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

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

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Author Details

Daisuke Murakami
  • Institute of Statistical Mathematics, Tokyo, Japan
Narumasa Tsutsumida
  • Saitama University, Japan
Takahiro Yoshida
  • The University of Tokyo, Japan
Tomoki Nakaya
  • Tohoku University, Seindai, Japan

Funding

This research was funded by the Joint Support Center for Data Science Research at Research Organization of Information and Systems (ROIS-DS-JOINT) under Grant 006RP2018, 004RP2019, 003RP2020, and 005RP2021.

Cite AsGet BibTex

Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida, and Tomoki Nakaya. Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 12:1-12:5, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.COSIT.2022.12

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Model development and analysis
Keywords
  • Spatial prediction
  • Scalable geographically weighted regression
  • Large data
  • Housing price

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References

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