Abstract

Predicting the footfall in a new brick-and-mortar shop (and thus, its prosperity), is a problem of strategic importance in business. Few previous attempts have been made to address this problem in the context of big data analytics in smart cities. These works propose the use of social network check-ins as a proxy for business popularity, concentrating however only on singular business types. Adding to the existing literature, we mine a large dataset of high temporal granularity check-in data for two medium-sized cities in Southern and Northern Europe, with the aim to predict the evolution of check-ins of new businesses of any type, from the moment that they appear in a social network. We propose and analyze the performance of three algorithms for the dynamic identification of suitable neighbouring businesses, whose data can be used to predict the evolution of a new business. Our SmartGrid algorithm reaches a performance of being able to accurately predict the evolution of 86% of new businesses. In this paper, extended from our original contribution at IEEE InteEnv’19, we further investigate the influence of neighbourhood venues in prediction accuracy, depending on their exhibited weekly data patterns.

中文翻译：

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使用城市社交数据挖掘预测零售业务成功

摘要

预测一家新的实体店的客流量（以及由此带来的繁荣），在企业中具有战略重要性。在智能城市中进行大数据分析的背景下，很少有人尝试解决此问题。这些作品提出使用社交网络签到作为企业受欢迎程度的代理，但是仅专注于单个企业类型。在现有文献的基础上，我们为南欧和北欧的两个中型城市挖掘了一个大型的高时间粒度登记数据集，目的是预测各种类型的新企业的登记过程从他们出现在社交网络中的那一刻。我们提出并分析了三种算法的性能，用于动态识别合适的邻近企业，其数据可用于预测新业务的发展。我们的SmartGrid算法具有能够准确预测86％的新业务发展的性能。在本文中，从我们在IEEE InteEnv'19上所做的最初贡献扩展而来，我们将进一步研究邻域场所对预测准确性的影响，这取决于它们每周展示的数据模式。