LIPIcs.GIScience.2023.40.pdf
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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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Volume:
12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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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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Supplementary Materials
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Software (Source code)
https://github.com/GWmodel-Lab/GWmodel3
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References
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