The Geographic Moran Effect: Theory, Methods, and Interpretation
Abstract
The Moran effect traditionally refers to the synchronization of population dynamics driven by shared environmental forcing rather than direct interactions among populations. Although extensively studied in ecology, the Moran effect has received limited attention in geography, and its applicability has yet to be fully explored. In this study, we build upon the classical Moran effect in population ecology as a conceptual foundation, developing a more comprehensive conceptual framework in a geographic context. We introduce the new concept Geographic Moran Effect (GME), defined as temporally coherent and synchronized variation across geographic entities induced by common external forcing, with the magnitude and potentially the direction of local responses varying according to a spatially varying sensitivity field. A two-step spatiotemporal factor framework is developed to identify and interpret the GME. The framework first tests for the possible presence of GME-type synchrony in spatiotemporal data and then examines the GME and its spatiotemporal evolution by investigating its structure through spatial and temporal dimensions separately. A synthetic experiment validates the framework's reliability, while an application to U.S. county-level presidential election data (2008-2024) demonstrates its ability to identify the GME and its spatial and temporal components. By redefining the Moran effect as a geographic theory and a distinct object of inference, this work provides a mechanism-oriented perspective on spatiotemporal analysis with broad applicability in geography, ecology, and public health, where explicitly identifying and interpreting GME-type synchrony is essential for understanding system-level dynamics.
Versions
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BibTeX
@misc{jiao2026geographic,
title = {{The Geographic Moran Effect: Theory, Methods, and Interpretation}},
author = {Zhenzhi Jiao and X. Angela Yao and Jean-Claude Thill},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.17497},
eprint = {2610.17497},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.17497},
}
RIS
TY - UNPB
TI - The Geographic Moran Effect: Theory, Methods, and Interpretation
AU - Zhenzhi Jiao
AU - X. Angela Yao
AU - Jean-Claude Thill
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.17497
UR - https://arxiv.org/abs/2610.17497
AB - The Moran effect traditionally refers to the synchronization of population dynamics driven by shared environmental forcing rather than direct interactions among populations. Although extensively studied in ecology, the Moran effect has received limited attention in geography, and its applicability has yet to be fully explored. In this study, we build upon the classical Moran effect in population ecology as a conceptual foundation, developing a more comprehensive conceptual framework in a geographic context. We introduce the new concept Geographic Moran Effect (GME), defined as temporally coherent and synchronized variation across geographic entities induced by common external forcing, with the magnitude and potentially the direction of local responses varying according to a spatially varying sensitivity field. A two-step spatiotemporal factor framework is developed to identify and interpret the GME. The framework first tests for the possible presence of GME-type synchrony in spatiotemporal data and then examines the GME and its spatiotemporal evolution by investigating its structure through spatial and temporal dimensions separately. A synthetic experiment validates the framework's reliability, while an application to U.S. county-level presidential election data (2008-2024) demonstrates its ability to identify the GME and its spatial and temporal components. By redefining the Moran effect as a geographic theory and a distinct object of inference, this work provides a mechanism-oriented perspective on spatiotemporal analysis with broad applicability in geography, ecology, and public health, where explicitly identifying and interpreting GME-type synchrony is essential for understanding system-level dynamics.
ER -