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.
| Original language | English (US) |
|---|---|
| Title of host publication | Machine Learning for Data Science Handbook |
| Subtitle of host publication | Data Mining and Knowledge Discovery Handbook, Third Edition |
| Publisher | Springer International Publishing |
| Pages | 401-422 |
| Number of pages | 22 |
| ISBN (Electronic) | 9783031246289 |
| ISBN (Print) | 9783031246272 |
| DOIs | |
| State | Published - Jan 1 2023 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2023.