Spatial and big data analytics of E-market transaction in China

Xinyue Ye; Zeng Lian; Bing She; Sonali Kudva

doi:10.1007/s10708-018-09964-y

Spatial and big data analytics of E-market transaction in China

Xinyue Ye, Zeng Lian, Bing She, Sonali Kudva

Informatics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.

Original language	English (US)
Pages (from-to)	329-341
Number of pages	13
Journal	GeoJournal
Volume	85
Issue number	2
DOIs	https://doi.org/10.1007/s10708-018-09964-y
State	Published - Apr 1 2020

All Science Journal Classification (ASJC) codes

Geography, Planning and Development

Keywords

Big data
China
E-market
Spatial structure

Access to Document

10.1007/s10708-018-09964-y

Cite this

@article{8074d0cead9d426db722c618f42c3a6a,

title = "Spatial and big data analytics of E-market transaction in China",

abstract = "This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.",

keywords = "Big data, China, E-market, Spatial structure",

author = "Xinyue Ye and Zeng Lian and Bing She and Sonali Kudva",

note = "Funding Information: This study was partially supported by the National Science Foundation (Grant Number 1416509). Publisher Copyright: {\textcopyright} 2019, Springer Nature B.V.",

year = "2020",

month = apr,

day = "1",

doi = "10.1007/s10708-018-09964-y",

language = "English (US)",

volume = "85",

pages = "329--341",

journal = "GeoJournal",

issn = "0343-2521",

publisher = "Springer Netherlands",

number = "2",

}

TY - JOUR

T1 - Spatial and big data analytics of E-market transaction in China

AU - Ye, Xinyue

AU - Lian, Zeng

AU - She, Bing

AU - Kudva, Sonali

PY - 2020/4/1

Y1 - 2020/4/1

N2 - This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.

AB - This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.

KW - Big data

KW - China

KW - E-market

KW - Spatial structure

UR - http://www.scopus.com/inward/record.url?scp=85059648241&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059648241&partnerID=8YFLogxK

U2 - 10.1007/s10708-018-09964-y

DO - 10.1007/s10708-018-09964-y

M3 - Article

AN - SCOPUS:85059648241

SN - 0343-2521

VL - 85

SP - 329

EP - 341

JO - GeoJournal

JF - GeoJournal

IS - 2

ER -

Spatial and big data analytics of E-market transaction in China

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this