The rapid development of the Chinese economy has stimulated consumer demand and brought huge opportunities for the retail industry. Previous studies have emphasized the importance of estimating regional consumption potentiality. However, the determinants of retail sales are yet to be systematically studied, especially at the micro level. As a result, the realization of sustainable development goals in the retail industry is restricted. In this paper, we studied the determinants of retail sales from two aspects—location-based socioeconomic factors and spatial competition between shops. Using 12,500 retail shops as our sample and by adopting a grid-division strategy, we found that regional retail sales can be positively impacted by nearby population, road length, and most non-commercial points of interest (POIs). By contrast, the number of other commercial facilities, such as catering facilities and shopping malls, and the area of geographic barriers often caused negative impacts on retail sales. As to the competition effects, we found that the isolation and decentralization of shops in one area have a marginally positive effect on sales performance within a threshold distance of 226.19 m for a central grid and a threshold distance of 514.85 m for surrounding grids, respectively. This study explores the determinants of micro-level retail sales and provides decision makers with practical and realistic approaches for generating better site selection and marketing strategies, thus realizing the sustainable development goals of the retail industry.

The extraction of a road network is critical for city planning and has been widely studied in previous research using high resolution images, whereas the high cost of high-resolution remote sensing data and the complexity of its analysis also cause huge challenges for the extraction. The successful launch of a high resolution (130 m) nighttime remote sensing satellite, Luojia 1-01, provides great potential in the study of urban issues. This study attempted to extract city roads using a Luojia 1-01 nighttime lighting image. The urban regions were firstly distinguished through a threshold method. Then, an unsupervised PCNN (pulse coupled neural network) was established to extract the road networks in urban regions. A series of optimizing methods was proposed to enhance the image contrast and eliminate the residential regions along the roads. The final extraction results after optimizing were compared with OSM (OpenStreetMap) data, showing the high precision of the proposed approach with the accuracy rate reaching 83.2%. We also found the precision of city centers to be lower than suburban regions due to the influence of intensive human activities. Our study confirms the potential of Luojia 1-01 data in the extraction of city roads and provides new thought for more complex and microscopic study of city issues.