Details
| Original language | English |
|---|---|
| Title of host publication | Handbook of Big Geospatial Data |
| Place of Publication | Cham |
| Publisher | Springer International Publishing AG |
| Pages | 399-426 |
| Number of pages | 28 |
| ISBN (electronic) | 9783030554620 |
| ISBN (print) | 9783030554613 |
| Publication status | Published - 17 Dec 2020 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
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Handbook of Big Geospatial Data. Cham: Springer International Publishing AG, 2020. p. 399-426.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Spatial statistics, or how to extract knowledge from data
AU - Antoniuk, Anna
AU - Merk, Miryam S.
AU - Otto, Philipp
PY - 2020/12/17
Y1 - 2020/12/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85148969647&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-55462-0_15
DO - 10.1007/978-3-030-55462-0_15
M3 - Contribution to book/anthology
AN - SCOPUS:85148969647
SN - 9783030554613
SP - 399
EP - 426
BT - Handbook of Big Geospatial Data
PB - Springer International Publishing AG
CY - Cham
ER -