Spatial Analysis of the Vulnerability to Meteorological Hazards in Korea
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The purpose of this research is to provide an objective and accurate regional vulnerability index at a finer resolution with the research period from 1983 to 2012 in Korea. To find the spatial patterns and characteristics of regional vulnerability, this research conducted four different types of analyses. First, the most vulnerable regions in terms of demographic, climatological-geographic, socioeconomic, and technological factors were respectively investigated. Second, total vulnerability index combining all the four factors was examined. Next, the most influential factors deciding vulnerability and common spatial patterns of vulnerability were extracted using empirical orthogonal function (EOF) analysis. Lastly, the degree of clustering for each factor was checked using Moran’s I and local indicators of spatial association (LISA). The result found the most vulnerable provinces were Jeolla and Gyeongsang Province, regarding to demographic and climatological-geographic factors, respectively. In the case of socioeconomic factors, the difference between urban and rural areas was larger than the difference between provinces. In addition, the EOF analysis showed that demographic factors would be the most influential factors which explained 32.2 percent of the total variance of data. Lastly, climatological and geographic factors represented the highest degree of clustering (global Moran’s I: 0.51).
Abstract
The purpose of this research is to provide an objective and accurate regional vulnerability index at a finer resolution with the research period from 1983 to 2012 in Korea. To find the spatial patterns and characteristics of regional vulnerability, this research conducted four different types of analyses. First, the most vulnerable regions in terms of demographic, climatological-geographic, socioeconomic, and technological factors were respectively investigated. Second, total vulnerability index combining all the four factors was examined. Next, the most influential factors deciding vulnerability and common spatial patterns of vulnerability were extracted using empirical orthogonal function (EOF) analysis. Lastly, the degree of clustering for each factor was checked using Moran’s I and local indicators of spatial association (LISA). The result found the most vulnerable provinces were Jeolla and Gyeongsang Province, regarding to demographic and climatological-geographic factors, respectively. In the case of socioeconomic factors, the difference between urban and rural areas was larger than the difference between provinces. In addition, the EOF analysis showed that demographic factors would be the most influential factors which explained 32.2 percent of the total variance of data. Lastly, climatological and geographic factors represented the highest degree of clustering (global Moran’s I: 0.51).
참고문헌 (52)
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