Statistical Methods for Environmental Social Science

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Recently, there has been an explosion of both public and research interest in the cause-and-effect relationships between humans and their environment. Much of this interest has been directed at answering questions of environmental justice, i.e., determining whether certain sociodemographic subgroups bear an undue share of certain environmental hazards. Helping facilitate this interest has been the concurrent emergence of geographic information systems (GISs), sophisticated computer programs for "layering" multiple data sources over a common study area. However, such programs at present have no facility for statistical inference, which is necessary for determining whether the data offer "significant" evidence of injustice, or whether the findings could just as well have arisen completely by chance.This project focuses on developing necessary statistical hierarchical modeling methods for the improved analysis of such data sets, as well as corresponding user-friendly computing tools to enable use of the methods by non-expert statistical support staff. The project will focus on at least three environmental social science application areas. First, statistical methods for handling misaligned areal data (of the sort so easily mapped by GISs) will be considered. Here the methods must accommodate data aggregated over misaligned regional boundaries (say, zip codes and census tracts), as well as boundaries which evolve over time. Second, the methods will be extended to the case where data are not only misaligned, but of different types. For example, one variable might be available only as a regional aggregate (say, percent foreign-born by zip code), while another is available only at certain points in space (say, daily particulate matter levels at a collection of fixed monitoring stations). Finally, the methods will be further extended to incorporate formal tools for resolving multiple and conflicting priorities and goals. Achieving environmental equity often involves making tradeoffs between efficiency (i.e., maximizing the overall difference between costs and benefits) and equity (i.e., an evenhanded distribution of these costs and benefits). Benefits accruing to society will include improved environmental justice assessments, a systematic approach for handling data at different scales, and practical strategies for resolving conflicting analytic priorities and goals. These should be of interest and use to public policymakers as they grapple with the problems of cleaning up existing environmental hazards, as well as equitably siting future potentially hazardous facilities.