地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (6): 1034-1046.doi: 10.12082/dqxxkx.2022.210690

• “2021中国地理信息科学理论与方法学术年会”优秀论文 • 上一篇    下一篇

大型商场顾客消费行为轨迹推断

初晨1,2(), 张恒才1,*(), 陆锋1,2   

  1. 1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2.中国科学院大学资源与环境 学院,北京 100049
  • 收稿日期:2021-10-30 修回日期:2021-12-16 出版日期:2022-06-25 发布日期:2022-08-25
  • 通讯作者: *张恒才(1985— ),男,山东济南人,博士,副研究员,主要从事立体时空计算研究。E-mail: zhanghc@lreis.ac.cn
  • 作者简介:初 晨(1998— ),男,山东青岛人,硕士,主要从事时空数据挖掘研究。E-mail: chuchen0411@igsnrr.ac.cn
  • 基金资助:
    国家重点研发计划项目(2021YFB3900803)

Inferring Consumption Behavior of Customers in Shopping Malls from Indoor Trajectories

CHU Chen1,2(), ZHANG Hengcai1,*(), LU Feng*()   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-10-30 Revised:2021-12-16 Online:2022-06-25 Published:2022-08-25
  • Supported by:
    National Key Research and Development Program of China(2021YFB3900803)

摘要:

如何获取大型商场内海量顾客消费行为一直是行为地理学面临的难点问题,而近年来爆发式增长的室内轨迹数据为这一问题解决提供了机遇,但室内轨迹的语义信息缺失、数据质量差等问题给推断顾客消费行为造成了挑战。本研究提出了一种顾及文本-轨迹的商场顾客消费行为轨迹推断框架,无需隐私敏感的顾客消费记录数据,可以获取大量顾客消费行为,该方法通过爬取室内店铺的网络文本,增强室内店铺语义属性,进而实现顾客几何轨迹到语义轨迹的转化提升,并引入了轨迹嵌入特征表示学习方法,捕捉群体轨迹之间的移动特征,综合轨迹移动特征、轨迹语义特征及顾客嵌入特征,通过高维聚类实现了大型商场顾客消费模式的推断。通过某大型商场7045位顾客的真实轨迹进行实验分析,实验结果表明,本文提出的方法与传统特征提取方法相比,聚类结果在轮廓系数上提升最高达69.8%,顾客消费行为提取准确率更高。研究发现,室内顾客移动具有一定楼层倾向性,并且室内空间结构如店铺位置、扶梯位置、功能区划分等,会影响顾客消费模式。本文提出的方法可以有效识别不同消费水平、移动特征的顾客群体,实现顾客消费行为的轨迹推断。

关键词: 室内轨迹, 轨迹挖掘, 室内消费行为, 轨迹聚类, 人群时空行为, 移动特征嵌入, 移动模式挖掘, 室内空间结构, 人群动态观测

Abstract:

How to obtain the consumption behavior of massive customers in large indoor shopping malls has always been a difficult problem in behavioral geography. However, with the explosive growth of indoor trajectory data in recent years, there's a great opportunity to solve this problem. Meanwhile, the lack of semantic information and poor data quality of indoor trajectory still pose challenges to the inference of consumer behavior. This study proposes a framework for customers' consumption behavior inference in shopping malls without collecting private personal consumption records. This framework integrates the Web text information of stores with movement features extracted from personal and historical customer trajectories. The semantic attributes of indoor stores are enhanced by introducing the crawled network text data of indoor stores, so as to realize the transformation from customer geometric trajectory to semantic trajectory. Specifically, the framework offers a method to model the customers' consumption feature from three aspects, including the raw trajectory's movement feature, semantic feature, and movement embedding feature. By employing the representation learning algorithm in extraction of customers' movement embedding feature, the framework can learn the movement pattern from the historical crowd trajectories and use the movement embedding feature to model movements of a single customer in a complex indoor environment automatically. Finally, the research realizes residents' consuming behavior inference by clustering the concatenated multi-sources consuming features and analyzing the clusters with statistic values and visualization. Through the experimental analysis of a real-world indoor trajectory dataset generated from a large shopping mall with 7045 customers, the inference result proves that the framework can effectively extract the spatial-temporal movement and consumption pattern of residents. Comparing with the classic feature extraction methods and typical clustering methods, the framework we propose achieves an improvement for up to 69.8% in the Silhouette Coefficient. This improvement illustrates that the customers' consumption behavior inferring framework we propose can identify the customers with different consuming behaviors more effectively and cluster customers' feature with high dimension more precisely. Through the analysis of indoor customer clusters' movement pattern, the research finds out that the moving behavior of all shopping mall customers are affected directly and prominently by the design of indoor environment e.g., the distribution of functional zones, location of escalators, etc. Besides, the research also finds out that customers have strong preference to consume in the identical floor. The framework we proposed can identify customer groups with different consumption levels and movement patterns and discover consuming patterns from massive shopping mall customers without knowing their personal information. The application of the framework in inferring customer behavior patterns could provide a support for relative researches in behavioral geography.

Key words: indoor trajectory, trajectory data mining, indoor consumption behavior, trajectory clustering, crowd spatial-temporal movement, movement feature embedding, movement pattern mining, indoor space structure, human dynamic observation