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A new methodology of spatial cross-correlation analysis
1Department of Geography, College of Urban and Environmental Sciences, Peking University, 100871, Beijing, China.
Plos One
|May 21, 2015
Summary
This study introduces new models for spatial cross-correlation analysis, complementing existing spatial autocorrelation methods. The research offers tools to better understand complex geographical relationships and their underlying causality.
Area of Science:
- Geographic Information Science
- Spatial Statistics
- Econometrics
Background:
- Spatial autocorrelation theory is established, but spatial cross-correlation analysis requires advancement.
- Existing methods for analyzing spatial relationships are insufficient for complex cross-correlation phenomena.
Purpose of the Study:
- To develop novel models and analytical procedures for spatial cross-correlation analysis.
- To establish a theoretical framework for geographical cross-correlation modeling.
- To provide tools for visually revealing causality in spatial systems.
Main Methods:
- Defined global and local spatial cross-correlation coefficients.
- Proposed spatial cross-correlation scatterplots for visual analysis.
- Decomposed Pearson's correlation coefficient into direct and indirect components.
Main Results:
- Developed a theoretical framework for spatial cross-correlation modeling analogous to Moran's index.
- Introduced global and local coefficients and scatterplots for analyzing spatial relationships.
- Demonstrated the application of the methodology to China's urbanization and economic development.
Conclusions:
- The proposed models and procedures advance spatial cross-correlation analysis.
- The methodology offers new insights into geographical systems and causality.
- This work provides a foundation for future geographical spatial analysis.
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