Abstract
GIS has been widely used in geochemistry-related environmental health studies and practices to map and analyze sampling locations and spatial distribution of geochemical features and health information. In the big data era, the focus is shifting towards revealing the hidden patterns and features. This chapter explores the challenging issues of spatial analysis, machine learning, and uncertainty in such studies and practices. Spatial analysis needs to focus more on hot spot analysis and identification of spatial outliers, as well as exploration of spatially varying relationships. Machine learning can be adopted to conduct deep learning with a focus on non-linear features and their links with causal effects. The field and laboratory uncertainty of environmental geochemistry should be incorporated in GIS analysis. The analyses of the association between environment and health need to be more intelligent and accurate. GIS continues to provide useful tools to make novel findings in environmental geochemistry and health from the spatial aspect.
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Zhang, C., Xia, X., Guan, Q., Liao, Y. (2022). Challenging Issues in Applying GIS to Environmental Geochemistry and Health Studies. In: Li, B., Shi, X., Zhu, AX., Wang, C., Lin, H. (eds) New Thinking in GIScience. Springer, Singapore. https://doi.org/10.1007/978-981-19-3816-0_40
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DOI: https://doi.org/10.1007/978-981-19-3816-0_40
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