HOPES AND OPPORTUNITIES FOR INNER CITY RESIDENTS: Temporal and Spatial Assessment of Racial and Socioeconomic Conditions of Neighborhoods Adjacent to Brownfields in the Detroit Metropolitan Area.

Although many environmental justice studies have examined racial and socioeconomic disparities in locations of hazardous waste facilities, no study has examined to date racial and socioeconomic disparities in brownfield locations. In order to fill this gap, this dissertation thus examines the racial and socioeconomic characteristics of neighborhoods adjacent to brownfields in the Detroit region from 1960 to 2000. Based on some of the past sociological claims in the specific context of brownfields, this dissertation argues that deindustrialization in the 1960s and the subsequent concentration of poverty in the 1970s were responsible for socioeconomic disparities in brownfield locations. That is, socioeconomic conditions of brownfield neighborhoods are worse than socioeconomic conditions of non-brownfield neighborhoods. Moreover, this dissertation also argues that residential segregation imposed on minorities also was responsible for racial disparities in brownfield locations, meaning that brownfield neighborhoods are minority concentrated compared to non-brownfield neighborhoods. This dissertation combines the locations of brownfields provided by the Michigan Department of Environmental Quality with 1960, 1970, 1980, 1990, and 2000 US Census data employing distance-based methods. Results reveal that brownfield neighborhoods show a higher concentration of minorities and a lower socioeconomic condition than non-brownfield neighborhoods. In addition, race is the strongest independent predictor of brownfield locations. Longitudinal analyses of brownfield locations from 1960 to 2000 reveal that brownfield neighborhoods experienced greater socioeconomic decline than did non-brownfield neighborhoods. When socioeconomic characteristics in 1970 are controlled, distinctive patterns of subsequent changes in socioeconomic characteristics were found on the basis of initial socioeconomic status. For the wealthiest neighborhoods in 1970, brownfield neighborhoods experienced greater socioeconomic declines than non-brownfield neighborhoods only in the 1970s. For second and third wealthiest neighborhoods, brownfield neighborhoods experienced greater socioeconomic declines than non-brownfield neighborhoods in both the 1970s and 1980s. For the most impoverished neighborhoods, no significant changes in socioeconomic differences between brownfield and non-brownfield neighborhoods were found in any decade. Finally, impoverished and minority-concentrated neighborhoods tend to get priority in brownfield cleanup. Findings from this dissertation suggest that deindustrialization led not only to economic and social inequality but also to environmental inequality.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60767/1/sangyunl_1.pd

Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis

Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models themselves do not consider any relative importance of each frequency component. Therefore, to incorporate the inductive bias for image data, we propose a novel generative process that synthesizes images in a coarse-to-fine manner. First, we generalize the standard diffusion models by enabling diffusion in a rotated coordinate system with different velocities for each component of the vector. We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds. Specifically, the proposed blur diffusion consists of a forward process that blurs an image and adds noise gradually, after which a corresponding reverse process deblurs an image and removes noise progressively. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets. Code is available at https://github.com/sangyun884/blur-diffusion