GeoAI and the Future of Spatial Analytics

Wenwen Li; Samantha T. Arundel

doi:10.1007/978-981-19-3816-0_17

GeoAI and the Future of Spatial Analytics

Wenwen Li, Samantha T. Arundel

Geographical Sciences and Urban Planning, School of (SGSUP)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

4 Scopus citations

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.

Original language	English (US)
Title of host publication	New Thinking in GIScience
Publisher	Springer Nature
Pages	151-158
Number of pages	8
ISBN (Electronic)	9789811938160
ISBN (Print)	9789811938153
DOIs	https://doi.org/10.1007/978-981-19-3816-0_17
State	Published - Jan 1 2022

Keywords

Artificial intelligence
Data-driven discovery
Deep learning
GeoAI
Spatial analysis

ASJC Scopus subject areas

General Computer Science
General Earth and Planetary Sciences

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10.1007/978-981-19-3816-0_17

Cite this

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GeoAI and the Future of Spatial Analytics

Abstract

Keywords

ASJC Scopus subject areas

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