基于不同尺度的中国运动健身场所空间差异及影响因素研究

doi:10.12118/j.issn.1000-6060.2022.153

干旱区地理 ›› 2022, Vol. 45 ›› Issue (6): 1938-1948.doi: 10.12118/j.issn.1000-6060.2022.153

基于不同尺度的中国运动健身场所空间差异及影响因素研究

赵海莉^1,²(),李家亮¹(),李开丽³,杜雨涵¹,王家明¹

1.西北师范大学地理与环境科学学院，甘肃兰州 730070
2.甘肃省绿洲资源环境与可持续发展重点实验室，甘肃兰州 730070
3.江苏第二师范学院城市与资源环境学院，江苏南京 210013

收稿日期:2022-04-11 修回日期:2022-07-01 出版日期:2022-11-25 发布日期:2023-02-01
通讯作者: 李家亮（1996-），男，硕士研究生，主要从事健康地理学等方面的研究. E-mail: 18851253591@163.com
作者简介:赵海莉（1977-），女，博士，副教授，主要从事健康地理学等方面的研究. E-mail: zhl.grase@163.com
基金资助:
中国科学院A类战略性先导科技专项(XDA19040502)

Spatial differences and influencing factors of stadia and fitness centers at different scales in China

ZHAO Haili^1,²(),LI Jialiang¹(),LI Kaili³,DU Yuhan¹,WANG Jiaming¹

1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, Gansu, China
2. Key Laboratory of Resource Environment and Sustainable Development of Oasis, Lanzhou 730070, Gansu, China
3. School of Urban Resources and Environment, Jiangsu Second Normal University, Nanjing 210013, Jiangsu, China

Received:2022-04-11 Revised:2022-07-01 Online:2022-11-25 Published:2023-02-01
Contact: Jialiang LI

摘要/Abstract

摘要：

运动健身场所是开展全民健身运动的重要推手，探明运动健身场所的区域差异及影响因素对推动运动健身场所的建设、推进“健康中国”战略有重要作用。从省级、城市群以及地级市3个尺度出发，运用总体分异测度指数（Global differentiation index，GDI）、Moran’s I指数、热点分析探讨运动健身场所的空间差异，采用Pearson相关系数、灰色关联度以及地理探测器等方法分析运动健身场所分布的影响因素。结果表明：（1）运动健身场所数量与每万人拥有运动健身场所数量主要集中分布于东部地区，而西部地区除成都、重庆外，其余省区均分布较少。（2）运动健身场所数量与每万人拥有运动健身场所数量的GDI随着尺度的缩小而扩大，城市群尺度中，优化提升类差异最大，而发展壮大类差异最小。（3）经济总量和人口数量是运动健身场所集中的重要驱动因素，人口数量与城镇人口占比的交互作用最强。在城市群尺度上，受教育程度和城市规模大小是运动场所的重要影响因素。而在地级市尺度上，建成区面积占辖区面积比重对运动健身场所的影响更为显著。

关键词: 运动健身场所, 尺度, 区域差异, 人口数量, GDP

Abstract:

Stadia and fitness centers are an important power in conducting the national fitness campaign. The implementation of the “Healthy Chinese” strategy and promotion of the construction of stadia and fitness centers were achieved by exploring the regional differences and examining the factors that affect stadia and fitness centers. This study discusses the spatial differences between stadia and fitness centers at provincial, urban agglomeration, and prefecture-level city scales. In this study, GDI (Global differentiation index) was used to measure the number of sports and fitness space differentiation. Moran’s I index and hotspot analysis prove the accumulation characteristics of sports and fitness places. The Pearson correlation coefficient and Grey correlation are used to determine the linear relationship and degree of closeness between the influencing factors of stadia and fitness centers, and the Geo-Detector can determine the explanatory power of influencing factors on stadia and fitness centers. The results are shown as follows. First are the number of stadia and fitness centers and the number of sports and fitness places per 10000 people. They are mainly distributed in the eastern region and less in western China, except in the core cities of the Chengdu-Chongqing urban agglomeration. Moran’s I index shows that there is a significant and positive spatial autocorrelation at the urban agglomeration scale and prefecture-level city scale, but it is not obvious at the provincial scale. Moreover, hotspot analysis shows that the smaller the scale, the more obvious the spatial agglomeration of the number of stadia and fitness centers and the number of sports and fitness places per 10000 people. The hotspots are mainly distributed in the eastern coastal region, while the cold spots are mostly distributed in the northwest, southwest, and Qinghai-Tibet regions. Second are the GDI of stadia and fitness centers and the number of stadia and fitness centers per 10000 people, which expands with the reduction in the scale. On the scale of urban agglomerations, the difference between developing and expanding urban agglomerations is the smallest, while the difference between optimizing and upgrading is the largest. Third are the economic aggregate and population, which are important driving factors for concentrating stadia and fitness centers, and the population has the strongest interaction with the proportion of the urban population. At the scale of the prefecture-level city and urban agglomeration, the size of the cities is an important factor influencing the number of stadia and fitness centers. Finally, the following policy suggestions are provided. As GDI expands with scaling down, there are obvious differences between east and west in the hotspot analysis at the prefecture-level city scale. Therefore, in the future, the government should invest in the construction of sports and fitness places in western China. The results of the geodetector show that GDP and population are important driving factors. Therefore, the government can actively build commercial sports venues by attracting investment. Additionally, while building fitness venues, the government should research the fitness needs of different groups to create a local fitness culture and fitness concepts.

Key words: stadia and fitness centers, scale, regional differences, population size, GDP

赵海莉, 李家亮, 李开丽, 杜雨涵, 王家明. 基于不同尺度的中国运动健身场所空间差异及影响因素研究[J]. 干旱区地理, 2022, 45(6): 1938-1948.

ZHAO Haili, LI Jialiang, LI Kaili, DU Yuhan, WANG Jiaming. Spatial differences and influencing factors of stadia and fitness centers at different scales in China[J]. Arid Land Geography, 2022, 45(6): 1938-1948.

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维度	影响因素名称	影响因素代码	选取原因
人口	人口数量/10⁴人	Pop	运动健身场所是服务业性质的场所。一般来说，人口数量越多，对运动健身的需求越大，催生更多的运动健身场所的集聚。
	15~59岁人口占总人口比重/%	Pop_15	运动健身场所的服务人群多在15岁以上，且劳动力对运动健身有更高的需求。
	城镇人口占比/%	Pop_Urban	城镇人口越多，对服务业的数量及质量要求越高。
教育	每10×10⁴人拥有大专以上学历人数/人	Edu	教育水平与居民的收入、职业等有较大联系，对消费行为与习惯产生较大影响。
经济	经济总量/10⁸元	GDP	经济水平是影响服务业布局的重要因素。经济总量与运动健身场所数量应具有较高的相关性。
经济	人均GDP/10⁴元	PGDP	人均GDP较客观地反映社会的发展水平与发展程度。
人民生活	城镇居民可支配收入/元	UPDI	居民购买力与服务业的种类、构成有着较高的关联。不同的可支配收入对服务业的种类、数量、质量等有着不同的要求。
城市建设	建成区面积占辖区面积比重/%	Built	商业是城市土地利用的重要组成部分。土地城市化水平越高，服务业的数量越多。
城市建设	城市规模	Urban_Size	一般而言，城市规模越大，服务业的种类越丰富，数量越多。需要说明的是，城市规模是针对地级市而言的，因此该指标未在省级尺度上选取。

	省级			城市群			市级
影响因素	Pearson 相关系数	灰色关联度	地理探测器q值	Pearson 相关系数	灰色关联度	地理探测器q值	Pearson 相关系数	灰色关联度	地理探测器q值
Pop	0.888^***	0.961	0.732^***	0.871^***	0.961	0.808^***	0.873^***	0.838	0.807^***
Pop_15	-0.035	0.841	0.405^*	0.491^***	0.812	0.320^***	0.341^***	0.681	0.164^***
Pop_Urban	0.263	0.904	0.233	0.594^***	0.835	0.354^***	0.510^***	0.620	0.271^***
Edu	-0.056	0.806	0.040	0.621^***	0.933	0.418^***	0.586^***	0.714	0.357^***
GDP	0.922^***	0.967	0.818^***	0.917^***	0.965	0.826^***	0.924^***	0.952	0.836^***
PGDP	0.266	0.894	0.187	0.536^***	0.905	0.286^***	0.543^***	0.738	0.293^***
UPDI	0.286	0.889	0.384^*	0.597^***	0.902	0.390^***	0.595^***	0.681	0.397^***
Built	0.149	0.897	0.652	0.619^***	0.918	0.374^***	0.639^***	0.896	0.409^***
Urban_Size	-	-	-	0.684^***	0.819	0.808^***	0.644^***	0.589	0.684^***

交互因素	省级	城市群	市级	交互因素	省级	城市群	市级
Pop∩Pop_15	0.944	0.900	0.899	Pop_Urban∩UPDI	0.712	0.567	0.549
Pop∩Pop_Urban	0.984	0.906	0.908	Pop_Urban∩Built	0.700	0.596	0.606
Pop∩Edu	0.861	0.892	0.880	Pop_Urban∩Urban_Size	-	0.906	0.807
Pop∩GDP	0.974	0.881	0.880	Edu∩GDP	0.974	0.860	0.878
Pop∩PGDP	0.857	0.894	0.881	Edu∩PGDP	0.927	0.541	0.503
Pop∩UPDI	0.986	0.904	0.901	Edu∩UPDI	0.793	0.591	0.579
Pop∩Built	0.862	0.899	0.892	Edu∩Built	0.784	0.608	0.606
Pop∩Urban_Size	-	0.809	0.845	Edu∩Urban_Size	-	0.892	0.799
Pop_15∩Pop_Urban	0.677	0.455	0.365	GDP∩PGDP	0.973	0.862	0.870
Pop_15∩Edu	0.854	0.538	0.448	GDP∩UPDI	0.845	0.845	0.853
Pop_15∩GDP	0.974	0.876	0.884	GDP∩Built	0.846	0.874	0.879
Pop_15∩PGDP	0.927	0.491	0.409	GDP∩Urban_Size	-	0.881	0.811
Pop_15∩UPDI	0.793	0.545	0.529	PGDP∩UPDI	0.719	0.451	0.437
Pop_15∩Built	0.799	0.874	0.641	PGDP∩Built	0.777	0.575	0.439
Pop_15∩Urban_Size	-	0.899	0.819	PGDP∩Urban_Size	-	0.894	0.560
Pop_Urban∩Edu	0.328	0.487	0.406	UPDI∩Built	0.707	0.625	0.649
Pop_Urban∩GDP	0.844	0.866	0.873	UPDI∩Urban_Size	-	0.903	0.843
Pop_Urban∩PGDP	0.370	0.471	0.439	Built∩Urban_Size	-	0.899	0.876

基于不同尺度的中国运动健身场所空间差异及影响因素研究

Spatial differences and influencing factors of stadia and fitness centers at different scales in China

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