Advancing Methods for Spatial Analysis in Local Modeling

改进局部建模中的空间分析方法

基本信息

批准号：
2117455
负责人：
Alexander Fotheringham
金额：
$ 39.99万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117455&HistoricalAwards=false
关键词：
Advancing Methods Spatial Analysis Local

项目摘要

This project develops statistical methods for spatial data. It is common for data to exhibit variation across spatial contexts, including outcomes such as crime rates, voting preferences in elections, and disease prevalence. Conventionally, researchers have used statistical models that implicitly assume that the processes generating these outcomes are uniform across locations. Yet, the processes that lead to this variation may vary in different spatial contexts, and statistical approaches are needed that account for this variation. In this study, the researchers develop statistical methods that address correlations between spatially proximate outcomes. As a complement to the methods that they develop, the researchers are developing open source software to make these approaches freely available to other scholars. In addition to the methodological advances, the award supports the involvement of two graduate students, who benefit from the training in scientific research. The award supports a researcher with a disability, which contributes to goals of broadening participation in science.In this study, the researchers examine and develop methods for the statistical modeling of data at multiple scales. Specifically, the researchers advance multiscale geographically weighted regression (MGWR) methods, which allows the effects of predictor variables to vary based on their spatial context. These approaches address known challenges to statistical inferences, specifically that regression models may indicate biased and contrary effects if the models do not adequately account for the scale and structure in the dataset. In this project, the researchers examine the extent to which these problems can be addressed with the use of MGWR methods. Additional aims include new methods for adjusting inferences when multiple hypotheses are examined and the implementation of diagnostic tools for examining the robustness of model assumptions. To advance the methods, the researchers apply the modeling approach to empirical datasets with spatial structure, such as mortality during the COVID-19 pandemic, teen pregnancy rates, and voting trends in recent elections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目开发了用于空间数据的统计方法。数据通常在空间环境中表现出差异，包括犯罪率，选举中的投票偏好以及疾病患病率等结果。通常，研究人员使用了统计模型，这些模型隐含地假设生成这些结果的过程在整个位置都统一。但是，导致这种变化的过程可能在不同的空间环境中有所不同，并且需要统计方法来解释这种变化。在这项研究中，研究人员开发了统计方法，以解决空间接近结果之间的相关性。作为对他们开发的方法的补充，研究人员正在开发开源软件，以使其他学者免费使用这些方法。除了方法学的进步外，该奖项还支持两名研究生的参与，他们受益于科学研究的培训。该奖项支持残疾的研究人员，这有助于扩大参与科学的目标。在这项研究中，研究人员检查和开发了多个量表数据统计建模的方法。具体而言，研究人员推进了多尺度地理位置加权回归（MGWR）方法，该方法允许预测变量的影响根据其空间上下文而变化。这些方法应对统计推断的已知挑战，特别是，如果模型不能充分说明数据集中的规模和结构，则回归模型可能表明有偏见和相反的效果。在这个项目中，研究人员研究了使用MGWR方法可以解决这些问题的程度。其他目的包括在检查多个假设时调整推论的新方法，并实施诊断工具以检查模型假设的鲁棒性。为了推进方法，研究人员将建模方法应用于具有空间结构的实证数据集，例如在Covid-19期间的死亡率，青少年怀孕率和最近选举中的投票趋势。这一奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛影响来评估NSF的法定任务。