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

doi:10.13959/j.issn.1003-2398.2021.01.011

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Abstract Recently, as an emerging accommodation product, B & B has triggered changes in the tourism market. It can fully meet the personalized and diverse needs of tourists and become a new growth pole for tourism destinations. Taking Nanjing as an example, we used crawler software to obtain the data of Airbnb's B & B, relied on the DBSCAN clustering algorithm to identify the clusters of B & B in Nanjing, and analyzed the spatial distribution characteristics of B & B with the help of the spatial variation function model. Finally, the geographically weighted regression model was used to explore the impact and spatial differences of eight indicator factors in five dimensions, including transportation, commerce, population level, tourism resources, and internal relationships in the industry. The results show that: 1) The agglomeration effect of B & B space in Nanjing is significant, forming a 5-level 14-cluster of "25124" pattern and presenting a spatial layout characteristic of "two horizontal and two vertical" tic-tac-toe shape; 2) The density distribution of B & B has a certain spatial heterogeneity, and the nugget coefficient of the spatial variation function reaches 0.093, indicating that there is a strong distribution of B & B spatial autocorrelation. 3) Different factors have great heterogeneity in the direction and influence of the spatial pattern of the B & B.

Key words： B & B DBSCAN spatial pattern influencing factors Nanjing

Received: 12 March 2020

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	MA Xiao-bin
	HOU Guo-lin
	LI Li
	YANG Yan

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MA Xiao-bin,HOU Guo-lin,LI Li等. CLUSTER IDENTIFICATION, DISTRIBUTION PATERN AND INFLUENCING FACTORS OF B & B BASED ON DBSCAN: A CASE STUDY OF NANJING[J]. HUMAN GEOGRAPHY, 2021, 36(1): 84-93.

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http://rwdl.xisu.edu.cn/EN/10.13959/j.issn.1003-2398.2021.01.011 OR http://rwdl.xisu.edu.cn/EN/Y2021/V36/I1/84

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