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.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited