Identifying and Bounding Ethnic Neighborhoods.

This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual 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.

Recognition of shapes by editing their shock graphs.

This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high-dimensional, three steps are taken to make the search practical: 1) define an equivalence class for shapes based on shock-graph topology, 2) define an equivalence class for deformation paths based on shock-graph transitions, and 3) avoid complexity-increasing deformation paths by moving toward shock-graph degeneracy. Despite these steps, which tremendously reduce the search requirement, there still remain numerous deformation paths to consider. To that end, we employ an edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time. The proposed approach gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations. The recognition rates on two distinct databases of 99 and 216 shapes each indicate highly successful within category matches (100 percent in top three matches), which render the framework potentially usable in a range of shape-based recognition applications.