Analysis on influencing factors and spatial distribution of leisure agriculture at provincial level based on geographic big data: a case study of Zhejiang Province, China

doi:10.3969/j.issn.1004-1524.2021.07.12

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (7): 1264-1274.DOI: 10.3969/j.issn.1004-1524.2021.07.12

• Environmental Science • Previous Articles Next Articles

Analysis on influencing factors and spatial distribution of leisure agriculture at provincial level based on geographic big data: a case study of Zhejiang Province, China

YE Han^a(), WU Bowen^a, XU Hongwei^a, GAN Muye^a, ZHANG Jing^a^,^b, WANG Ke^a^,^*()

a. Institute of Agricultural Remote Sensing and Information Technology Application
b. The Rural Development Academy, Zhejiang University, Hangzhou 310058, China

Received:2020-08-12 Online:2021-07-25 Published:2021-08-06
Contact: WANG Ke

Abstract

Abstract:

Most of the current studies on the spatial distribution of leisure agriculture are based on the traditional survey data. With the development of online electronic maps, POI (point of interest) data and driving time data provide new big data sources, yet there are a few examples of this kind of big-data-based study on spatial distribution of leisure agriculture at the provincial level. Based on POI data from Gaode Map and driving time data from Baidu Map, with methods including Nearest Neighbor Index and Kernel Density Estimation, and implementation of Geographic Detector, tthe spatial distribution characteristics and influencing factors of leisure agriculture in Zhejiang Province were studied. It is shown that leisure agriculture has a multi-core agglomeration distribution at the provincial level, with apparent dependence of cities and scenic spots, forming core dense area including Hangzhou-Huzhou junction and Anji County, as well as several urban dependent and scenic dependent dense areas. The types of leisure vacationing, rural farm work experiencing, agritainment and back-to-nature entertainment show continuous clusters due to geographic location and terrain characteristics, while the folk custom experiencing and villages and townships sightseeing types show weak geographic links and are more evenly distributed. The number of science and education touring type POIs is quite small. The levels of economic and agricultural development, together with income and consumption capability, are important social and economic factors affecting the intensive distribution of leisure agriculture. Overall, in areas with certain natural resources, high levels of economic and agricultural development, or where residents have relatively high consumption capability and the tourist market is large, or where transportation is relatively convenient, a better environment for leisure agriculture development is likely to be provided.

Key words: Zhejiang Province, leisure agriculture, spatial distribution, geographic big data, influencing factors

CLC Number:

K909

YE Han, WU Bowen, XU Hongwei, GAN Muye, ZHANG Jing, WANG Ke. Analysis on influencing factors and spatial distribution of leisure agriculture at provincial level based on geographic big data: a case study of Zhejiang Province, China[J]. Acta Agriculturae Zhejiangensis, 2021, 33(7): 1264-1274.

Figures/Tables 7

References 30

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地级市 Prefecture-level city	POI数 Number of POI	占比 Proportion/%
杭州市Hangzhou	5 471	23.81
湖州市Huzhou	2 405	10.47
嘉兴市Jiaxing	924	4.02
宁波市Ningbo	2 260	9.83
绍兴市Shaoxing	1 308	5.69
舟山市Zhoushan	1 968	8.56
台州市Taizhou	1 496	6.51
温州市Wenzhou	2 139	9.31
衢州市Quzhou	1 614	7.02
金华市Jinhua	1 704	7.42
丽水市Lishui	1 691	7.36

地级市 Prefecture-level city	POI数 Number of POI	占比 Proportion/%
杭州市Hangzhou	5 471	23.81
湖州市Huzhou	2 405	10.47
嘉兴市Jiaxing	924	4.02
宁波市Ningbo	2 260	9.83
绍兴市Shaoxing	1 308	5.69
舟山市Zhoushan	1 968	8.56
台州市Taizhou	1 496	6.51
温州市Wenzhou	2 139	9.31
衢州市Quzhou	1 614	7.02
金华市Jinhua	1 704	7.42
丽水市Lishui	1 691	7.36

类型Type	NNI
整体Overall	0.25
田园农业体验类Rural farm work experiencing	0.52
民俗风情类Folk custom experiencing	0.66
农家乐类Agritainment	0.36
村落乡镇类Villages and townships sightseeing	0.70
休闲度假类Leisure vacationing	0.33
科教旅游类Science and education touring	0.59
回归自然类Back-to-nature entertainment	0.48

类型Type	NNI
整体Overall	0.25
田园农业体验类Rural farm work experiencing	0.52
民俗风情类Folk custom experiencing	0.66
农家乐类Agritainment	0.36
村落乡镇类Villages and townships sightseeing	0.70
休闲度假类Leisure vacationing	0.33
科教旅游类Science and education touring	0.59
回归自然类Back-to-nature entertainment	0.48

自变量指示类型 Indication type of variables	自变量 Variable	意义 Meaning	q	P
经济发展水平	X₁	人均GDP Per capita GDP/yuan	0.102	<0.001
Levels of economic development	X₂	城镇化率Urbanization rate/%	0.102	<0.001
收入和消费能力	X₃	城镇居民人均可支配收入Per capita disposable income of urban residents/yuan	0.085	<0.001
Income and consumption	X₄	农村居民人均可支配收入Per capita disposable income of rural residents/yuan	0.083	<0.001
capability	X₅	社会消费品零售总额Total retail sales of consumer goods/(10⁸ yuan)	0.102	<0.001
	X₆	住户存款年末余额Year-end balance of household deposits/(10⁸ yuan)	0.102	<0.001
农业发展水平	X₇	农作物播种面积Sown area of crops/(10³ hm²)	0.102	<0.001
Levels of agricultural	X₈	农业产值Agricultural output value/(10⁴ yuan)	0.102	<0.001
development
旅游资源禀赋	X₉	AAA~AAAAA级景点数 Number of AAA-AAAAA scenic spots	0.025	0.022
Tourism resources	X₁₀	2016年游客人次Visitors in 2016/10⁴	0.102	<0.001
	X₁₁	榜单上榜数Number in rankings	0.016	0.094

Analysis on influencing factors and spatial distribution of leisure agriculture at provincial level based on geographic big data: a case study of Zhejiang Province, China

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