License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2022.12
URN: urn:nbn:de:0030-drops-168971
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16897/
Go to the corresponding LIPIcs Volume Portal


Murakami, Daisuke ; Tsutsumida, Narumasa ; Yoshida, Takahiro ; Nakaya, Tomoki

Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study (Short Paper)

pdf-format:
LIPIcs-COSIT-2022-12.pdf (4 MB)


Abstract

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.

BibTeX - Entry

@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/opus/volltexte/2022/16897},
  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}
}

Keywords: Spatial prediction, Scalable geographically weighted regression, Large data, Housing price
Collection: 15th International Conference on Spatial Information Theory (COSIT 2022)
Issue Date: 2022
Date of publication: 22.08.2022


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI