Cities around the world are interested in using information and communications technology to solve their urban problems. The smart city concept has been widely adopted as a solution, but the range of services offered in smart cities varies by city and region. The differences are due to a variety of factors, including urban characteristics, social needs, and governmental structures. In Korea, the National Strategic Smart City Program (NSSP) was launched to establish a new technological ecosystem and standards for smart cities. In this study, we introduce the smart city services being developed by the Korean NSSP and compare them to services offered in 15 smart cities in Europe, Asia, and North America. The NSSP services are characterized by applications of 5G telecommunication technology and the definition of its data structure. These characteristics are unique; unlike other existing smart city policies, the NSSP aims to widen the industrial territory of the smart city itself. In conclusion, smart city policies in recent years include measures for not only making cities sustainable but also creating prospective industrial areas, which requires cooperation with public information and construction systems.

Commercial gentrification is a common cause of urban transformation in modern cities. Former residential and industrial areas are being transformed into commercial ones specializing in the food and beverage (F&B) industry because of their unique atmosphere. However, F&B businesses in the gentrified commercial areas are prone to intense competition for survival that debate on the emerging commercial street's sustainability. Thus, this study analyzed F&B street businesses' survival characteristics where commercial gentrification has occurred. The research site was selected among Seoul's commercial streets by performing data analysis. The Cox proportional hazards model and urban spatial indicators were used to analyze the factors affecting survival time. The research findings were as follows: First, physical attractiveness, such as openness or visual accessibility, lowered the closure risk. Second, accessibility to cultural facilities or open spaces was beneficial for the nearby F&B demands; however, it occasionally intensified the competition. Finally, competitive businesses' agglomeration increased the closure risk, while non-competitive ones' agglomeration and diversification decreased it. This research's results could be useful for policymakers in promoting a more sustainable business environment for F&B entrepreneurs.

Artificial intelligence (AI) has demonstrated its ability to complete complex tasks in various fields. In urban studies, AI technology has been utilized in some limited domains, such as control of traffic and air quality. This study uses AI to better understand diverse urban studies data through a novel approach that uses a convolutional neural network (CNN). In this study, a building outline in the form of a two-dimensional image is used with its corresponding metadata to test the applicability of CNN in reading urban data. MobileNet, a high-efficiency CNN model, is trained to predict the location of restaurants in each building in Seoul, Korea. Consequently, using only 2D image data, the model satisfactorily predicts the locations of restaurants (AUC = 0.732); the model with 2D images and their metadata has higher performance but has an overfitting problem. In addition, the model using only 2D image data accurately predicts the regional distribution of restaurants and shows some typical urban forms with restaurants. The proposed model has several technical limitations but shows the potential to provide a further understanding of urban settings.