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Open AccessArticle

A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region

by Changhyo Yi 1,* and Kijung Kim 2
1
Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
2
Department of Urban Planning and Design, University of Seoul, Seoul 02504, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(9), 2996; https://doi.org/10.3390/su10092996
Received: 23 July 2018 / Revised: 21 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
This study aimed to evaluate the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study included the Internal Migration Statistics microdata provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR using machine learning techniques, such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements are relatively farther. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information for the urban management of metropolitan residential districts and the construction of reasonable housing policies. View Full-Text
Keywords: residential relocation distance; residential movement; machine learning; decision tree regression; Seoul metropolitan region residential relocation distance; residential movement; machine learning; decision tree regression; Seoul metropolitan region
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Yi, C.; Kim, K. A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region. Sustainability 2018, 10, 2996.

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