Spatial Clustering for Carolina Breast Cancer Study.

Q2 Computer Science
Hongqian Niu, Melissa Troester, Didong Li
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引用次数: 0

Abstract

In the Carolina Breast Cancer Study (CBCS), clustering census tracts based on spatial location, demographic variables, and socioeconomic status is crucial for understanding how these factors influence health outcomes and cancer risk. This task, known as spatial clustering, involves identifying clusters of similar locations by considering both geographic and characteristic patterns. While standard clustering methods such as K-means, spectral clustering, and hierarchical clustering are well-studied, spatial clustering is less explored due to the inherent differences between spatial domains and their corresponding covariates. In this paper, we introduce a spatial clustering algorithm called Gaussian Process Spatial Clustering (GPSC). GPSC leverages the flexibility of Gaussian Processes to cluster unobserved functions between different domains, extending traditional clustering techniques to effectively handle geospatial data. We provide theoretical guarantees for GPSC's performance and demonstrate its capability to recover true clusters through several empirical studies. Specifically, we identify clusters of census tracts in North Carolina based on socioeconomic and environmental indicators associated with health and cancer risk.

卡罗莱纳州乳腺癌研究的空间聚类。
在卡罗莱纳乳腺癌研究(CBCS)中,基于空间位置、人口变量和社会经济地位的人口普查区聚类对于理解这些因素如何影响健康结果和癌症风险至关重要。这个任务被称为空间聚类,包括通过考虑地理和特征模式来识别相似位置的集群。虽然标准聚类方法,如K-means、光谱聚类和分层聚类已经得到了很好的研究,但由于空间域及其相应协变量之间的内在差异,对空间聚类的探索较少。本文介绍了一种空间聚类算法高斯过程空间聚类(GPSC)。GPSC利用高斯过程的灵活性在不同域之间对未观察到的函数进行聚类,扩展了传统的聚类技术来有效地处理地理空间数据。我们为GPSC的性能提供了理论保证,并通过几个实证研究证明了其恢复真实集群的能力。具体而言,我们根据与健康和癌症风险相关的社会经济和环境指标确定了北卡罗来纳州人口普查区的集群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
4.50
自引率
0.00%
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