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Updated: Jan 9, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
[A Study on Spatial Autocorrelation according to the Geographical Distribution of Major Health Indicators: Focusing
Gyeongmin Lee1, MyungBae Park2, EunAh Kim3
1단국대학교 의과대학 의예과.
Abstract:
Long-term strategies are necessary to prevent and manage chronic diseases owing to the aging of the population in the Chungcheong region. Geographical connections with nearby areas were determined by identifying the spatial distribution characteristics of major health indicators at the city, county, and district levels. Consequently, factors such as the depression experience rate and prevalence of hypertension and diabetes mellitus were interpreted to be insignificant. Most of the major health indicators (in addition to the ratio of single-person households, number of essential medical clinics, and need for unmet medical care) comprised the spatial clustering of adjacent communities within the Chungcheong area. This study presented the current status of regional health gaps from various viewpoints by schematizing the health indicators for each city, county, and district. Thus, this study aimed to suggest public health intervention strategies through the identification of significant characteristics of the Chungcheong area compared to the entire country.
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