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Spatial Data Science

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Yan Li

    (University of Minnesota)

  • Yiqun Xie

    (University of Minnesota)

  • Shashi Shekhar

    (University of Minnesota)

Abstract

Spatial data science is a multi-disciplinary field that applies scientific methods to acquire, store, and manage spatial data, as well as to retrieve previously unknown, but potentially useful and non-trivial knowledge and insights from the data. Spatial data science is important for societal applications in public health, public safety, agriculture, environmental science, climate, etc. The challenges of spatial data science are brought about by its interdisciplinary nature and the unique properties of spatial data, such as spatial autocorrelation and spatial heterogeneity. In this section, we discuss spatial data science in its life cycle: data acquisition, data storage, data mining, result validation, and domain interpretation.

Suggested Citation

  • Yan Li & Yiqun Xie & Shashi Shekhar, 2023. "Spatial Data Science," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 401-422, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_18
    DOI: 10.1007/978-3-031-24628-9_18
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