Document Open Access Logo

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

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

PDF

File

Thumbnail PDF
PDF
LIPIcs.COSIT.2022.12.pdf
  • Filesize: 3.54 MB
  • 5 pages

Document Identifiers

Subject Classification

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

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.

Cite As Get 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

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.

References

  1. Chris Brunsdon, A Stewart Fotheringham, and Martin Charlton. Some notes on parametric significance tests for geographically weighted regression. Journal of regional science, 39(3):497-524, 1999. Google Scholar
  2. Noel Cressie. Statistics for spatial data. John Wiley & Sons, 2015. Google Scholar
  3. Abhirup Datta, Sudipto Banerjee, Andrew O Finley, and Alan E Gelfand. Hierarchical nearest-neighbor gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514):800-812, 2016. Google Scholar
  4. A Stewart Fotheringham, Chris Brunsdon, and Martin Charlton. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003. Google Scholar
  5. Melanie S Hammer, Aaron van Donkelaar, Chi Li, Alexei Lyapustin, Andrew M Sayer, N Christina Hsu, Robert C Levy, Michael J Garay, Olga V Kalashnikova, Ralph A Kahn, et al. Global estimates and long-term trends of fine particulate matter concentrations (1998-2018). Environmental Science & Technology, 54(13):7879-7890, 2020. Google Scholar
  6. Paul Harris, AS Fotheringham, R Crespo, and Martin Charlton. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences, 42(6):657-680, 2010. Google Scholar
  7. Matthew J Heaton, Abhirup Datta, Andrew Finley, Reinhard Furrer, Rajarshi Guhaniyogi, Florian Gerber, Robert B Gramacy, Dorit Hammerling, Matthias Katzfuss, Finn Lindgren, et al. Methods for analyzing large spatial data: A review and comparison. arXiv preprint, 22, 2017. URL: http://arxiv.org/abs/1710.05013.
  8. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017. Google Scholar
  9. Jin Li and Andrew D Heap. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3-4):228-241, 2011. Google Scholar
  10. Ziqi Li, A Stewart Fotheringham, Wenwen Li, and Taylor Oshan. Fast geographically weighted regression (fastgwr): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1):155-175, 2019. Google Scholar
  11. Haitao Liu, Yew-Soon Ong, Xiaobo Shen, and Jianfei Cai. When gaussian process meets big data: A review of scalable gps. IEEE transactions on neural networks and learning systems, 31(11):4405-4423, 2020. Google Scholar
  12. Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida, Tomoki Nakaya, and Binbin Lu. Scalable gwr: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels. Annals of the American Association of Geographers, 111(2):459-480, 2020. Google Scholar
  13. Tomoki Nakaya, Alexander S Fotheringham, Chris Brunsdon, and Martin Charlton. Geographically weighted poisson regression for disease association mapping. Statistics in medicine, 24(17):2695-2717, 2005. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact