NCRN-MN: Improving the Interpretability and Usability of the American Community Survey Through Hierarchical Multiscale Spatio-Temporal Statistical Models

NCRN-MN：通过分层多尺度时空统计模型提高美国社区调查的可解释性和可用性

• 批准号: 1132031
• 负责人: Scott Holan
• 金额: $ 285.42万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1132031&HistoricalAwards=false
• 关键词: NCRN; MN; Improving; Interpretability; Usability

The American Community Survey (ACS) is an ongoing survey that releases data annually, providing communities with the timely information needed to plan the distribution of resources and services. According to the U.S. Census Bureau, the data from the survey provides input into how more than $400 billion in federal and state funds are distributed annually. Census released the first five-year period estimates associated with the ACS for all standard tabulation areas in 2010. This marks an interesting time in U.S. history as Census shifts from the decennial census long-form data to using an ongoing survey that releases data annually. Making this transition presents many methodological challenges, both for Census and for data users. The hierarchical multiscale spatio-temporal statistical models and expansive statistical methodology in this project will address many of these challenges and facilitate broader and more effective utilization of the ACS. In particular, this project will develop an efficient framework for carrying out small area estimation while preserving geographical and temporal constraints that arise from the aggregate structure found in the ACS. Further, by borrowing strength across multiple scales in space and time and multiple outcomes, the approach will reduce the variance in the ACS small area estimates and its derivatives. Additionally, from a data-user perspective, the methodology will simultaneously provide coherent estimates on several temporal scales rather than being hampered by the published multiyear estimates, allowing researchers to compare trends across different geographic scales and units.This project will investigate methodology that provides novel solutions across a wide-range of applied problems. The research will improve the interpretability and usability of the ACS through the development of hierarchical multiscale spatio-temporal statistical models. In addition, the project will provide a variety of methods that are of independent interest and can be used in many other surveys administered by Census and other federal statistics agencies. Several of the proposed methods also will directly carry over to the area of disease mapping and thus provide important tools for public health. The project will contribute to the statistics literature and will be of value to the work of government agencies and many subject-matter disciplines. One of the major focuses of this project will be to educate and train graduate students and postdoctoral researchers. It is expected that students will acquire the requisite skills for possible integration into the federal sector. This activity is supported by the NSF-Census Research Network funding opportunity.

美国社区调查（ACS）是一项持续进行的调查，每年发布数据，为社区提供规划资源和服务分配所需的及时信息。 根据美国人口普查局的数据，调查数据为每年超过4000亿美元的联邦和州基金的分配提供了参考。 2010年，人口普查局发布了与ACS相关的所有标准制表地区的第一个五年期估计。 这标志着美国历史上一个有趣的时刻，因为人口普查从十年一次的人口普查长期数据转变为使用每年发布数据的持续调查。 这一转变对人口普查和数据用户都提出了许多方法上的挑战。 该项目中的分层多尺度时空统计模型和扩展的统计方法将解决许多这些挑战，并促进更广泛和更有效地利用ACS。 特别是，该项目将制定一个有效的框架，进行小面积的估计，同时保留地理和时间的限制，所产生的聚合结构中发现的ACS。 此外，通过在空间和时间的多个尺度以及多个结果上借用力量，该方法将减少ACS小区域估计及其衍生物的方差。 此外，从数据用户的角度来看，该方法将同时提供几个时间尺度上的连贯估计，而不是受到已公布的多年估计的阻碍，使研究人员能够比较不同地理尺度和单位的趋势。 该研究将提高ACS的可解释性和可用性，通过分层多尺度时空统计模型的发展。 此外，该项目将提供各种方法，这些方法具有独立的意义，可用于人口普查和其他联邦统计机构管理的许多其他调查。 一些拟议的方法也将直接延续到疾病绘图领域，从而为公共卫生提供重要工具。 该项目将为统计文献作出贡献，并将对政府机构和许多主题学科的工作具有价值。 该项目的主要重点之一将是教育和培养研究生和博士后研究人员。 预计学生将获得必要的技能，以便有可能融入联邦部门。 这项活动得到了NSF-人口普查研究网络资助机会的支持。