Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 23 July 2018 / Approved: 23 July 2018 / Online: 23 July 2018 (10:03:10 CEST)

A peer-reviewed article of this Preprint also exists.

Yi, C.; Kim, K. A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region. Sustainability 2018, 10, 2996. Yi, C.; Kim, K. A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region. Sustainability 2018, 10, 2996.

Journal reference: Sustainability 2018, 10, 2996
DOI: 10.3390/su10092996

Abstract

This study aimed to ascertain 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 involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by 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 the 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 have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

Subject Areas

residential relocation distance; residential movement; machine learning; decision tree regression; Seoul metropolitan region

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