A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO₂ : Prediction Accuracy, Uncertainty Quantification, and Model Interpretation

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007291
URN to cite this document: urn:nbn:de:bvb:703-epub-7291-8

Title data

Lu, Meng ; Cavieres, Joaquin ; Moraga, Paula:
A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO₂ : Prediction Accuracy, Uncertainty Quantification, and Model Interpretation.
In: Geographical Analysis. Vol. 55 (2023) Issue 4 . - pp. 703-727.
ISSN 1538-4632
DOI der Verlagsversion: https://doi.org/10.1111/gean.12356

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Further data

Item Type:	Article in a journal
DDC Subjects:	500 Science > 550 Earth sciences, geology
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Juniorprofessur Geoinformatik - Spatial Big Data > Juniorprofessur Geoinformatik - Spatial Big Data - Juniorprof. Dr. Meng Lu Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Juniorprofessur Geoinformatik - Spatial Big Data
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-7291-8
Date Deposited:	08 Nov 2023 10:20
Last Modified:	08 Nov 2023 10:21
URI:	https://epub.uni-bayreuth.de/id/eprint/7291

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