基于图的健康相关面状数据空间分割
Graph-Based Spatial Segmentation of Health-Related Areal Data
AI总结：
针对含噪声面状数据的不连续空间过程估计问题，提出一种基于图的快速分段常数空间估计方法，比融合套索模型更稀疏，可识别健康相关区域，辅助政策制定。
摘要：
平滑处理常被用于提升含噪声面状数据的可读性与可解释性，但在很多场景下，基础量存在不连续性，此时需要特定方法来估计分段常数空间过程。该场景下一种知名方法是利用邻接图对信号进行分割，即基于图的融合套索（graph-based fused lasso），但该方法无法很好地适配大规模图的计算需求。本文提出一种新的分段常数空间估计方法，一是在大规模图上计算速度快，二是（在相同正则化程度下）生成的模型比融合套索更稀疏，估计结果更易解释。我们在模拟数据上验证了该方法，并将其应用于荷兰超重患病率的真实数据，识别出无法用人口或社会经济特征解释的健康区与不健康区。研究发现，该方法能够识别这类区域，可辅助政策制定者制定健康改善策略。该方法的R语言实现已在github.com/goepp/graphseg公开。
英文摘要：
Smoothing is often used to improve the readability and interpretability of noisy areal data. However there are many instances where the underlying quantity is discontinuous. In this case, specific methods are needed to estimate the piecewise constant spatial process. A well-known approach in this setting is to perform segmentation of the signal using the adjacency graph, as does the graph-based fused lasso. But this method does not scale well to large graphs. This article introduces a new method for piecewise-constant spatial estimation that (i) is fast to compute on large graphs and (ii) yields sparser models than the fused lasso (for the same amount of regularization), giving estimates that are easier to interpret. We illustrate our method on simulated data and apply it to real data on overweight prevalence in the Netherlands. Healthy and unhealthy zones are identified which cannot be explained by demographic of socio-economic characteristics. We find that our method is capable of identifying such zones and can assist policy makers with their health-improving strategies. The implementation of our method in R is publicly available at github.com/goepp/graphseg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
