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Using a people-centered approach to new urbanization, China has committed to building high-quality living environments through improving urban livability and promoting a stronger sense of belonging among residents. Retail stores serve as one of the most immediate and accessible destinations for residents’ consumption, and their spatial configuration has a direct impact on residents’ satisfaction and happiness in their daily lives. In this context, for the present study we selected the central urban area of Lanzhou City as the case study area. Based on POI data and using the daily life circle as the basic unit, we applied methods such as kernel density analysis, hotspot analysis, and the Shannon–Weaver index to analyze spatial distribution patterns of the retail industry. Furthermore, we applied Geodetector to analyze the impacts of four factors that are closely related to the retail industry: economic level, convenience level, market demand, and location. The conclusions are as follows: In the central urban area of Lanzhou, the retail industry exhibits a belt distribution pattern along the Yellow River. The density of distribution gradually decreases from the city center toward the outskirts, forming four prominent agglomeration centers. Overall, within the central urban area of Lanzhou, the spatial distribution of the retail industry at the scale of daily living circles shows that only a small proportion of the industry demonstrates noticeable clustering effects. In terms of spatial patterns, the retail industry at the scale of the daily living circles demonstrates similar characteristics in terms of diversity and agglomeration distribution. It exhibits a decreasing trend from the urban core toward the peripheral areas. The agglomeration distribution pattern of the retail industry in the central urban area of Lanzhou is considerably influenced by market demand, economic level, convenience, and location. The spatial distribution of the retail industry in the central urban area is primarily influenced by economic factors and convenience, while market demand plays a major role and location has a relatively minimal impact.

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.