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Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper)

Authors Yigong Hu , Binbin Lu , Richard Harris , Richard Timmerman



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Author Details

Yigong Hu
  • School of Geographical Sciences, University of Bristol, UK
Binbin Lu
  • School of Remote Sensing and Information Engineering, Wuhan University, Hubei, China
Richard Harris
  • School of Geographical Sciences, University of Bristol, UK
Richard Timmerman
  • School of Geographical Sciences, University of Bristol, UK

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Yigong Hu, Binbin Lu, Richard Harris, and Richard Timmerman. Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 40:1-40:7, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.40

Abstract

Traditional geographically weighted regression and its extensions are important methods in the analysis of spatial heterogeneity. However, they are based on distance metrics and kernel functions compressing differences in multidimensional coordinates into one-dimensional values, which rarely consider anisotropy and employ inconsistent definitions of distance in spatio-temporal data or spatial line data (for example). This article proposes a general framework for locally weighted spatial modelling to overcome the drawbacks of existing models using geographically weighted schemes. Underpinning it is a multi-dimensional weighting scheme based on density regression that can be applied to data in any space and is not limited to geographic distance.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • Spatial heterogeneity
  • Multidimensional space
  • Density regression
  • Spatial statistics

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References

  1. Chris Brunsdon, A. Stewart Fotheringham, and Martin E. Charlton. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4):281-298, February 1996. URL: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x.
  2. Bo Huang, Bo Wu, and Michael Barry. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3):383-401, March 2010. URL: https://doi.org/10.1080/13658810802672469.
  3. Maryam Kordi and A. Stewart Fotheringham. Spatially Weighted Interaction Models (SWIM). Annals of the American Association of Geographers, 106(5):990-1012, September 2016. URL: https://doi.org/10.1080/24694452.2016.1191990.
  4. Binbin Lu, Martin Charlton, Paul Harris, and A. Stewart Fotheringham. Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4):660-681, April 2014. URL: https://doi.org/10.1080/13658816.2013.865739.
  5. J. A. Nelder and R. Mead. A Simplex Method for Function Minimization. The Computer Journal, 7(4):308-313, January 1965. URL: https://doi.org/10.1093/comjnl/7.4.308.
  6. Geoffrey S Watson. Smooth Regression Analysis. The Indian Journal of Statistics, Series A, 26(4):359-372, December 1964. Google Scholar
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