation, and make those findings relevant to efforts to lessen segregation's impact on inequality.