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The Chinese government regards the night-time economy as one of the essential means to expand domestic demand and enhance sustainable economic development. Scientifically choosing the night-time economic development path of the suburban counties of the Chinese metropolis (SCCM) and proposing a reasonable spatial matching planning strategy is an urgent problem for Chinese local governments. This study takes Anning county, a suburban Kunming metropolitan area, as the research area. Using Python to capture multi-spatial data, such as POI and Baidu heatmap, we use ArcGIS spatial analysis and statistical tools to show the spatial distribution characteristics of the night-time economic formats in Anning County. At the same time, the spatial coupling coordination model is used to calculate the coupling coordination degree of the night-time economic formats distribution and comprehensive traffic distribution (D1), night-time economic formats distribution and night-crowd vitality (D2), and the spatial coupling coordination of the three (D3). It is divided into five spatial matching levels and analyzes the shortage of night-time economic development in each subdistrict. The research results show that the spatial development of the night-time economy in Anning county is unbalanced at the current stage. The northern part of the county has a good development trend, and the Lianran subdistrict has the highest coupling coordination degree (0.995). In contrast, the southern part of the county has the lowest coupling coordination degree due to a lack of economic formats and traffic restrictions (0.115). According to the subdistricts’ differences, the sustainable development strategy of the county’s night-time economy should be formulated from the perspective of the long-term development of metropolitan areas. We hope that this research can provide valuable inspiration and a development reference for relevant countries and regions to stimulate the sustainable power of the night-time economy.
Wang et al. (Tue,) studied this question.