This NSF-funded project will contribute new statistical methods and tools for the analysis of complex and incompatible geospatial data to advance uncertainty-aware, data-driven geographic knowledge discovery. As geospatial data become increasingly available, the complexity of geospatial data marked by complex patterns and sources of uncertainty increases dramatically, and goes well beyond the capabilities of conventional methods. This project will develop an uncertainty framework to support statistical learning of incompatible geospatial data for complex spatiotemporal patterns. This framework will be used to characterize and quantify the geospatial uncertainty in scientific modeling and practical applications. 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.
From a geostatistics perspective, this project will address the long-standing unsolved challenges in spatial analysis dealing with complex spatiotemporal patterns, heterogeneity and uncertainty of geospatial data. First, the developed framework will provide a comprehensive and practical approach to seamlessly integrate heterogeneous sources of geospatial data. Secondly, capitalizing on recent advances in deep learning technologies, the developed framework will characterize and model the complex spatiotemporal patterns implied in heterogeneous data. Thirdly, the developed framework will enable the characterization and modeling of geospatial uncertainty and the impact assessment in domain fields. 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.
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