Transdisciplinary foundations of geospatial data science

Yiqun Xie, Emre Eftelioglu, Reem Y. Ali, Xun Tang, Yan Li, Ruhi Doshi, Shashi Shekhar

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

Original languageEnglish (US)
Article number395
JournalISPRS International Journal of Geo-Information
Issue number12
StatePublished - Dec 2017

Bibliographical note

Publisher Copyright:
© 2017 by the authors.


  • Computer science
  • Geospatial data science
  • Mathematics
  • Statistics
  • Transdisciplinary foundations


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