사회네트워크 분석을 이용한 광주 전남지역의 공간 구조 변화 및 중심지 분석
Analysis of Spatial Structures and Central Places of Gwangju and Jeonnam Region using Social Network Analysis
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When an age of low growth and population decline, population migration plays an important role in spatial structure of region. There have been many researches on migration and regional spatial structure. The purpose of this study is to examine the changes of Gwangju and Jeonnam region’s spatial structure and central area using social network analysis methods. For analysis it was used that population and migration data and passenger OD(Origin and Destination) travel data released by Statistics Korea and Korea Transport Database(KTDB). Using Gephi 0.8.2, migration and passenger OD networks were visualized, and this describe network flow and density. The results of the network centrality analysis show that the most populated village is not always network center though population mass is an important factor of central places. The average eigenvector centrality of 2010 migration is the lowest during 2005-2015, and it means few regions have high centralities. When comparing migration and travel networks, travel data is more effective than migration data in determining the central location considering spatial functions.
Abstract
When an age of low growth and population decline, population migration plays an important role in spatial structure of region. There have been many researches on migration and regional spatial structure. The purpose of this study is to examine the changes of Gwangju and Jeonnam region’s spatial structure and central area using social network analysis methods. For analysis it was used that population and migration data and passenger OD(Origin and Destination) travel data released by Statistics Korea and Korea Transport Database(KTDB). Using Gephi 0.8.2, migration and passenger OD networks were visualized, and this describe network flow and density. The results of the network centrality analysis show that the most populated village is not always network center though population mass is an important factor of central places. The average eigenvector centrality of 2010 migration is the lowest during 2005-2015, and it means few regions have high centralities. When comparing migration and travel networks, travel data is more effective than migration data in determining the central location considering spatial functions.
참고문헌 (24)
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