Spatial analysis in geography is not only concerned with identifying patterns, but with explaining how those patterns differ across space relative to a broader context. In urban environments such as New York City, population distributions are highly uneven, reflecting long-standing processes of segregation, migration, and economic restructuring. Simply mapping counts or proportions of demographic groups does not fully capture these spatial relationships. What is required is a method that allows comparison between local conditions and the broader regional structure. The Location Quotient (LQ) provides this analytic capability. Rather than describing how many individuals of a given group live in a census tract, LQ measures whether that group is more or less concentrated in that tract relative to the overall population distribution of the city. This allows for the identification of areas of over-representation and under-representation, making it a powerful tool for understanding spatial inequality, clustering, and demographic structure. In the context of New York City, this is particularly important. The city is composed of diverse neighborhoods with distinct demographic profiles, and these patterns are shaped by historical processes such as redlining, housing policy, and migration flows. By applying Location Quotients to tract-level Census data, this analysis moves beyond descriptive mapping and toward a comparative spatial framework that reveals how demographic groups are distributed relative to the city as a whole.

This vignette demonstrates a complete, reproducible workflow for analyzing NYC census tract demographics in R using spatial data. The analysis moves from importing a GeoPackage layer to producing tract-level racial composition measures (proportions) and concentration measures (densities). The workflow is designed to be defensible, meaning it includes checks that help prevent common sources of error such as missing columns, invalid numeric types, impossible values, and division-by-zero. NYC is a strong case for tract-level demographic analysis because the city contains extreme variation over short distances. Tracts can shift quickly from high concentration to high fragmentation, which makes it possible to observe how demographic patterns become spatial patterns. Importantly, these patterns are not only descriptive. They reflect how urban space is produced through institutions, housing markets, infrastructure, and neighborhood change processes. Measuring proportions and densities at the tract scale provides a way to operationalize these spatial outcomes. Before running the code, ensure the GeoPackage is available in the same folder as this project or update the path accordingly.