基于POI的天山北坡城市群旅游要素空间格局及影响因素

doi:10.12118/j.issn.1000-6060.2024.527

摘要/Abstract

摘要： 基于旅游“六要素”理论，应用最近邻指数、核密度分析、双变量空间自相关、Ripley’s K函数等空间分析方法，对2024年4月获取的天山北坡城市群旅游要素兴趣点（POI）数据的空间分布及关联特征进行了分析，并基于地理探测器探讨了其影响因素。结果表明：（1）各旅游要素均呈显著集聚的空间分布特征，空间集聚程度由高到低依次为：“食”>“购”>“住”>“行”>“娱”>“游”。（2）各旅游要素空间连续性较弱，呈现出“一核、一轴、多中心”的空间分布格局；县域尺度下的各旅游要素空间关联性整体较弱，“行”要素与其他要素空间关联性较强，而“游”要素与其他要素空间关联性较弱，旅游要素空间分布格局有待优化。（3）旅游“六要素”的整体空间集聚尺度特征值为33.83 km。其中，“游”要素的空间集聚尺度特征值最大（42.95 km），而“住”要素的最小（18.48 km）。（4）各影响因子之间的交互作用对旅游要素空间格局的影响要显著高于单一因子，研究表明其受到经济发展水平、基础设施及人口等多维因素的综合影响。其中，GDP、夜间灯光指数、A级景区数量、人口密度、第三产业比重等因子对旅游要素空间格局的影响最为显著。

关键词: 城市群, 旅游要素, 双变量莫兰指数, Ripley’s K函数, 地理探测器, 天山北坡

Abstract:

This study employs the theory of the “six elements” of tourism and utilizes spatial analysis methods, including nearest neighbor index, kernel density analysis, bivariate spatial autocorrelation, and Ripley’s K-function, to examine the spatial distribution and correlation characteristics of point of interest data related to tourism elements in the urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang of China based on data collected in April 2024. In addition, we explore the influencing factors using a geographical detector. The results show the following. (1) The spatial distribution characteristics of each tourism element exhibit significant concentration, with the degree of spatial agglomeration ranking from high to low as follows: “food”>“shopping”>“accommodation”>“transportation”>“entertainment”>“tourism”. (2) Each tourism element demonstrates weak spatial continuity, resulting in a distribution pattern characterized by “one core, one axis, and multiple centers”. At the county level, the spatial correlation among tourism elements is generally weak; however, a strong correlation exists between the “transportation” element and other elements, whereas the “tourism” element exhibits weak correlations, indicating a need for optimization in the spatial distribution of tourism elements. (3) The characteristic value of the overall spatial agglomeration scale of the “six elements” of tourism is 33.83 km. Among the different elements, the “tourism” factor shows the largest spatial agglomeration scale eigenvalue (42.95 km), whereas the “accommodation” factor has the smallest (18.48 km). (4) The influence of the interaction between each factor on the spatial pattern of tourism elements is significantly greater than that of any single factor. This research highlights the effects of multi-dimensional factors, including economic development level, infrastructure, and population on the spatial pattern of tourism elements, with GDP, night light index, number of A-level scenic spots, population density, and the proportion of the tertiary industry having the most significant effects.

Key words: urban agglomeration, tourism elements, bivariate Moran’s I, Ripley’s K-function, geodetector, the northern slope of Tianshan Mountains

夏梓洋, 夏云帆, 王宁, 林伟, 马丽娜, 谭晓平, 张艳珍, 焦瑞. 基于POI的天山北坡城市群旅游要素空间格局及影响因素[J]. 干旱区地理, 2025, 48(7): 1243-1254.

XIA Ziyang, XIA Yunfan, WANG Ning, LIN Wei, MA Lina, TAN Xiaoping, ZHANG Yanzhen, JIAO Rui. Spatial pattern and influencing factors of tourism elements in the urban agglomeration on the northern slope of Tianshan Mountains based on POI[J]. Arid Land Geography, 2025, 48(7): 1243-1254.

图/表 9

图1

表1

表2

影响因子及数据来源"

一级指标	二级指标	年份	数据来源
基础设施	夜间灯光指数（ $X 1$ ）	2022	国家地球系统科学数据中心（https://www.geodata.cn/）
基础设施	A级景区数量（ $X 2$ ）/个	2022	新疆维吾尔自治区（新疆生产建设兵团）文化和旅游厅（局）官方网站数据（https://wlt.xinjiang.gov.cn/；http://wtgl.xjbt.gov.cn/）
经济发展水平	GDP（ $X 3$ ）/10⁴元	2022	资源环境科学数据平台（https://www.resdc.cn/）
人口	人口密度（ $X 4$ ）/人 $∙$ km^-2	2022	美国能源部橡树岭国家实验室（ORNL， https://landscan.ornl.gov/）
产业结构	第三产业比重（ $X 5$ ）/%	2022	研究区内各区县官网发布
交通	路网密度（ $X 6$ ）/km $∙$ km^-2	2023	开放街道地图（OSM）路网数据
自然环境	年累积降水量（ $X 7$ ）/mm	2022	国家地球系统科学数据中心（https://www.geodata.cn/）
	年平均气温（ $X 8$ ）/℃	2022	国家地球系统科学数据中心（https://www.geodata.cn/）
	DEM（ $X 9$ ）/m	2010	国家地球系统科学数据中心（https://www.geodata.cn/）
	坡度（ $X 10$ ）/(°)	2010	DEM数据基于ArcGIS坡度计算得到

表2

表3

图2

表4

图3

表5

单因子影响分析"

要素类型	$X 1$	$X 2$	$X 3$	$X 4$	$X 5$	$X 6$	$X 7$	$X 8$	$X 9$	$X 10$
“食”	0.428^***	0.304^***	0.394^***	0.212^***	0.188^***	0.161^***	0.010^***	0.010^***	0.027^***	0.002
“住”	0.427^***	0.215^***	0.315^***	0.172^***	0.293^***	0.142^***	0.017^***	0.014^***	0.032^***	0.004^**
“行”	0.418^***	0.269^***	0.402^***	0.209^***	0.201^***	0.153^***	0.015^***	0.014^***	0.033^***	0.003
“游”	0.419^***	0.164^***	0.174^***	0.087^***	0.411^***	0.132^***	0.051^***	0.039^***	0.077^***	0.011^***
“购”	0.415^***	0.319^***	0.433^***	0.221^***	0.159^***	0.150^***	0.009^***	0.008^***	0.022^***	0.001
“娱”	0.448^***	0.275^***	0.325^***	0.179^***	0.245^***	0.142^***	0.019^***	0.016^***	0.040^***	0.005^***
“六要素”	0.422^***	0.336^***	0.470^***	0.246^***	0.150^***	0.145^***	0.008^***	0.009^***	0.023^***	0.003

表5

图4

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要素	POI类型	POI数量	百分比/%
“食”	餐饮场所：餐厅、咖啡冷饮、蛋糕甜品店等	38797	27.43
“住”	住宿服务相关：招待所、宾馆酒店、民宿等	4684	3.31
“行”	交通设施相关：停车场、火车站、飞机场等	12851	9.08
“游”	风景名胜相关：景点、纪念馆、公园、广场等	1075	0.76
“购”	购物服务相关：综合市场、专卖店、超市等	76525	54.10
“娱”	影剧院、度假疗养场所、娱乐场所、运动场馆等	7528	5.32

要素	平均观测距离/km	期望平均距离/km	最近邻指数	Z值	空间分布类型
“食”	0.068	1.552	0.044	-360.24	显著集聚型
“住”	0.364	4.340	0.084	-119.94	显著集聚型
“行”	0.278	2.678	0.104	-194.37	显著集聚型
“游”	2.881	9.413	0.306	-43.53	显著集聚型
“购”	0.053	1.095	0.049	-503.42	显著集聚型
“娱”	0.052	3.556	0.147	-141.51	显著集聚型
“六要素”	0.053	0.828	0.064	-673.70	显著集聚型