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IBM PAIRS: Scalable Big Geospatial-Temporal Data and Analytics As-a-Service Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

15. Spatial Statistics, or How to Extract Knowledge from Data

verfasst von : Anna Antoniuk, Miryam S. Merk, Philipp Otto

Erschienen in: Handbook of Big Geospatial Data

Verlag: Springer International Publishing

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Abstract

In this paper, we give an overview of selected statistical models to draw (statistically significant) conclusions from data. This is particularly important to prove theoretical/physical models. For this reason, we initially describe classical modeling approaches in geostatistics and spatial econometrics. Moreover, we show how large spatial and spatiotemporal data can be modeled by these approaches. Therefore, we sketch selected suitable methods and their computational implementation. Thus, the paper should give a general guideline to analyze big geospatial data, where “big data” refers to the total number of spatially dependent observations, and to draw conclusions from these data. Finally, we compare two of these approaches by applying them to an empirical data set. To be precise, we model the particulate matter concentration (PM2.5) in Colorado.

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Vorheriges Kapitel Uncertain Spatial Data Management: An Overview
Nächstes Kapitel A Survey of Textual Data & Geospatial Technology
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Metadaten
Titel
Spatial Statistics, or How to Extract Knowledge from Data
verfasst von
Anna Antoniuk
Miryam S. Merk
Philipp Otto
Copyright-Jahr
2021
Buch
Handbook of Big Geospatial Data

Print ISBN: 978-3-030-55461-3

Electronic ISBN: 978-3-030-55462-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-55462-0_15

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