The Huff model has been widely used in location‐based business analysis to delineate a trade area containing a store’s potential customers. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor‐intensive surveys. With the increasing availability of mobile devices, users in location‐based platforms share rich multimedia information about their locations at a fine spatio‐temporal resolution, which offers opportunities for business intelligence. In this research, we present a time‐aware dynamic Huff model (T‐Huff) for location‐based market share analysis and calibrate this model using large‐scale store visit patterns based on mobile phone location data across the 10 most populated US cities. By comparing the hourly visit patterns of two types of stores, we demonstrate that the calibrated T‐Huff model is more accurate than the original Huff model in predicting the market share of different types of business (e.g., supermarkets versus department stores) over time. We also identify the regional variability where people in large metropolitan areas with a well‐developed transit system show less sensitivity to long‐distance visits. In addition, several socioeconomic and demographic factors (e.g., median household income) that potentially affect people’s visit decisions are examined and summarized.

中文翻译：

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使用位置大数据校准用于业务分析的动态Huff模型

Huff模型已在基于位置的业务分析中广泛使用，以描绘包含商店潜在客户的贸易区域。校准Huff模型及其扩展需要经验性的位置访问数据。许多研究依赖劳动密集型调查。随着移动设备可用性的提高，基于位置的平台中的用户可以以精细的时空分辨率共享有关其位置的丰富多媒体信息，这为商业智能提供了机会。在这项研究中，我们提出了一个基于时间的动态Huff模型（T-Huff），用于基于位置的市场份额分析，并根据人口稠密的美国10个城市的手机位置数据，使用大规模商店访问模式来校准该模型。通过比较两种类型商店的每小时访问模式，我们证明，经过校准的T-Huff模型在预测不同类型的业务（例如，超市与百货商店）随时间变化的市场份额方面比原始的Huff模型更为准确。我们还确定了区域差异性，大城市地区的公交系统发达的人们对长途旅行的敏感性较低。此外，还检查并总结了可能影响人们访问决策的若干社会经济和人口因素（例如家庭收入中位数）。我们还确定了区域差异性，大城市地区的公交系统发达的人们对长途旅行的敏感性较低。此外，还检查并总结了可能影响人们访问决策的若干社会经济和人口因素（例如家庭收入中位数）。我们还确定了区域差异性，大城市地区的公交系统发达的人们对长途旅行的敏感性较低。此外，还检查并总结了可能影响人们访问决策的若干社会经济和人口因素（例如家庭收入中位数）。