Geographic Big Data’s Applications in Retailing Business Market

Chen, Xin; Xu, Fangcao; Wang, Weili; Du, Yikang; Li, Miaoyi

doi:10.1007/978-3-319-51929-6_9

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…On the other side, Galati and Greenhalgh studied the inner mobility, contact duration and inter-contact time of mall customers [19]. In the context of big-data marketing, Chen et al have used a large dataset for optimizing retail store location [20].…”

Section: Related Workmentioning

confidence: 99%

Shopping mall attraction and social mixing at a city scale

et al. 2018

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In Latin America, shopping malls seem to offer an open, safe and democratic version of the public space. However, it is often difficult to quantitatively measure whether they indeed foster, hinder, or are neutral with respect to social inclusion. In this work, we investigate if, and by how much, people from different social classes are attracted by the same malls. Using a dataset of mobile phone network records from 387,152 devices identified as customers of 16 malls in Santiago de Chile, we performed several analyses to study whether malls with higher social mixing attract more people. Our pipeline, which starts with the socioeconomic characterization of mall visitors, includes the estimation of social mixing and diversity of malls, the application of the gravity model of mobility, and the definition of a co-visitation model. Results showed that people tend to choose a profile of malls more in line with their own socioeconomic status and the distance from their home to the mall, and that higher mixing does positively contribute to the process of choosing a mall. We conclude that (a) there is social mixing in malls, and (b) that social mixing is a factor at the time of choosing which mall to go to. Thus, the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility to make some malls strong social inclusion hubs.

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Section: Related Workmentioning

confidence: 99%

Shopping mall attraction and social mixing at a city scale

et al. 2018

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“…Query refers to a request for a service by providing a location. For example, users can query their locations from an LBS provider to find restaurants nearby [1], refine route planning [2], and receive location-based advertisements [3]. The annual market for LBSs is expected to reach 77.84 billion US dollars by 2021, with an annual growth rate of 38.9% [4].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model

Shaham

Ding

Liu

et al. 2021

IEEE Trans. on Mobile Comput.

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Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.

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“…第 5 期陈洪星等：大数据视角下的商业中心和热点区分布特征及其影响因素分析集聚区识别 [18][19] 等方面作出许多卓有成效的研究。在研究方法上，学者目前大都采用空间自相关分析、区位商分析、聚类分析等空间统计分析方法进行热点区识别 [20][21][22][23] ，并利用多种计量经济模型等对商业布局的影响因素进行测度 [6,[24][25] [30] 。将街区与 POI 数据构 [19,[31][32] ； dis为空间处一点 i 到 s 处的距离； h 为带宽，带宽的选择参考 Silverman [19] 的 "经验法则" ，经计算后本文带宽确定为…”

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