Spatial Homogeneity Learning Models with Applications to Socioeconomic Problems

空间同质性学习模型及其在社会经济问题中的应用

• 批准号: 2243058
• 负责人: Guanyu Hu
• 金额: $ 50万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243058&HistoricalAwards=false
• 关键词: Spatial; Homogeneity; Learning; Models; Applications

This research project will develop statistical methodologies and associated theories that facilitate the analysis of spatial data for socioeconomic problems. The project is motivated by common features found in many modern datasets, such as the U.S. Census Bureau's American Community Survey, data from the U.S. Bureau of Economic Analysis, and County Health Rankings & Roadmaps data. These public-use datasets are enormous and have hidden homogeneity features on many different demographic and economic indicators, at different spatial locations and different time periods. This project will provide a general formulation and a flexible machine learning toolbox for exploring latent heterogeneity and subgroups and discovering hidden patterns within subgroups of spatial data. Students will be recruited, especially from underrepresented groups, to participate in the research. New courses on spatio-temporal statistics and geographic information systems and user-friendly software packages will be developed. The project will advance knowledge within the statistical sciences, and the research results will be of value to the work of government agencies.This research project will develop a geographically adaptive concave fusion penalized (GACP) learning method that can simultaneously estimate the model parameters and recover the latent memberships. Based on the GACP learning, the project will pursue three specific research topics, and the newly developed methodology will be applied to different socioeconomic problems. In the first topic, the project will develop a generalized optimization estimation approach based on the GACP learning for spatially varying coefficient models with a latent grouping structure. In the second topic, the project will extend the newly developed framework to compositional covariates to explore heterogeneous effects of Intersectoral Gross Domestic Product contributions on Gini coefficients over subregions in the United States. In the third topic, the project will derive a joint estimation and clustering procedure of Lorenz curves across different states in the US. The project will establish consistency and asymptotic distributions for the newly developed estimators and will develop efficient algorithms for optimization. This project will advance the frontiers of spatial heterogeneity learning for socioeconomic problems. The knowledge gained from this research will benefit regional economic policy and other complex socioeconomic problems.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.

该研究项目将开发统计方法和相关理论，以促进分析社会经济问题的空间数据。该项目的动机是在许多现代数据集中发现的共同特征，例如美国人口普查局的美国社区调查，美国经济分析局的数据和县健康排名路线图数据。这些公共使用的数据集数量庞大，在不同的空间位置和不同的时间段，在许多不同的人口和经济指标上具有隐藏的同质性特征。该项目将提供一个通用的公式和一个灵活的机器学习工具箱，用于探索潜在的异质性和子组，并发现空间数据子组中的隐藏模式。将招募学生，特别是来自代表性不足的群体的学生参加研究。将开发关于时空统计和地理信息系统的新课程以及方便用户的软件包。该项目将推进统计科学领域的知识，研究成果将对政府机构的工作具有价值。该研究项目将开发一种地理自适应凹融合惩罚（GACP）学习方法，该方法可以同时估计模型参数和恢复潜在成员。在GACP学习的基础上，该项目将进行三个具体的研究课题，新开发的方法将应用于不同的社会经济问题。在第一个主题中，该项目将开发一个广义的优化估计方法的基础上GACP学习的空间变化系数模型与潜在的分组结构。在第二个主题中，该项目将把新开发的框架扩展到组成协变量，以探索美国各次区域跨部门GDP贡献对基尼系数的异质性影响。在第三个主题中，该项目将推导出美国不同州的洛伦兹曲线的联合估计和聚类程序。该项目将为新开发的估计量建立一致性和渐近分布，并将开发有效的优化算法。该项目将推进社会经济问题空间异质性学习的前沿。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Spatial Clustering Regression of Count Value Data via Bayesian Mixture of Finite Mixtures

通过有限混合物的贝叶斯混合进行计数值数据的空间聚类回归

• DOI: 10.1145/3580305.3599509
• 发表时间: 2023
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Zhao, Peng;Yang, Hou-Cheng;Dey, Dipak K.;Hu, Guanyu
• 通讯作者: Hu, Guanyu