Forecasting the urbanization dynamics in the Seoul metropolitan area using a long short-term memory-based model

Abstract

Over the past half century, the Seoul metropolitan area (SMA) has experienced rapid urbanization. Urban development and population growth within the SMA have caused various problems, such as a lack of affordable housing, traffic congestion, and socioeconomic inequality between the SMA and the rest of the country. As a solution, growth control was adopted, but it resulted in increasing housing prices within Seoul. In late 2018, skyrocketing housing prices forced Seoul's government to abandon its growth-control policy and announce large-scale "new-town" projects planned outside of the city's urban growth boundary. The primary purpose of this research is to predict future urbanization dynamics by utilizing the long short-term memory (LSTM)-based prediction model. The secondary purpose is to identify the influential driving factors in urbanization that can help policy makers develop evidence-based, informed strategies. To predict future urbanization's spatial patterns in the SMA, LSTM models have been estimated under two scenarios: (A) assuming that current urbanization trends and contributing factors will remain consistent in the future and (B) considering new development plans' impacts. A comparison of the modeling results indicates that the government-driven new-town projects will help urbanize 55.8% more land by 2030. The variable influence analysis also reveals that strong growth-control measures may be necessary for areas with higher employment and homeownership rates to control rapid urbanization. However, housing supply and economic growth-related policies in Seoul's suburbs would help attract the city's population to the outskirts. The LSTM-based model yields an accurate and reliable spatial prediction in the form of visual maps, and its graphic results will assist policy makers greatly in developing effective strategies for smart urban growth management.