• Spatial Information Research
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• v.23 no.3
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• pp.11-21
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• 2015

While the number of total population in Seoul has been on the constant decline for the last few years, the number of household has increased due to the rising tendency of the smaller households. In 2010, the small households in the metropolitan areas accounted for 44% of the entire households, and Statistics Korea has reported that one person household, which will take up more than 30% of the whole household, will have been the most common type of household by 2020. This reason of rise will be differently shown according to age like the preferred housing type or surrounding environments, this research is suggest to research hypothesis that distinction of age leads to the spatial distribution of one person household. Therefore, this research is to exercise a multiple regression analysis targeting on the facilities, which become the spatial distribution factor of one person household, with the independent variable gained from the concluded area calculated with the area ratio of the spatial unit followed by the service area analysis based on network. The spatial unit is the census output of Seoul, and based on this the interaction between the number of one person household according to age and the factors of its distribution. Also, the spatial regions - downtown, northeast, southeast, northwest, southwest - are designed as dummy variables and the results of each region are found out. As a result, the spatial regions occupied according to age are found to be varied - people in their 20s prefer housings near the college, 30s lease or the monthly rental housings, 40s the monthly rental housings, and over 60s the housing with the floor area of less than $40m^2$. Likewise, one person household has different types of housing environments preferred according to age, and thus a housing policy concerning this will have to be suggested.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1257-1260
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• 2017

Single-person households recently have been rapidly increasing and one reason may be the increment in elderly single-person. Since the change of living patterns is relevant to the government policy direction, it is important to understand how single-person households are clustered and which factors have influence on them. In this study, we tried to detect spatial clusters of single-person households and low-income elderly single-person households after adjusting for deprivation index. A recently developed fused lasso for Poisson data was used for data analysis and we provided the details on how to use it in R. From these analysis results, we observed the effect of socioeconomic level on the clusters and explained the reason why spatial clusters are shown after adjusting for deprivation index.