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Spatial distribution pattern of the customer count and satisfaction of commercial facilities based on social network review data in Beijing, China

机译：基于社交网络评论数据的中国北京市商业设施客户数量和满意度的空间分布模式

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摘要

In cities, commercial facilities play a very important role in economic growth and urban development. Current studies often discuss the spatial distribution of commercial facilities. The spatial distribution and relevant influencing factors of the customer count and satisfaction of commercial facilities, however, has rarely been considered. In this paper, a Weighted Network-constrained Kernel Density Estimation is applied to social network review data to analyze the spatial distribution of customer count and satisfaction of commercial facilities. We found differences in the spatial distribution of customer count, satisfaction, and the location of commercial facilities. To analyze these spatial differences, we present a new method for quantitative analysis using the Network-constrained Local Getis-Ord's General G* as an indicator. Road segments with high-value spatial clustering or low-value spatial clustering were detected, reflecting the spatial distribution pattern of the customer count and satisfaction of commercial facilities. The Network-constrained K-Function was used to explore the spatial clustering pattern of commercial facilities as well as the correlation between the spatial distribution of commercial facilities and other POI data, such as subway stations or business centers. The results of these analyses provide a quantitative reference when deciding locations for commercial facilities, and can help us to identify problems in commercial facility services to improve the quality of life among urban residents.
机译：在城市中，商业设施在经济增长和城市发展中起着非常重要的作用。当前的研究经常讨论商业设施的空间分布。然而，很少考虑客户数量和商业设施满意度的空间分布及其相关影响因素。本文将加权网络约束的内核密度估计应用于社交网络评论数据，以分析客户数量的空间分布和商业设施的满意度。我们发现客户数量，满意度和商业设施位置的空间分布存在差异。为了分析这些空间差异，我们提出了一种使用网络约束的本地Getis-Ord's General G *作为指标进行定量分析的新方法。检测到具有高价值空间聚类或低价值空间聚类的路段，反映了客户数量的空间分布模式和商业设施的满意度。网络约束的K函数用于探索商业设施的空间聚类模式，以及商业设施的空间分布与其他POI数据（如地铁站或商业中心）之间的相关性。这些分析的结果为确定商业设施的位置提供了定量参考，并且可以帮助我们发现商业设施服务中的问题，以改善城市居民的生活质量。

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