Spatial Data Science

Yan Li (), Yiqun Xie () and Shashi Shekhar ()
Additional contact information
Yan Li: University of Minnesota
Yiqun Xie: University of Minnesota
Shashi Shekhar: University of Minnesota

A chapter in Machine Learning for Data Science Handbook, 2023, pp 401-422 from Springer

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_18

Ordering information: This item can be ordered from
http://www.springer.com/9783031246289

DOI: 10.1007/978-3-031-24628-9_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().