Emerging trends in geospatial artificial intelligence (geoAI): Potential applications for environmental epidemiology

Trang Vopham, Jaime E. Hart, Francine Laden, Yao Yi Chiang

Research output: Contribution to journalReview articlepeer-review

113 Scopus citations

Abstract

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

Original languageEnglish (US)
Article number40
JournalEnvironmental Health: A Global Access Science Source
Volume17
Issue number1
DOIs
StatePublished - Apr 17 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Data mining
  • Deep learning
  • Environmental epidemiology
  • Exposure modeling
  • Geospatial artificial intelligence
  • Machine learning
  • Remote sensing
  • Spatial data science
  • geoAI

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