地理研究 ›› 2019, Vol. 38 ›› Issue (12): 2997-3009.doi: 10.11821/dlyj020190098

• 研究论文 • 上一篇    下一篇

中国县域网络购物空间格局及其影响因素

宋周莺1,2,3, 虞洋1,2,3, 祝巧玲1,2,3, 车姝韵1,2,3   

  1. 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京100101
    2. 中国科学院地理科学与资源研究所,北京100101
    3. 中国科学院大学资源与环境学院,北京100049
  • 收稿日期:2019-01-29 修回日期:2019-05-06 出版日期:2019-12-20 发布日期:2019-12-25
  • 作者简介:宋周莺(1983- ),女,浙江缙云人,博士,副研究员,硕士生导师,主要从事信息化、贸易与区域发展相关研究。E-mail: songzy@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金项目(41371006);国家自然科学基金项目(41671127)

Spatial characteristics and influencing factors of E-shopping development in China's counties

SONG Zhouying1,2,3, YU Yang1,2,3, ZHU Qiaoling1,2,3, CHE Shuyun1,2,3   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-01-29 Revised:2019-05-06 Online:2019-12-20 Published:2019-12-25

摘要:

基于阿里巴巴县域网络购物数据,分析中国1915个县域的网购格局,并利用地理探测器探讨其影响因素。结果表明:① 县域网购格局整体上以江浙为核心、从东部沿海向内陆扩散,但存在由东部沿海向华北地区延伸的高值条带及内陆散落状的高值区,低值集聚区主要在西南、东北地区。② 县域网购水平的核心影响因素是网商水平、城镇化水平、居民收入,二级影响因素是教育水平、信息化水平、物流发达程度,其他因素的影响较小。③ 不同地区县域网购水平的主要影响因素存在较大差异。除城镇化和受教育水平是所有县域的主要影响因素外,中部地区县域还受收入水平、信息化的影响,西部地区县域还受物流发达程度、经济水平、零售水平的影响,东部地区县域还受网商水平的影响。

关键词: 县域, 网络购物, 空间格局, 影响因素, 政策建议

Abstract:

It is now widely accepted that the world is moving rapidly into the information age and electronic commerce is a major component of this historic transformation. As the most important part of the electronic commerce, E-shopping has been expanding in the last decade, which has pushed forward China into the stage of nationwide online shopping. In recent years, the development of E-shopping is in its heyday, and the research of online shopping and online consumption became more and more popular. County is the new growth point of China's E-shopping and online consumption, but the relevant research is still very deficient. Under this background, based on data from Alibaba Group, this paper tries to analyze the spatial pattern of E-shopping development in China's county applying spatial autocorrelation method, and then discusses its impact factors using Geodetector method. The results show that: (1) The development of E-shopping declined from southeastern coastal counties to the inland counties. On one hand, the counties with high SI concentrated in coastal China, especially in Jiangsu and Zhejiang provinces. There are some high SI counties located along the strips extending from eastern coastal region to north China, and few high SI counties are scattered in inland China. On the other hand, the counties with low SI are concentrated in southwestern and northeastern China. (2) Geodetector results show that, the first-level significant influencing factors of counties' E-shopping pattern are the development of E-business (bi), regional urbanization (urb) and resident income (inc); the second-level factors are population education level (edu), regional information development level (ict) and local logistic system (lgt); while other factors have negligible impacts. (3) There are notable differences in the main influencing factors of county's E-shopping level in different regions. Except that regional urbanization (urb) and population education level (edu) are the main influencing factors of all counties in China, counties in Central China are also impacted by resident income (inc) and regional information development level (ict); counties in Western China are also impacted by local logistic system (lgt), economic development (eco) and local retail industry (ret); counties in Eastern China are also impacted by local E-business (bi). Overall, with the decline of county's SI value from Eastern to Western China, the impact of regional urbanization (urb) and population education level (edu) has increased, while the impact of resident income (inc), local logistic system (lgt), information development level (ict) and local E-business (bi) has decreased.

Key words: county-level, E-shopping, spatial pattern, impact factors, policy implication