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ISPRS Int. J. Geo-Inf. 2017, 6(12), 395; https://doi.org/10.3390/ijgi6120395

Transdisciplinary Foundations of Geospatial Data Science

1
Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
2
Wayzata High School, 4955 Peony Ln N, Plymouth, MN 55446, USA
*
Author to whom correspondence should be addressed.
Received: 7 October 2017 / Revised: 11 November 2017 / Accepted: 22 November 2017 / Published: 1 December 2017
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Abstract

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. View Full-Text
Keywords: geospatial data science; transdisciplinary foundations; mathematics; statistics; computer science geospatial data science; transdisciplinary foundations; mathematics; statistics; computer science
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xie, Y.; Eftelioglu, E.; Ali, R.Y.; Tang, X.; Li, Y.; Doshi, R.; Shekhar, S. Transdisciplinary Foundations of Geospatial Data Science. ISPRS Int. J. Geo-Inf. 2017, 6, 395.

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