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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.40
URN: urn:nbn:de:0030-drops-189354
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18935/
Hu, Yigong ;
Lu, Binbin ;
Harris, Richard ;
Timmerman, Richard
Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper)
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.
BibTeX - Entry
@InProceedings{hu_et_al:LIPIcs.GIScience.2023.40,
author = {Hu, Yigong and Lu, Binbin and Harris, Richard and Timmerman, Richard},
title = {{Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {40:1--40:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18935},
URN = {urn:nbn:de:0030-drops-189354},
doi = {10.4230/LIPIcs.GIScience.2023.40},
annote = {Keywords: Spatial heterogeneity, Multidimensional space, Density regression, Spatial statistics}
}
