Journal article
Uncovering spatial process heterogeneity from graph-based deep spatial regression
ISPRS journal of photogrammetry and remote sensing, Vol.232, pp.509-523
02/01/2026
DOI: 10.1016/j.isprsjprs.2025.12.008
Abstract
One can never specify the true statistical form of a complex spatial process. Model misspecification, as linearity and additivity, will lead to fundamentally flawed interpretations in the estimated coefficients, particularly in empirical geographic studies involving a large number of observations and complex generation processes. Motivated to learn representative patterns of spatial process heterogeneity, we propose a deep explainable spatial regression (XSR) framework based on graph convolutional neural networks (GCN), which bypasses the conventional parametric statistical assumptions in spatial regression modeling and generate deep spatially varying coefficients that depict the heterogeneity structure of spatial processes. introduce an analytical framework to (1) perform deep spatial regression modeling in multivariate crosssectional scenarios, (2) reconstruct spatial heterogeneity patterns from the learned deep coefficients, and explain the effectiveness of heterogeneity through a simple diagnostic test. Experiments on Greater Boston house prices modeling demonstrate better fitting performance over spatial regression baselines. The spatial patterns of deep local coefficients consistently exhibit stronger explanatory power than those derived geographically weighted regression, indicating a better representation of the true spatial process heterogeneity uncovered by graph-based deep spatial regression.
Details
- Title: Subtitle
- Uncovering spatial process heterogeneity from graph-based deep spatial regression
- Creators
- Di Zhu - University of MinnesotaSheng Wang - University of MinnesotaPeng Luo - Massachusetts Institute of Technology
- Resource Type
- Journal article
- Publication Details
- ISPRS journal of photogrammetry and remote sensing, Vol.232, pp.509-523
- DOI
- 10.1016/j.isprsjprs.2025.12.008
- ISSN
- 0924-2716
- eISSN
- 1872-8235
- Publisher
- Elsevier
- Number of pages
- 15
- Grant note
- Center for Transportation Studies, University of Minnesota; University of Minnesota System University of Minnesota; University of Minnesota System Center for Urban & Regional Affairs, University of Minnesota
- Language
- English
- Date published
- 02/01/2026
- Academic Unit
- School of Earth, Environment, and Sustainability
- Record Identifier
- 9985219301302771
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