Quantification and Reduction of Spatial Scale-Induced Uncertainty

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调查现象的空间尺度之间存在差异，许多空间研究受到损害。一个后果是，当在不同的尺度上进行分析时，研究结果往往相互冲突。因此，空间尺度带来的相互矛盾、有偏见的研究结果可能会误导决策和政策制定。由于缺乏处理这一空间问题的适当方法，使得许多现有的研究结果不那么引人注目，甚至无效。在大数据时代，随着空间数据采集技术的进步，曾经难以获得或不可能获得的数据，现在可以在各种空间尺度上广泛获取，并被用于研究各种问题。因此，关于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战略具有应用于许多领域和应用的潜力。研究成果将有助于研究人员和实践者处理、分析和呈现多源、多尺度空间数据。已经进行了大量的研究，以了解与尺度有关的问题如何影响分析和解释。尽管做出了努力，但这仍然是一个公认的复杂问题，几乎没有可推广的解决方案。即使在适当的空间尺度上进行研究，也可能存在不确定性，因为数据通常是在不同的尺度上收集的，因此必须进行汇总或插值以达到研究尺度。利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。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不一致而导致的尺度表征；2)量化尺度问题对估计显著性和参数偏倚的影响；3)可用于减少多尺度分析中不确定性的策略。

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.