Although the scalable geographically weighted regression (GWR) has been developed as a fast regression approach modeling non-stationarity, its potential on spatial prediction is largely unexplored. Given that, this study applies the scalable GWR technique for large-scale spatial prediction, and compares its prediction accuracy with modern geostatistical methods including the nearest-neighbor Gaussian process, and machine learning algorithms including light gradient boosting machine. The result suggests accuracy of our scalable GWR-based prediction.
@InProceedings{murakami_et_al:LIPIcs.COSIT.2022.12, author = {Murakami, Daisuke and Tsutsumida, Narumasa and Yoshida, Takahiro and Nakaya, Tomoki}, title = {{Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study}}, booktitle = {15th International Conference on Spatial Information Theory (COSIT 2022)}, pages = {12:1--12:5}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-257-0}, ISSN = {1868-8969}, year = {2022}, volume = {240}, editor = {Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.12}, URN = {urn:nbn:de:0030-drops-168971}, doi = {10.4230/LIPIcs.COSIT.2022.12}, annote = {Keywords: Spatial prediction, Scalable geographically weighted regression, Large data, Housing price} }
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