Statistical Methods for Environmental Social Science

最近，公众和研究对人类与环境之间的因果关系的兴趣激增。 这种兴趣的大部分是针对回答环境正义的问题，即，确定某些社会人口亚群体是否过度承受某些环境危害。 同时出现的地理信息系统（GIS）帮助促进了这种兴趣，它是一种复杂的计算机程序，用于在一个共同的研究领域内“分层”多个数据源。 然而，目前这些程序还没有统计推断的能力，这是确定数据是否提供了“重要”的不公正证据所必需的，或者这些发现是否完全是偶然出现的。本项目的重点是开发必要的统计分层建模方法，以改进对这些数据集的分析，以及相应的方便用户的计算工具，使非专业统计支助人员能够使用这些方法。 该项目将重点关注至少三个环境社会科学应用领域。 首先，将考虑处理错位面积数据（地理信息系统很容易绘制的那种）的统计方法。 在这里，这些方法必须适应在不一致的区域边界（例如，邮政编码和人口普查区）上聚集的数据，以及随着时间的推移而演变的边界。 其次，这些方法将扩展到数据不仅不对齐，而且类型不同的情况。 例如，一个变量可能只能作为区域总量（例如，按邮政编码划分的外国出生百分比），而另一个变量只能在空间的某些点上（例如，固定监测站的每日颗粒物水平）。 最后，这些方法将进一步扩展，以纳入解决多重和相互冲突的优先事项和目标的正式工具。 实现环境公平往往涉及在效率（即，最大化成本和收益之间的总体差异）和公平（即，公平地分配这些成本和收益）。 对社会产生的惠益将包括改善环境公正评估，处理不同规模数据的系统方法，以及解决相互冲突的分析优先事项和目标的实用战略。 公共政策制定者在努力解决清除现有环境危害以及公平地确定未来潜在危险设施的选址问题时，应该对这些问题感兴趣并加以利用。

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.