A case study of hybrid statistical models for analyzing spatio-temporal effects in historical map series of Donau-Ries county
Abstract
This case study compares generalized linear models, generalized additive models, and semi-structured deep distributional regression for analyzing settlement patterns in historical land-cover maps. Using four reference years for Donau-Ries county (Landkreis Donau-Ries) in southern Germany, we quantify how distances to agricultural areas, forests, and rivers relate to residential presence, and we evaluate predictive performance through spatial block cross-validation. Classical models yield stable and interpretable distance effects, whereas the semi-structured framework improves prediction once local image context is included. The analysis illustrates a practical trade-off between interpretability, accuracy, and robustness when interpretable covariates are combined with deep components in spatio-temporal data analysis.
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BibTeX
@misc{fangueng2026case,
title = {{A case study of hybrid statistical models for analyzing spatio-temporal effects in historical map series of Donau-Ries county}},
author = {Mireille Fangueng and Frank Thiemann and Monika Sester and Philipp Otto},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40513},
eprint = {2610.40513},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40513},
}
RIS
TY - UNPB
TI - A case study of hybrid statistical models for analyzing spatio-temporal effects in historical map series of Donau-Ries county
AU - Mireille Fangueng
AU - Frank Thiemann
AU - Monika Sester
AU - Philipp Otto
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40513
UR - https://arxiv.org/abs/2610.40513
AB - This case study compares generalized linear models, generalized additive models, and semi-structured deep distributional regression for analyzing settlement patterns in historical land-cover maps. Using four reference years for Donau-Ries county (Landkreis Donau-Ries) in southern Germany, we quantify how distances to agricultural areas, forests, and rivers relate to residential presence, and we evaluate predictive performance through spatial block cross-validation. Classical models yield stable and interpretable distance effects, whereas the semi-structured framework improves prediction once local image context is included. The analysis illustrates a practical trade-off between interpretability, accuracy, and robustness when interpretable covariates are combined with deep components in spatio-temporal data analysis.
ER -