GI_Forum 2021, Volume 9, Issue 2
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2021, Volume 9, Issue 2 ISSN 2308-1708 Online Edition ISBN 978-3-7001-9183-4 Online Edition
Oliver Hennhöfer,
Julian Bruns,
Peter Ullrich,
Andreas Heiß,
Galibjon Sharipov,
Dimitrios Paraforos
S. 136 - 151 doi:10.1553/giscience2021_02_s136 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2021_02_s136
Abstract: The assessment of spatial autocorrelation is one of the primary tasks in geographical data analysis. Identifying and examining deviations from the expected autocorrelation is key to gaining a thorough understanding of the phenomenon under investigation. Traditional measures of geospatial sciences focus on the detection of spatial clusters or spatial heteroscedasticity, often in low-dimensional data. However, phenomena are often multidimensional and interdependent – both with and without their spatial dependency – and the toolbox of geospatial sciences is not yet well developed in this regard. The present study aims to contribute to this toolbox for scientists and practitioners. The proposed approach focuses on the detection of spatial discontinuity, considering heteroscedasticity by spatially contrasting residuals from a fitted spatial error model (SEM). This contrast-enhancing technique identifies locations whose attributes differ significantly from those of the surrounding features, and with that the technique indicate spatial breaks. The approach is evaluated using agro-ecological field data to identify anomalies and was originally motivated for application in the context of precision farming. Our results enhance understanding of the underlying spatial processes of agricultural fields. The findings contribute to advanced, multidimensional, exploratory, spatial data analysis and present an alternative approach to conventional methods. Keywords: heteroscedasticity, heterogeneity, spatial autocorrelation, spatial discontinuity, spatial error model, spatial outlier detection, spatial regression Published Online: 2021/12/28 12:30:27 Object Identifier: 0xc1aa5576 0x003d25f0 Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |