|Table of Contents|

“Commercialized”Sharing:Spatial-Temporal Evolution Characteristics andContributing Factors of Airbnb Listings in the Main Urban Area of Beijing(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
25-32
Research Field:
·地理学·
Publishing date:

Info

Title:
“Commercialized”Sharing:Spatial-Temporal Evolution Characteristics andContributing Factors of Airbnb Listings in the Main Urban Area of Beijing
Author(s):
Zhong Xing12Hou Guolin12Wen Xiaojiu1Ma Xiaobin12Li Qingqing12
(1.School of Geography,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)
Keywords:
shared accommodationcommercializationAirbnb listingspatial-temporal evolutionthe main urban area of Beijing
PACS:
K901
DOI:
10.3969/j.issn.1001-4616.2021.04.004
Abstract:
The prosperity and development of the shared accommodation industry has attracted many commercial operators into the market. The trend of commercialization is becoming more and more significant,and the attribution of “sharing”has changed quietly. Based on the data of Airbnb listings in the main urban area of Beijing from 2010 to 2019,the listings resources are divided into I,II,and III according to the degree of commercialization. The spatial and temporal distribution characteristics and spatial agglomeration causes of various Airbnb listings are calculated by using grid dimension model,DBSCAN clustering algorithm and geographic detector. The findings are listed as follows. Firstly,the proportion of II and III Airbnb listings in the main urban area of Beijing is as high as 80%,and the degree of commercialization is higher than that of overseas cities such as Boston,Lisbon and San Francisco.Secondly,the number of Airbnb listings in the main urban area of Beijing is increasing year by year,which is characterized by“phased development and stable expansion in the later period”. Thirdly,Airbnb listings in the main urban area of Beijing have obvious fractal characteristics,and the fractal structure is complex,showing“multi-center agglomeration,continuous development,fission-type diffusion”. Among them,the distribution of I listings are the most balanced,the cluster centers at all levels develop independently,and the cluster distribution of II listings are“more in the east and less in the west”,the fractal structure of III listings are underdeveloped,showing a“strong in the south and weak in the north”characteristic. Finally,development foundation,business prosperity,socio-economic factors,and public service levels are important factors influencing the agglomeration of Airbnb listings. And the higher the degree of commercialization of Airbnb listings is,the more complex the factors affecting their aggregation are.

References:

[1] HAMARI J,SJ?LINT M,UKKONEN A. The sharing economy:why people participate in collaborative consumption[J]. Journal of the association for information science and technology,2016,67(9):2047-2059.
[2]央广网. 《中国共享经济发展报告(2020)》发布[EB/OL]. [2020-03-04]. http://m.cnr.cn/tech/20200304/t20200304_525002607.html.
[3]DOGRU T,MODY M,SUESS C,et al. Airbnb 2.0:is it a sharing economy platform or a lodging corporation?[J]. Tourism management,2020,78(3):104049.
[4]DOLNICAR S. A review of research into paid online peer-to-peer accommodation:launching the annals of tourism research curated collection on peer-to-peer accommodation[J]. Annals of tourism research,2019,75(2):248-264.
[5]HORN K,MERANTE M. Is home sharing driving up rents?Evidence from Airbnb in Boston[J]. Journal of housing economics,2017,38(4):14-24.
[6]ADAMIAK C. Peer-to-peer accommodation networks:pushing the boundaries[J]. Annals of leisure research,2019,22(1):172-173.
[7]GUNTER U. What makes an Airbnb host a superhost?Empirical evidence from San Francisco and the Bay Area[J]. Tourism management,2018,66(3):26-37.
[8]XIE K,MAO Z. The impacts of quality and quantity attributes of Airbnb hosts on listing performance[J]. International journal of contemporary hospitality management,2017,83(1):80-92.
[9]LANE J,WOODWORTH R M. The sharing economy checks in:an analysis of Airbnb in the United States[R]. Los Angeles:CBRE,2016:12-14.
[10]GUTIéRREZ J,GARCíA-PALOMARES J C,ROMANILLOS G,et al. The eruption of Airbnb in tourist cities:comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona[J]. Tourism management,2017,62(5):278-291.
[11]KI D,LEE S. Spatial distribution and location characteristics of Airbnb in Seoul,Korea[J]. Sustainability,2019,11(15):4108.
[12]ZHANG Z,CHEN R J C. Assessing Airbnb logistics in cities:geographic information system and convenience theory[J]. Sustainability,2019,11(9):2462.
[13]轩源. 基于网络数据挖掘的共享住宿空间格局特征及影响因素研究——以北京为例[D]. 南京:南京师范大学,2020.
[14]马小宾,侯国林,李莉,等. 基于DBSCAN算法的民宿集群识别、分布格局及影响因素——以南京市为例[J]. 人文地理,2021,36(1):84-93.
[15]闫丽英,李伟,杨成凤,等. 北京市住宿业空间结构时空演化及影响因素[J]. 地理科学进展,2014,33(3):432-440.
[16]许志晖,戴学军,庄大昌,等. 南京市旅游景区景点系统空间结构分形研究[J]. 地理研究,2007,26(1):132-140.
[17]高旭,桂志鹏,隆玺,等. KDSG-DBSCAN:一种基于K-D Tree和Spark GraphX的高性能DBSCAN算法[J]. 地理与地理信息科学,2017,33(6):1-7.
[18]张铁映,李宏伟,许栋浩,等. 采用密度聚类算法的兴趣点数据可视化方法[J]. 测绘科学,2016,41(5):157-162.
[19]李鸣蝉,杨昆,许泉立,等. 2014年鲁甸Ms6.5级地震时空分布聚类分析[J]. 地理与地理信息科学,2018,34(2):66-72.
[20]王劲峰,徐成东. 地理探测器:原理与展望[J]. 地理学报,2017,72(1):116-134.
[21]SHARMA S. Impact of short term rentals on the rental affordability in San Francisco:the case of Airbnb[D]. Illinois:University of Illinois,2018.
[22]FRANCO S F,SANTOS C D,LONGO R. The impact of Airbnb on residential property values and rents:evidence from Portugal[J]. Regional science and urban economics,2021,88(3):103667.
[23]BENGUIGUI L,MARINOV M,CZAMANSKI D,et al. When and where is a city fractal?[J]. Environment and planning B:planning and design,2000,27(4):507-519.
[24]唐健雄,何倩. 长株潭城市群酒店业空间布局研究[J]. 经济地理,2015,35(11):78-84.
[25]童昀,马勇,刘军,等. 大数据支持下的酒店业空间格局演进与预测——武汉案例[J]. 旅游学刊,2018,33(12):76-87.
[26]李莉,侯国林,夏四友. 上海市共享住宿时空格局及影响因素识别[J]. 人文地理,2021,36(1):104-114.

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Last Update: 2021-12-15