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Updated: May 10, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Spatial clusters in a global-dependence model.

Tai-Chi Wang¹, Ching-Syang Jack Yue

¹Department of Statistics, National Chengchi University, No. 64, Sec. 2, ZhiNan Rd., Wenshan District, Taipei City 11605, Taipei, Taiwan, ROC. taichi@alumni.nccu.edu.tw

Spatial and Spatio-Temporal Epidemiology

|June 4, 2013

Summary

This summary is machine-generated.

This study introduces a novel two-stage method to effectively separate and identify both global and local spatial clustering effects. The approach improves cluster detection accuracy and reduces false alarms in spatial data analysis.

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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Spatial statistics
Geographic information systems (GIS)
Epidemiology

Background:

Spatial data frequently exhibit complex clustering patterns, including both broad global trends and localized hotspots.
Differentiating between global spatial autocorrelation and local cluster effects is challenging with traditional methods.
Existing spatial scan statistics may be influenced by global clustering, potentially affecting local cluster detection accuracy.

Purpose of the Study:

To develop and validate a novel statistical approach for simultaneously analyzing global and local spatial clustering.
To enhance the accuracy of local cluster detection by mitigating the impact of global spatial dependence.
To compare the performance of the proposed method against the established spatial scan statistic.

Main Methods:

A two-stage statistical approach was employed.
The first stage involved estimating spatial autocorrelation using an Expectation-Maximization (EM) algorithm.
The second stage utilized a generalized least squares (GLS) method for cluster detection.

Main Results:

The proposed method effectively distinguishes between global and local spatial clustering components.
It demonstrated a reduction in false alarms compared to the spatial scan statistic.
Simulations and real-world data analysis (North Carolina sudden infant disease syndrome) confirmed the method's efficacy.

Conclusions:

The developed two-stage method provides a robust solution for analyzing spatial data with simultaneous global and local clustering.
This approach offers improved precision in identifying local disease clusters.
The findings have implications for public health surveillance and spatial epidemiology.