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Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)
GI_Forum 2018, Volume 6, Issue 1
Journal for Geographic Information Science
Julian Bruns,
Johannes Riesterer,
Bowen Wang,
Till Riedel,
Michael Beigl
S. 65 - 81 doi: 10.1553/giscience2018_01_s65
Verlag der Österreichischen Akademie der Wissenschaften doi: 10.1553/giscience2018_01_s65
Abstract:
Today, we have access to a vast amount of weather, air quality, noise or radioactivity data collected by individuals around the globe. This volunteered geographic information often contains data of uncertain and of heterogeneous quality, in particular when compared to official in-situ measurements. This limits their application, as rigorous, work-intensive data-cleaning has to be performed, which reduces the amount of data and cannot be performed in real-time. In this paper, we propose a method to evaluate dynamically learning the quality of individual sensors by optimizing a weighted Gaussian process regression using an evolutionary algorithm. The evaluation was carried out in south-west Germany in August 2016 for temperature data from the Wunderground network and the Deutsche Wetter Dienst (DWD), in total 1,561 stations. Using a 10-fold cross-validation scheme based on the DWD ground truth, we show significant improvements for the predicted sensor readings: we obtained a 12.5% improvement on the mean absolute error.
crowdsourcing air temperature; data quality assessment; Evolutionary Learning; Gaussian process regression; volunteered geographic information. Published Online:
2018/07/02 07:33:11 Document Date:
2018/06/22 07:35:00 Object Identifier:
0xc1aa5572 0x00390cc4
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.
Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum
GI_Forum is listed on the Directory of Open Access Journals (DOAJ)
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