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Spatial Statistics, or How to Extract Knowledge from Data

In: Handbook of Big Geospatial Data

Author

Listed:
  • Anna Antoniuk

    (European University Viadrina)

  • Miryam S. Merk

    (University of Göttingen)

  • Philipp Otto

    (Leibniz University Hannover)

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.

Suggested Citation

  • Anna Antoniuk & Miryam S. Merk & Philipp Otto, 2021. "Spatial Statistics, or How to Extract Knowledge from Data," Springer Books, in: Martin Werner & Yao-Yi Chiang (ed.), Handbook of Big Geospatial Data, chapter 0, pages 399-426, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-55462-0_15
    DOI: 10.1007/978-3-030-55462-0_15
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