江苏省研发企业空间格局演化及其影响因素分析

doi:10.3969/j.issn.1004-9479.2022.05.2020624

摘要/Abstract

摘要：

以江苏省县区为研究单元，综合运用标准差椭圆、Ripley’s K函数和探索性空间数据分析方法，探究2005、2011和2017年江苏省研发企业的空间分异特征，并采用负二项回归模型考察江苏省研发企业空间分异的影响因素，研究结论如下：研发企业空间分布呈现出稳定的西北-东南分布格局，且具有显著的空间集聚性，聚集规模和强度先增强后减弱；研发企业冷热点呈块状分布，存在明显的“俱乐部趋同”现象；研发企业和开发区、高等学校空间匹配程度较高；经济发展水平、劳动力成本、开发区建设、邻近高等院校以及集聚经济等因素对江苏省研发企业空间布局影响显著。

关键词: 研发企业, 江苏省, 空间集聚, 空间关联, 影响因素

Abstract:

Taking Jiangsu county district as the research unit, this paper explored the spatial differentiation characteristics of R&D enterprises in Jiangsu province in 2005, 2011 and 2017 with the methods of standard deviation ellipse, Ripley's K function and exploratory spatial data analysis, and then investigated the influencing factors of spatial differentiation of R&D enterprises in Jiangsu province by the negative binomial regression model, the main conclusions are as follows. The R&D enterprises show a stable northwest-southeast spatial distribution pattern, and it has significant spatial clustering, and the scale and intensity of the clustering increase first and then decrease. What's more, the cold and hot spots of R&D enterprises are distributed in blocks, and there is an obvious phenomenon of "club convergence". In addition, R&D enterprises, development zones and institutions of higher learning have a high degree of spatial matching. Lastly, factors such as economic development level, labor cost, development zone construction, proximity to institutions of higher learning and agglomeration economy have a significant influence on the spatial distribution of R&D enterprises in Jiangsu Province.

Key words: R&D enterprises, Jiangsu Province, spatial agglomeration, spatial correlation, influencing factors

张筱娟, 汤琪凤, 廖紫薇. 江苏省研发企业空间格局演化及其影响因素分析[J]. 世界地理研究, 2022, 31(5): 1034-1045.

Xiaojuan ZHANG, Qifeng TANG, Ziwei LIAO. Spatial pattern evolution of R&D enterprises and its influencing factors in Jiangsu Province[J]. World Regional Studies, 2022, 31(5): 1034-1045.

图/表 8

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年份	中心点经度	中心点纬度	长轴标准差/km	短轴标准差/km	转角θ/°
2005	119.8 483°E	31.9 788°N	1.3 496	0.7 751	124.7 590
2011	120.0 222°E	31.9 107°N	1.3 172	0.6 698	124.5 891
2017	120.0 267°E	31.9 848°N	1.4 561	0.7 464	130.9 284

年份	中心点经度	中心点纬度	长轴标准差/km	短轴标准差/km	转角θ/°
2005	119.8 483°E	31.9 788°N	1.3 496	0.7 751	124.7 590
2011	120.0 222°E	31.9 107°N	1.3 172	0.6 698	124.5 891
2017	120.0 267°E	31.9 848°N	1.4 561	0.7 464	130.9 284

年份	邻接矩阵			地理距离倒数矩阵
年份	Moran′s I	E(I)	Z值	Moran′s I	E(I)	Z值
2005	0.1 343^*	-0.0 189	1.4 904	0.0 472^***	-0.0 189	4.0 748
2011	0.3 107^***	-0.0 189	2.7 928	0.1 063^***	-0.0 189	6.7 427
2017	0.2 954^***	-0.0 189	2.8 630	0.0 816^***	-0.0 189	5.8 089

年份	邻接矩阵			地理距离倒数矩阵
年份	Moran′s I	E(I)	Z值	Moran′s I	E(I)	Z值
2005	0.1 343^*	-0.0 189	1.4 904	0.0 472^***	-0.0 189	4.0 748
2011	0.3 107^***	-0.0 189	2.7 928	0.1 063^***	-0.0 189	6.7 427
2017	0.2 954^***	-0.0 189	2.8 630	0.0 816^***	-0.0 189	5.8 089

变量名称	变量定义	单位	符号
经济发展水平	人均GDP	元	pgdp
产业结构	二、三产业占GDP比重	%	indus
劳动力成本	在岗职工平均工资	元	wa
开发区建设	省级及以上开发区数量	个	dz
高速公路路网密度	高速公路长度与总面积的比值	-	hw
邻近高等院校	高等学校数量	个	sc
集聚经济	2005年各县区研发企业数	个	agglo