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Zhenzhi Jiao

Publications and source records attributed to Zhenzhi Jiao.

2 recordsLinked to original sources

The Geographic Moran Effect: Theory, Methods, and Interpretation

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.

stat.ME↗

Geographically Weighted Canonical Correlation Analysis: Local Spatial Associations Between Two Sets of Variables

This article critically assesses the utility of the classical statistical technique of Canonical Correlation Analysis (CCA) for studying spatial associations and proposes a new approach to enhance it. Unlike bivariate correlation analysis, which focuses on the relationship between two individual variables, CCA investigates associations between two sets of variables by identifying pairs of linear combinations that are maximally correlated. CCA has strong potential for uncovering complex multivariate relationships that vary across geographic space. We propose Geographically Weighted Canonical Correlation Analysis (GWCCA) as a new technique for exploring local spatial associations between two sets of variables. GWCCA localizes standard CCA by weighting each observation according to its spatial distance from a target location, thereby estimating location-specific canonical correlations. The effectiveness of GWCCA in recovering spatial structure and capturing spatial effects is evaluated using synthetic data. A case study of US county-level health outcomes and social determinants of health further demonstrates the empirical capabilities of the proposed method. The results indicate that GWCCA has broad potential applications in spatial data-intensive fields such as urban planning, environmental science, public health, and transportation, where understanding local multivariate spatial associations is critical.

stat.ME↗