Quantification and Reduction of Spatial Scale-Induced Uncertainty

空间尺度引起的不确定性的量化和减少

• 批准号: 1461390
• 负责人: Daoqin Tong
• 金额: $ 15万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461390&HistoricalAwards=false
• 关键词: Quantification; Reduction; Spatial; Scale; Induced

This research project will examine spatial scale-induced uncertainties and address issues involved in assembling multi-source, multi-scale data in a spatial analysis. Many spatial studies are compromised due to a discrepancy between the spatial scale at which data are analyzed and the spatial scale at which the phenomenon under investigation operates. One consequence has been that research findings often conflict when analyses are conducted at differing scales. Decision making and policy formulation therefore may be misguided by conflicting, biased research findings brought about by spatial scale. Lacking appropriate ways to deal with this spatial problem has made many existing findings less compelling or even invalid. In the era of big data with the advancement of spatial data collection technologies, data that were once difficult or impossible to obtain are now widely available at various spatial scales and are being used to study a variety of problems. Questions regarding spatial data thus become more pressing. The strategies to be developed for addressing the spatial scale issues have the potential to be applied to many fields and applications. The research results will benefit researchers and practitioners in processing, analyzing, and presenting multi-source, multi-scale spatial data. Numerous studies have been conducted to understand how issues related to scale influence analyses and interpretation. Despite the efforts, this remains a widely recognized, complex problem with few generalizable solutions. Even when studies are conducted at the appropriate spatial scale, uncertainty may exist because data are usually collected at different scales and therefore must be aggregated or interpolated to achieve the study scale. Using a large public health surveillance dataset, the investigators will develop a measurement error-based statistical framework to quantify the space scale-induced uncertainties and provide strategies to ameliorate the issues. The research will address issues of 1) characterization of scale due to spatial scale definition and data misalignment in the measurement error framework; 2) quantification of the effects of scale issues on the estimation significance and parameter biasedness; and 3) strategies that may be used to reduce uncertainties in a multi-scale analysis.

该研究项目将研究空间尺度引起的不确定性，并解决在空间分析中组装多源、多尺度数据所涉及的问题。 由于分析数据的空间尺度与所研究现象发生的空间尺度之间的差异，许多空间研究受到损害。 结果之一是，当以不同规模进行分析时，研究结果往往会发生冲突。 因此，决策和政策制定可能会被空间尺度带来的相互矛盾、有偏见的研究结果所误导。 由于缺乏处理这一空间问题的适当方法，许多现有的发现变得不那么引人注目，甚至无效。 在大数据时代，随着空间数据采集技术的进步，曾经难以或不可能获取的数据现在可以在各种空间尺度上广泛使用，并被用来研究各种问题。 因此，有关空间数据的问题变得更加紧迫。 为解决空间尺度问题而制定的策略有可能应用于许多领域和应用。 研究成果将有利于研究人员和从业者处理、分析和呈现多源、多尺度的空间数据。已经进行了大量研究来了解与量表相关的问题如何影响分析和解释。尽管做出了努力，但这仍然是一个广泛认可的复杂问题，几乎没有通用的解决方案。 即使研究是在适当的空间尺度上进行的，也可能存在不确定性，因为数据通常是在不同尺度上收集的，因此必须进行汇总或插值才能达到研究尺度。 使用大型公共卫生监测数据集，研究人员将开发一个基于测量误差的统计框架，以量化空间尺度引起的不确定性，并提供改善问题的策略。 该研究将解决以下问题：1）由于空间尺度定义和测量误差框架中的数据错位而导致的尺度表征； 2）量化尺度问题对估计显着性和参数偏差的影响； 3）可用于减少多尺度分析中的不确定性的策略。