山地城市医疗点空间结构识别与影响机理探究

doi:10.3969/j.issn.1004-9479.2023.02.2021149

摘要/Abstract

摘要：

以山地城市贵阳市中心城区为例，运用平均最近邻、核密度分析、标准差椭圆法、空间自相关分析和网格维数法对其医疗点的空间结构进行识别及影响机理探究。结果表明：①贵阳市中心城区医疗点空间分布呈显著集聚模式，医疗点的核密度在空间分布上呈“四核心，次中心多点环绕”的分布特征，呈“y”形布局态势；医疗点整体呈“西北-东南”方向布局。②乡镇社区（街道）单位人口的医疗点数量呈显著空间正相关，冷热点围绕环线分布。③贵阳市中心城区医疗点具有不均衡性、概率不等的空间分布特征，分形结构复杂。④区域医疗点空间分布受到人口分布特征格局、道路等级与可达性、房价与GDP、海拔与坡度等方面的综合影响。

关键词: 医疗服务业, 空间结构, GIS, 网格维数法, 贵阳市中心城区

Abstract:

Taking the central urban area of Guiyang,a mountainous city,as an example,the average nearest neighbor,kernel density analysis,standard deviation ellipse method,spatial autocorrelation analysis and grid dimension method are used to identify the spatial structure of its medical points and explore the impact mechanism. The results show that:①The spatial distribution of medical treatment points in the central city of Guiyang is in a significant cluster mode.The nuclear density of medical treatment points is characterized by "four cores,sub-center and multi-point surround",showing the "y" layout. The overall medical treatment points are in the trend of "northwest-southeast" direction. ②The number of medical treatment points of the unit population in the township community (street) is positively correlated with each other. The distribution structure of cold hot spots around the ring line is prominent. ③The medical treatment points in the central city of Guiyang have the characteristics of uneven spatial distribution and complex fractal structure. ④The spatial distribution of regional medical points is affected by the pattern of population distribution,road grade and accessibility, house price and GDP, altitude and slope.

Key words: medical services industry, spatial structure, GIS, grid dimensional method, Guiyang city center

黄勇, 赵航, 陈国磊. 山地城市医疗点空间结构识别与影响机理探究[J]. 世界地理研究, 2023, 32(2): 125-135.

Yong HUANG, Hang ZHAO, Guolei CHEN. The identification and influence mechanism of spatial structure for medical points in mountainous cities:[J]. World Regional Studies, 2023, 32(2): 125-135.

图/表 12

表1 国内外研究对比

Tab.1 Comparison of domestic and foreign research

要素	国外		国内
研究内容	空间结构和优化与区位选择及影响因素	空间可达性与资源空间配置及影响因素	空间结构和优化与区位选择及影响因素	空间可达性与资源空间配置及影响因素
研究区域	韩国^[1]、巴西^[21]、葡萄牙^[31]等	印度^[9]、日本^[11]、加拿大^[17]、美国^[32]等	天津^[6]、广州^[7]、北京^[30]、上海^[33]、香港^[34]等	北京^[8]、上海^[33]、全国或城市群^[12,18,19]等
研究方法	核密度分析、地理探测器、标准差椭圆、LSCM（长短期记忆网络）、MCLP（最大覆盖问题）、OLS（最小二乘法）等	2SFCA（两步移动搜索法）、3SFCA（三步移动搜索法）、泰尔指数、基尼系数、集中指数等	核密度分析、Ripley’s K 函数、最邻近点指数法、P-重心模型、改进的LSCM、SLM、SEM等	改进的2SFCA、胡弗模型、洛伦兹曲线、潜能模型、泰森多边形等
影响指标	城市建设、人口统计特征、经济水平、自然因数等	种族、社会阶层、收入、自然、交通、医疗水平等	人口、经济、城市建设、自然环境、交通布局等	人口、医疗服务能力、经济、交通布局等

图1 研究区医疗点与交通网络布局

Fig.1 Layout of medical points and transportation network in the study area

表2 医疗点最邻近点指数

Tab.2 Nearest neighbor index of medical point

总数	$D O ¯$	$D E ¯$	R	Z	P
862	213.08m	583.08m	0.37	-35.62	0.00

表2 医疗点最邻近点指数

Tab.2 Nearest neighbor index of medical point

总数	$D O ¯$	$D E ¯$	R	Z	P
862	213.08m	583.08m	0.37	-35.62	0.00

图2 医疗点核密度与标准差椭圆空间格局

Fig.2 The spatial pattern of kernel density and standard deviation ellipse of medical points

表3 医疗点全局Moran's I 指数

Tab.3 Medical point global Moran's I

Moran’s I	预期指数	方差	Z值	P值
0.685	-0.013	0.001	19.994	0.000

图3 冷热点空间格局

Fig.3 Spatial pattern of cold and hot spots

表4 医疗点网格维数测算数据

Tab.4 Calculation data of grid dimension of medical points

K	2	3	4	5	6	7	8	9	10
N(r)	4	7	10	15	19	26	27	31	37
I(r)	1.341 2	0.974 6	1.654 4	1.592 9	2.065 5	2.142 1	2.397 1	2.465 3	2.701 2
ln(K)	0.693 1	1.098 6	1.386 3	1.609 4	1.791 8	1.945 9	2.079 4	2.197 2	2.302 6
ln(N(r))	1.386 3	1.945 9	2.302 6	2.708 1	2.944 4	3.258 1	3.295 8	3.434 0	3.610 9
ln(I(r))	0.293 6	-0.025 7	0.503 5	0.465 5	0.725 4	0.761 8	0.874 2	0.902 3	0.993 7

图4 医疗点网格维数双对数散点图

Fig.4 Grid dimension double logarithm scatter diagram of medical points

图5 公交可达性得分空间格局

Fig.5 Spatial pattern of bus accessibility score

表5 公交可达性得分等级下的医疗点数量

Tab.5 The number of medical points under the score of bus accessibility

可达性得分等级	医疗点数量/个	占比/%
低可达（0.017~0.681）	47	5.45
较低可达（0.682~4.970）	118	13.69
中可达(4.971~9.398)	296	34.34
较高可达(9.399~15.047)	169	18.31
高可达(15.048~21.584)	232	27.91

图6 医疗点与人口密度空间格局

Fig.6 Spatial pattern of medical points and population density

表6 不同高程与坡度等级下的医疗点数量

Tab.6 Number of medical points of different elevation and slope grades

地形	等级	医疗点数量/个	占比/%
海拔/m	<1 100	453	52.55
	1 100~1 200	233	27.03
	1 201~1 300	164	19.03
	>1 300	12	1.39
坡度/º	0~5	514	59.63
	6~10	158	18.33
	11~15	83	9.63
	16~20	54	6.26
	>20	53	6.15

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