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Investigating spatial clusters of single-person households and low-income elderly single-person using penalized likelihood
Journal of the Korean Data & Information Science Society 2017;28:1257-69
Published online November 30, 2017
© 2017 Korean Data & Information Science Society.

Eunjung Song1 · Woojoo Lee2

12Department of Statistics, Inha University
Correspondence to: Woojoo Lee
Professor, Department of Statistics, Inha university, 100 Inharo, Nam-gu Incheon 22212, Korea. E-mail:
Received October 11, 2017; Revised November 7, 2017; Accepted November 13, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords : Elderly single-person households, fused lasso, penalized likelihood, singleperson households, spatial clustering