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A context entanglement-based geographically weighted regression method
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DOI：10.1080/15481603.2026.2713313.png)
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Abstract
En 中文
Geographically weighted regression (GWR) provides an important framework for modelling spatial non-stationarity, but its weight-generation mechanism usually relies on isotropic Euclidean distance. In complex geographic environments, physical proximity does not necessarily indicate strong geographic association, especially when adjacent locations differ sharply in spatial structure, directional configuration, or attribute conditions. To address this limitation, this study proposes a context entanglement-based geographically weighted regression (CEGWR) method. Context entanglement is defined as the non-separable coupling among geographic location, directional configuration, and attribute distribution in determining local spatial association. Instead of treating geographic distance and attribute similarity as separate sources of proximity, CEGWR represents each observation through a high-dimensional context vector that jointly encodes spatial, directional, and attribute information. These context vectors are transformed into PCA-whitened context coordinates, and contextual proximity is then used to generate local regression weights. Through this design, CEGWR reconstructs the weighting mechanism of GWR by replacing purely geometric proximity with context-dependent geographic association, while retaining the interpretable local coefficient structure of GWR. In the two simulated settings and an open-source real-world dataset examined here, CEGWR yielded lower fitting errors and AICc values than the compared GWR-type models and improved coefficient-surface recovery in the simulations. These results suggest that, for spatial processes characterized by abrupt transitions or context-dependent local associations, incorporating contextual information beyond geographic distance into the weighting scheme may improve model fitting and the estimation of spatially varying coefficients, providing a flexible local regression framework for analysing complex geographic phenomena.
Keywords:
Geographically weighted regression
context similarity
attribute similarity
contextual distance
local regression