摘要
对网络购物活动背后的关注行为进行分析,将有助于把握短期的、全民性的消费行为。基于百度指数大数据获取2019年天猫双十一的网络关注度,利用空间自相关分析发掘天猫双十一关注度的空间分布特征,并多元线性回归和随机森林回归探寻双十一关注度的影响因素和影响机制。结果表明:双十一关注度具有明显的首位特征和长尾特征,并且在地理空间上呈现出明显的集聚态势,高低值区分别分布于东部和西部,且有明显的界限;影响因素探寻方面,随机森林回归结果表明信息层面和消费层面的影响因素对于双十一关注度的贡献较大,但宏观尺度和中观尺度的影响因素重要性不尽相同,在宏观尺度上潜在的消费行为依赖于信息,而在中观尺度上消费能力则更具影响力。
An analysis of attentive behavior behind the online shopping activities will be helpful to grasp the short-term and universal consumption behavior.Based on the big data from Baidu Index,the network public attention of Tmall Double Eleven in 2019 was obtained.The spatial autocorrelation method was used to analyze the spatial distribution characteristics of Tmall Double Eleven.The random forest regression(RFR)was employed to explore its influencing factors and mechanisms.The main conclusions are as follows:The attention of Tmall Double Eleven was obviously characterized by first degree and long tail.In geographical space,the attention showed a significant agglomeration trend,i.e.the higher and lower attention values were respectively distributed in the east and the west and there were obvious boundaries between them.In terms of exploration of influencing factors,the results of RFR indicate that the influencing factors at the information and consumption level contribute more to the attention of Tmall Double Eleven.However,the results on the macroscale and mesoscale,the influencing factors were not of the same importance.On macroscale,the potential consumption was more dependent on information accessibility.Overall,on the mesoscale,the consumption capacity was more crucial.
作者
陈曦
闫广华
CHEN Xi;YAN Guanghua(School of Geographical Sciences,Changchun Normal University,Changchun 130032,China;Key Laboratory of Watershed Geographic Science,Nanjing Institute of Geography and Limnology,CAS,Nanjing 210008,China)
出处
《地理信息世界》
2021年第5期117-123,共7页
Geomatics World
基金
吉林省科技厅重大科技攻关项目(2019030317SF)、吉林省教育厅科学研究项目(JJKH20210878KJ)、吉林省青少年发展研究计划项目(2020jqy-zj4)、长春师范大学研究生科研创新项目(2020070)。
关键词
双十一
天猫
公众关注度
网络购物
百度指数
随机森林
Double Eleven
Tmall
public attention
online shopping
Baidu Index
random forest
