Department of Biostatistics, Harvard University, T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, USA.
Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Durham, NC, USA.
Biostatistics. 2019 Jul 1;20(3):468-484. doi: 10.1093/biostatistics/kxy016.
With the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them. By defining the latent factors over the intersection of the two partitions, the model minimizes loss of information. Using simulated data constructed to mimic the spatial structure of our real data, we confirm the reliability of the model and demonstrate its superiority over competing ad hoc methods for dealing with misaligned data in spatial factor analysis. Finally, we construct an index of community social vulnerability for each census tract in Louisiana, a state prone to environmental disasters, which could be exacerbated by climate change, by applying the joint spatial factor analysis model to a set of misaligned social indicator data from the state. To demonstrate the utility of this index, we integrate it with Louisiana flood insurance claims data to identify communities that may be at particularly high risk during natural disasters, based on both social and geographic features.
随着气候变化威胁的迫近，公共卫生界不仅有兴趣根据地理特征，而且有兴趣根据社会特征来确定受灾风险最高的社区。可以通过对每个社区的一组相关社会变量进行空间因子分析来创建社区社会脆弱性指数; 然而，目前的空间因子分析方法不适合处理空间错位数据。我们引入了一种联合空间因子分析模型，该模型可以容纳来自地理空间两个不同分区的空间数据，并确定它们背后共同的潜在因子集。通过在两个分区的交点上定义潜在因子，该模型最大限度地减少了信息的损失。使用模拟数据构建，模拟我们真实数据的空间结构，我们确认了该模型的可靠性，并证明了它在处理空间因子分析中空间错位数据方面优于竞争的特定方法的优越性。最后，我们通过将联合空间因子分析模型应用于来自该州的一组错位社会指标数据，为路易斯安那州的每个普查区构建了社区社会脆弱性指数。为了展示这个指数的实用性，我们将其与路易斯安那州洪水保险理赔数据相结合，根据社会和地理特征，确定在自然灾害期间可能面临特别高风险的社区。
