地理国情分类区划模型构建及实证研究——以河南省为例

doi:10.18306/dlkxjz.2016.11.006

Abstract

Abstract:

Classification based regionalization is an effective way to analyze geographical national conditions and a basis for studying the regional differences in the classification characteristics, combinations, change, and spatial differentiation of geographical national conditions. The index system of the classification-based regionalization of geographical national conditions is determined through the analysis of the basic statistical data of the geographical national conditions survey. Using multidimensional scaling and cluster analysis, the classification-based regionalization model of geographical national conditions was established. The classification-based regionalization experiment was carried out using the geographical national conditions survey data of Henan Province. The number of regions of geographical national conditions was initially intuitively determined through multidimensional scaling on geographic national conditions data of Henan Province. On this basis, the classification-based regionalization was achieved by K-means clustering. The type of regions is defined and explained according to the cluster centers and the characteristics of index data in each cluster. Finally, the clustering result is spatialized. The experimental results show that the geographical national conditions in Henan province can be divided into four regions: extreme value region, low value region, median value region and high value region. The spatial agglomeration effect of regions of geographical national conditions in Henan Province is significant, and It is mutual tolerance and penetration between each classification-based regionalization and constructed of four southwest to northeast strip. In the overall planning of regional development and realizing regional coordinated and sustainable development, we can make feasible regional balanced development policies according to the geographical national conditions of each region.

Key words: geographical national condition, classification-based regionalization, multidimensional scaling, cluster analysis, Henan Province

Mingxia XIE, Jiayao WANG, Ke CHEN. The model construction and empirical research on classification-based regionalization of geographical national conditions:Take Henan Province as an example[J].PROGRESS IN GEOGRAPHY, 2016, 35(11): 1360-1368.

Figures/Tables 14

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References 24

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方法	分析对象	分析目的
因子分析	相似性	维度缩减,分析维度和因素的含义
聚类分析	相似性(或相异性)	数据分组
多维尺度分析	相异性	维度缩减,分析降维后数据点的位置

行政区	维度1 坐标值	维度2 坐标值	行政区	维度1 坐标值	维度2 坐标值
安阳	-0.4225	0.7576	濮阳	-1.1777	-0.1420
鹤壁	-1.0321	0.2182	三门峡	-0.6020	1.1954
济源	-1.4897	0.2407	商丘	-0.2797	-1.1644
焦作	-0.1989	0.0637	新乡	0.6378	-1.0900
开封	-0.3322	-0.2362	信阳	1.0043	-1.2966
洛阳	0.9954	1.7661	许昌	-0.5465	0.0765
漯河	-1.1636	-0.2370	郑州	1.6840	1.6548
南阳	3.0637	-0.0791	周口	-0.2780	-1.0988
平顶山	0.0623	0.1584	驻马店	0.0753	-0.7874

聚类簇	簇中心坐标		距离簇中心最近的地市	各聚类簇包含的地市
聚类簇	维度1	维度2	距离簇中心最近的地市	各聚类簇包含的地市
1	0.2319	-1.0874	驻马店	驻马店、商丘、新乡、周口、信阳
2	1.3397	1.7105	洛阳(郑州)	洛阳、郑州
3	-0.6903	0.2095	许昌	三门峡、安阳、济源、鹤壁、许昌、平顶山、濮阳、焦作、开封、漯河
4	3.0637	-0.0791	南阳	南阳

聚类簇	区划类型	区划特征
1	指标数据极值区	各项指标值有高有低,发展不均衡
2	指标数据中值区	大部分指标值处于中间值,仅有较少部分指标值处于较高水平
3	指标数据低值区	大部分指标值处于较低值
4	指标数据高值区	各项分类区划指标值均较高

The model construction and empirical research on classification-based regionalization of geographical national conditions:Take Henan Province as an example

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14	0	0	0	0	0	0	0	1	1	0	0	0	1	1	1	1	0	0
15	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	1	0	0
16	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0
17	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0
18	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1

ID	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
1	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
2	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0
3	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0
4	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
5	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0
6	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
7	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
8	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
9	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0
10	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
11	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
12	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
13	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0
14	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
15	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0
16	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0
17	1	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	0
18	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1

ID	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
1	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0
2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
3	0	1	1	0	0	1	1	0	0	0	0	0	0	0	1	0	0	1
4	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1
5	1	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0	0	1
6	0	0	1	0	1	1	1	1	1	0	0	0	0	0	0	0	0	1
7	0	0	1	0	0	1	1	1	0	0	0	0	0	0	1	0	0	0
8	0	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0	0
9	0	0	0	0	1	1	0	1	1	1	0	0	1	1	0	0	0	0
10	1	0	0	0	1	0	0	0	1	1	0	0	1	0	0	0	0	0
11	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0
12	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0
13	0	0	0	0	0	0	0	0	1	1	1	1	1	1	0	0	1	0
14	0	0	0	0	0	0	0	1	1	0	0	0	1	1	1	1	0	0
15	0	0	1	0	0	0	1	1	0	0	0	0	0	1	1	1	0	0
16	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0
17	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0
18	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1