This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined by any aspect of their population composition) and to define their boundaries. The authors use data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units like census tracts as the building blocks of neighborhoods. For theoretical reasons the street segment is chosen as the basic unit of analysis. All three methods use information on the ethnic composition of buildings or street segments and the ethnicity of their neighbors. One approach is a variation of k-functions calculated for each adult resident, which are then subjected to a cluster analysis to detect discrete patterns. The second is an application of an energy minimization algorithm commonly used to enhance digital images. The third is a Bayesian approach previously used to study county-level disability data. Results of all three methods depend on decisions about technical procedures and criteria that are made by the investigator. Resulting maps are roughly similar, but there is no one best solution. We conclude that researchers should continue to seek alternative methods, and that the preferred method depends on how one's conceptualization of neighborhoods matches the empirical approach.