Spatial distribution characteristics and influencing factors of milk tea stores in Wuhan based on sDNA and OPGD models

Milk tea stores have rapidly expanded in Wuhan due to residents’ increased consumption demand. Therefore, studying the spatial distribution and influencing factors of stores is important for optimizing their layout and promoting economic development. Using milk-tea store data from Amap City’s Point of Interest function and road network data from Baidu HeatMap, we analyzed the spatial distribution characteristics of stores within the third ring road of Wuhan City using ArcGIS. We then examined the influencing factors by combining spatial design network analysis, optimal parameters-based geographical detection, and location-based service big data. Our results revealed the following: (1) The spatial distribution of stores was concentrated in areas with high closeness and betweenness centrality, forming a multi-core “northwest–southeast” distribution pattern with significant spatial positive correlation. (2) The stores’ spatial pattern was influenced by the road network betweenness and the presence of office buildings, shopping malls, shopping centers, and tourism resources. The road network betweenness had the greatest impact on the stores’ spatial distribution, while the kernel density of betweenness presented a “one major and multiple sub-core” structure consistent with that of the stores’ spatial distribution. The kernel density of closeness and betweenness regulated the formation of the stores’ core area and multiple sub-core areas, respectively, and both factors governed the stores’ spatial distribution, which was characterized by a “widely-scattered and sporadically-clustered” pattern. (3) The stores’ distribution was closely associated with the spatial and dynamic population distribution at different times of the day. By demonstrating the big data for the spatial distribution and driving factors of milk tea stores at the urban regional scale, we fill the research gap on the spatial distribution of milk tea stores at the meso-scale. Our results offer insights into the future urban planning of milk tea stores amid the current milk tea craze.