Geo-spotting: mining online location-based services for optimal retail store placement
Geo-spotting: mining online location-based services for optimal retail store placement
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DOI：
10.1145/2487575.2487616
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Dmytro Karamshuk;A. Noulas;S. Scellato;V. Nicosia;C. Mascolo
中科院分区：
文献类型：
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作者：
Dmytro Karamshuk;A. Noulas;S. Scellato;V. Nicosia;C. Mascolo
确定新零售店的最佳选址问题一直是过去研究的焦点，特别是在土地经济领域，因为它对企业的成功至关重要。解决这个问题的传统方法已经考虑了人口统计、收入以及附近或偏远地区的汇总人流统计数据。然而，获取相关数据通常很昂贵。随着基于位置的社交网络的发展，描述用户移动性和地点受欢迎程度的细粒度数据最近已成为可能。在本文中，我们通过使用从纽约 Foursquare 收集的数据集，研究了各种机器学习功能对城市零售商店受欢迎程度的预测能力。我们挖掘的特征基于两个一般信号：地理信号，其中特征是根据附近地点的类型和密度制定的；以及用户移动性，包括场地之间的转换或来自遥远区域的移动用户的传入流量。我们的评估表明，在分析中考虑的三个不同商业连锁店中，性能最佳的功能是常见的，尽管也可能存在差异，正如零售设施吸引用户的方式的异质性所解释的那样。我们还表明，在监督学习算法中结合多个特征时，性能会显着提高，这表明企业的零售成功可能取决于多个因素。
The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.