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Article

Commercial Vacancy Prediction Using LSTM Neural Networks

1
Department of Urban Design and Planning, Hongik University, Seoul 04066, Korea
2
Department of Urban Policy and Administration, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Nađa Beretić, Arnaldo Cecchini and Valentina Talu
Sustainability 2021, 13(10), 5400; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105400
Received: 2 April 2021 / Revised: 30 April 2021 / Accepted: 7 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue Sustainable Regeneration of Degraded Urban Structures and Fabric)
Previous studies on commercial vacancy have mostly focused on the survival rate of commercial buildings over a certain time frame and the cause of their closure, due to a lack of appropriate data. Based on a time-series of 2,940,000 individual commercial facility data, the main purpose of this research is two-fold: (1) to examine long short-term memory (LSTM) as a feasible option for predicting trends in commercial districts and (2) to identify the influence of each variable on prediction results for establishing evidence-based decision-making on the primary influences of commercial vacancy. The results indicate that LSTM can be useful in simulating commercial vacancy dynamics. Furthermore, sales, floating population, and franchise rate were found to be the main determinants for commercial vacancy. The results suggest that it is imperative to control the cannibalization of commercial districts and develop their competitiveness to retain a consistent floating population. View Full-Text
Keywords: commercial vacancy; LSTM; time-series forecasting; spatial big data commercial vacancy; LSTM; time-series forecasting; spatial big data
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MDPI and ACS Style

Lee, J.; Kim, H.; Kim, H. Commercial Vacancy Prediction Using LSTM Neural Networks. Sustainability 2021, 13, 5400. https://0-doi-org.brum.beds.ac.uk/10.3390/su13105400

AMA Style

Lee J, Kim H, Kim H. Commercial Vacancy Prediction Using LSTM Neural Networks. Sustainability. 2021; 13(10):5400. https://0-doi-org.brum.beds.ac.uk/10.3390/su13105400

Chicago/Turabian Style

Lee, Jaekyung, Hyunwoo Kim, and Hyungkyoo Kim. 2021. "Commercial Vacancy Prediction Using LSTM Neural Networks" Sustainability 13, no. 10: 5400. https://0-doi-org.brum.beds.ac.uk/10.3390/su13105400

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