EconPapers    
Economics at your fingertips  
 

Café and Restaurant under My Home: Predicting Urban Commercialization through Machine Learning

Seung-Chul Noh and Jung-Ho Park
Additional contact information
Seung-Chul Noh: Department of Public Administration, Hanshin University, Osan 18101, Korea
Jung-Ho Park: SURE Education Research Group, Department of Smart City, Chung-Ang University, Seoul 06974, Korea

Sustainability, 2021, vol. 13, issue 10, 1-22

Abstract: The small commercial stores opening in housing structures in Seoul have been soaring since the beginning of this century. While commercialization generally increases urban vitality and achieves land use mix, cafés and restaurants in low-rise residential areas may attract numerous passenger populations, with increased noise and crimes, in the residential area. The urban commercialization is so fast and prevalent that neither urban researchers nor policymakers can respond to it timely without a practical prediction tool. Focusing on cafés and restaurants, we propose an XGBoost machine learning model that can predict commercial store openings in urban residential areas and further play the role of an early warning system. Our findings highlight a large degree of difference in the predictor importance between the variables used in our machine learning model. The most important predictor relates to land price, indicating that economic motivation leads to the conversion of urban housing to small cafés and restaurants. The Mapo neighborhood is predicted to be the most prone to the commercialization of urban housing, therefore, its urgency to be prepared against expected commercialization deserves underscoring. Overall, our results show that the machine learning approach can be applied to predict changes in land uses and contribute to timely policy designs in rapidly changing urban context.

Keywords: commercialization; urban residential area; land use prediction model; machine learning; XGBoost; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/10/5699/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/10/5699/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:10:p:5699-:d:557756

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager (indexing@mdpi.com).

 
Page updated 2024-12-28
Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5699-:d:557756