A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability.

ABSTRACT: 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 informat

SUBMITTER: Nethery RC 

PROVIDER: S-EPMC6659171 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature