Collaborative Research: Smooth National Measurement of Public Opinion Across Boundaries and Levels: A View From the Bayesian Spatial Approach

这个研究项目将测量美国各地投票选区的民意。该项目将对调查数据很少或没有调查数据的地区（如州立法区）的意见进行估计。该项目的智力价值在于建立了一种衡量公众舆论的新方法，这种方法不仅使用调查对象对投票问题的回答，而且还包含了关于受访者所在位置以及这意味着公众舆论的地理模式的重要信息。加上来自美国人口普查的人口信息，该项目将在调查数据分布稀疏的情况下，对公众情绪做出更有力的估计。调查人员将发布免费软件，其中包括用户友好功能，允许任何公民在他或她自己的选区或尚未投票的选区（如重新划分选区的拟议国会选区）确定民意。该软件将允许更老练的用户获得与地理边界有关的任何变量（即使与公众舆论无关）的测量，这将扩展到公共卫生、流行病学、经济学、社会学、商业和法律等领域的研究。该项目的数据和软件将为新闻媒体、公众和民选官员提供更多关于按选区和地区划分的国家前景的信息，从而更好地了解美国的代表过程，这将对社会产生更广泛的影响。该项目将招募一批多样化的研究助理，他们将接受这类统计分析方面的培训。有关公众舆论的研究往往满足于不太理想的数据。通常，研究人员会将50个州或435个国会选区的民意调查汇总在一起（随着时间的推移而失去变化的感觉），使用旧的民意测量方法（可能与当前的公众观点不一致），或者使用总统投票份额来近似公众情绪（这很容易出错，因为意识形态以外的因素会影响投票选择）。在比这更小的地区，比如州立法区，这个问题就被放大了，因为在这么小的地区很少有很多调查对象。在这个项目中，调查人员问：如何使用调查反应和受访者的地理位置来可靠地预测选区民意？为了回答这个问题，研究者将使用贝叶斯普适克里金方法。这种技术将训练模型与调查数据相匹配，以确定人口因素如何影响公众舆论，以及如何通过地理平滑过程来解释无法用人口统计数据解释的调查回应部分。有了这样的模型，选区的民意可以用已知的人口统计数据和该地区的地理平滑误差过程的值来预测。

This research project will measure public opinion in voting constituencies around the United States. The project will provide estimates of opinion in districts with little or no survey data, such as state legislative districts. The project's intellectual merit comes from establishing a new means for measuring public opinion that not only uses survey respondents' answers to polling questions but also incorporates important information about where respondents are located and what that implies about geographic patterns in public opinion. Coupled with population information from the U.S. Census, the project will produce stronger estimates of public sentiment when survey data are sparsely distributed. The investigators will release free software that includes user-friendly functions allowing any citizen to determine public opinion in his or her own district or in districts that have not yet cast any votes, such as proposed congressional districts in a redistricting cycle. The software will allow more sophisticated users to obtain measures for any variable (even if unrelated to public opinion) in relationship to geographic boundaries, which will have extensions to research in public health, epidemiology, economics, sociology, business, and law. The broader impact to society will be that the data and software from this project will provide more information for the news media, the public, and elected officials regarding the outlook of the nation by constituency and locale, thereby providing a better understanding of the American representation process. The project will recruit a diverse group of research assistants that will be trained in this kind of statistical analysis.Studies relating to public opinion often settle for less-than-ideal data. Frequently, researchers will measure public opinion in the 50 states or the 435 congressional districts by pooling together several surveys taken over time (losing a sense of change over time), using old measures of public opinion (which may not be consistent with current public views), or using presidential vote share to approximate public sentiment (which is prone to error because factors besides ideology affect vote choices). With smaller districts than these, such as state legislative districts, the problem is magnified considerably, because it is rare to have many survey respondents in such a small area. In this project, the investigators ask: How can survey responses and the geographic location of the respondents be used to reliably forecast constituency public opinion? To answer this question, the investigators will use the method of Bayesian universal kriging. This technique fits a training model over survey data to determine how demographic factors shape public opinion and how the portion of survey responses that cannot be explained by demographics can be explained by a geographically smoothed process. With a model like this, public opinion in constituencies can be predicted with known population demographics and the values of the geographically smoothed error process over that district.