Enki Yoo

Modeling ambient air pollution using multiscale spatiotemporal data fusion

Modeling ambient air pollution using multiscale spatiotemporal data fusion.

E.H. Yoo

My primary research focuses on multiscale geo-spatial data fusion with applications to air pollution exposure and health. Technological advancements potentially increase data availability, but also these data often vary widely in their resolutions in space and time, as well as in their quality. I am currently advancing my past work on spatial data fusion, which addresses differences in the resolutions of data and boundary conditions, to accommodate spatially and temporally indexed data with heterogeneous quality. The flexibility and a wide applicability of my current work contributes to both Geographical Information Science and spatial statistics, and has the potential to improve air quality modeling in epidemiological studies.