Dr.
Guofeng Cao received a $265,058 funding support from National Science Foundation for the project proposal "''. With this support, Guofeng will develop new deep learning-based spatial statistical framework to address long-standing problems in geospatial analysis, including complex geospatial patterns, geospatial heterogeneity and geospatial uncertainty. This project will offer novel solutions to fundamental analysis, modeling, and integration problems involving geospatial data, and advance the understanding of the nature of geospatial uncertainty. This project will enhance the proper and cost-effective utilization of geospatial data, and will have broader impacts on disciplines that geospatial data are involved. Furthermore, with a public outreach on uncertainty-aware spatial thinking, this project will advance the public good by increasing the public awareness of the geospatial uncertainty and critical map reading and usage. The performances of the developed methods will be evaluated in two domain applications: spatiotemporal disease mapping in public health and modeling uncertainty of land cover changes and the impact on atmospheric models.
