Project Summary/Abstract The last decade has seen an explosion of interest in statistical modeling and analysis of spatiotemporally misaligned data and change-of-support problems, where different variables of scientific interest are observed at disparate scales making them difficult to be coherently modeled. This is especially relevant in environmental public health, where exposure data may be based upon data from monitoring data networks, while climate data are usually available as rasterized outputs from numerical models. The situation is further compounded by our objective of associating these factors with health outcomes (e.g. disease incidence, hospitalizations, mortality and so on), which are reported by public health sources as aggregated data over regions rather than at points. Furthermore, public health researchers today routinely encounter datasets exhibiting high-dimensional spatial misalignment or change-of-support, where “dimension” refers to one or all of the following: (a) the number of spatial units (e.g., geographically referenced coordinates), (b) the number of temporal units (time points) at which the variables have been observed, and (c) the number of outcomes and other variables being studied. We propose a versatile collection of easily implementable and innovative Bayesian statistical methods that, in conjunction with appropriate software, will offer more comprehensive and statistically reliable mapping and analysis for misaligned spatiotemporal data in high-dimensional settings. Our methods and software will help spatial analysts to establish relationships among health outcomes and environmetal and climate-related predictors. Our dissemination efforts will deliver our methodology to a far broader audience of health and environmental researchers and administrators than is currently accessible.