Model solves key challenge in combining mismatched geographic health data

Combining data across mismatched maps is a key challenge in global health and environmental research. A powerful modeling approach has been developed to enable faster and more accurate integration of spatially misaligned datasets, including air pollution prediction and disease mapping. The study is published in the journal Stochastic Environmental Research and Risk Assessment.

This post was originally published on this site