An Essay on the Importance, Efficacy, and Management of Spatiotemporal Data
<p id="p1">Before any analytics, data mining, or machine learning project can proceed, the analyst must first “get the data right.” When spatiotemporal data are involved, the data management challenges are greatly increased. This chapter asserts that essentially all data involve location...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89846 |
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| Sumario: | <p id="p1">Before any analytics, data mining, or machine learning project can proceed, the analyst must first “get the data right.” When spatiotemporal data are involved, the data management challenges are greatly increased. This chapter asserts that essentially all data involve location and time and hence are spatiotemporal in nature. Consequently, the way that analysts approach “getting the data right” must, of necessity, proceed with a spatiotemporal perspective in mind. Herein, we present examples of spatial and temporal data and discuss various aspects that must be considered when dealing with them. Such factors include data management/curation/storage, georeferencing and map projection, spatial and temporal autocorrelation, and data quality. We also discuss a number of data pitfalls of which the analyst should be aware. We conclude with some remarks about the efficiencies to be gained when organizations take the spatiotemporal nature of data into consideration.</p> |
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| ISBN: | 978-1-83968-501-9 |
| DOI: | 10.5772/intechopen.115679 |