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GeoAI and the Future of Spatial Analytics

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New Thinking in GIScience

Abstract

This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a convergent spatial analytical framework is suggested as a potential future pathway for spatial analysis.

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Acknowledgements

This work is supported in part by National Science Foundation (NSF) under grant BCS-1853864. Li acknowledges additional funding support from NSF (BCS-1455349, GCR-2021147, PLR-2120943, and OIA-2033521). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.

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Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, 85287-5302, USA

    Wenwen Li

  2. U.S. Geological Survey, Center of Excellence for Geospatial Information Science (CEGIS), Rolla, MO, 65401, USA

    Samantha T. Arundel

Authors
  1. Wenwen Li
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  2. Samantha T. Arundel
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Corresponding author

Correspondence to Wenwen Li .

Editor information

Editors and Affiliations

  1. Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, SC, USA

    Bin Li

  2. Department of Geography, Dartmouth College, Hanover, NH, USA

    Xun Shi

  3. Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

    A-Xing Zhu

  4. Department of Geography, University of South Carolina, Columbia, SC, USA

    Cuizhen Wang

  5. School of Geography and Environment, Jiangxi Normal University, Nanchang, Jiangxi, China

    Hui Lin

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Li, W., Arundel, S.T. (2022). GeoAI and the Future of Spatial Analytics. 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_17

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