长江中游城市群创新平台空间分布及其影响因素分析

doi:10.18306/dlkxjz.2020.04.001

摘要/Abstract

摘要：

创新是引领发展的第一动力。创新平台作为区域创新体系的重要组成部分和集聚创新资源的核心支撑载体,是推动创新驱动发展的主要抓手。论文以长江中游城市群为研究区域,运用最邻近指数、核密度估计、Ripley's K函数、地理探测器等方法,探究2017年长江中游城市群省级以上创新平台的空间分布特征及其影响因素。结果表明：① 长江中游城市群创新平台整体呈现出集聚分布特征,以武汉、长沙和南昌为核心呈“品字形”的分布格局;② 基于不同等级和不同功能类型划分的各类创新平台均呈现出显著集聚的特征,但集聚强度和集聚状态存在一定差异;③ 创新平台总体和不同类型创新平台的空间集聚存在尺度效应,随地理距离的变化空间集聚趋势先增强后减弱;④ 创新平台的空间分异是多因素综合作用的结果,金融实力、信息化程度、利用外资水平、人力资本条件的影响最为显著,其次为交通可达性、城镇化水平、经济基础和创新氛围。

关键词: 创新平台, 空间分布, 长江中游城市群

Abstract:

Innovation is the primary driving force behind development. As an important part of the regional innovation system and the core supporting carrier for the accumulation of innovative resources, innovation platforms are the main way to promote innovation-driven development. This study took the urban agglomerations of the middle reaches of the Yangtze River Basin as the research area and used the nearest neighbor index, kernel density estimation, Ripley's K function, and geo-detector to explore the spatial distribution characteristics and influencing factors of the innovation platforms above provincial level in the urban agglomerations in 2017. The results show that: 1) The overall spatial distribution of the innovation platforms in the urban agglomerations is clustered, showing a triangular distribution pattern with Wuhan, Changsha, and Nanchang as the cores. 2) Innovation platforms of various scales and functional types exhibit significant agglomeration characteristics, but there exist certain differences in agglomeration intensity and state among them. 3) The overall innovation platforms and various types of innovation platforms show scale effect, which first strengthens and then weakens with the change of geographical distance. 4) The spatial differentiation of the innovation platforms is the result of multiple factors. Strength of financial resources, degree of informationalization, level of foreign investment, and conditions of human capital are most significant, followed by traffic accessibility, urbanization level, economic base, and innovation atmosphere.

Key words: innovation platforms, spatial distribution, urban agglomerations of the middle reaches of the Yangtze River Basin

唐承丽, 郭夏爽, 周国华, 吴佳敏, 陈伟杨. 长江中游城市群创新平台空间分布及其影响因素分析[J]. 地理科学进展, 2020, 39(4): 531-541.

TANG Chengli, GUO Xiashuang, ZHOU Guohua, WU Jiamin, CHEN Weiyang. Spatial distribution and influencing factors of innovation platforms in urban agglomerations of the middle reaches of the Yangtze River Basin[J]. PROGRESS IN GEOGRAPHY, 2020, 39(4): 531-541.

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名称	主要功能	功能类型	国家级	省级	总计
重点实验室	组织基础研究	知识型	49	485	534
工程技术研究中心	工程应用方面的技术研究	知识型	39	894	933
国际科技合作基地	发挥国际科技合作	知识型	55	—	55
企业技术中心	企业开展自主创新、成果转化	产业型	103	898	1001
高新技术产业开发区(园区)	发展高新技术产业	产业型	20	27	47
众创空间	提供创业公共服务	服务型	139	249	388
科技企业孵化器	为高新技术中小企业提供服务	服务型	78	161	239
星创天地	提供农业科技创新创业服务	服务型	87	68	155
总计	—	—	570	2782	3352

类型		平均观测距离/km	预期平均距离/km	NNI	Z值	P值	分布模式
创新平台总体		1.18	5.88	0.2009	-88.5038	<0.0001	显著集聚
按等级	国家级创新平台	4.27	13.98	0.3052	-31.7333	<0.0001	显著集聚
按等级	省级创新平台	1.38	6.41	0.2155	-79.1598	<0.0001	显著集聚
按功能类型	知识型创新平台	2.00	8.20	0.2441	-56.4158	<0.0001	显著集聚
	产业型创新平台	3.07	10.11	0.3040	-43.1013	<0.0001	显著集聚
	服务型创新平台	3.85	11.87	0.3247	-36.1253	<0.0001	显著集聚

维度	指标名称	指标描述	q值
城市经济发展水平	X₁经济基础	人均GDP	0.5638^*
	X₂产业结构	第二、三产业产值占GDP比重	0.1793
	X₃金融实力	金融机构贷款余额占GDP比重	0.8944^*
城市对外开放程度	X₄交通可达性	交通情况赋值	0.6665^*
城市对外开放程度	X₅利用外资水平	人均实际使用外资金额	0.8908^*
城市科技创新环境	X₆人力资本条件	每万人拥有R&D人员数	0.7655^*
	X₇创新投入	R&D内部经费支出占GDP比重	0.2606
	X₈科技进步	高新技术产业增加值占GDP的比重	0.1069
城市社会公共环境	X₉创新氛围	每万人拥有专利申请数	0.4720^*
	X₁₀城镇化水平	城镇化率	0.6247^*
	X₁₁信息化程度	互联网普及率	0.8921^*
	X₁₂绿色资源禀赋	人均绿地面积	0.2499

因素	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
X₁	0.5638
X₂	0.6510	0.1793
X₃	0.9041	0.9030	0.8944
X₄	0.9083	0.8379	0.9731	0.6665
X₅	0.9189	0.9006	0.9099	0.9734	0.8908
X₆	0.9362	0.8414	0.9347	0.9875	0.9320	0.7655
X₇	0.8922	0.3620	0.9762	0.8043	0.9775	0.8761	0.2606
X₈	0.8830	0.6659	0.9698	0.8558	0.9720	0.8256	0.7146	0.1069
X₉	0.8540	0.7984	0.9462	0.9223	0.9326	0.9343	0.8850	0.5082	0.4720
X₁₀	0.6533	0.6430	0.9056	0.9845	0.9085	0.9478	0.8756	0.8705	0.8442	0.6247
X₁₁	0.9050	0.9009	0.8995	0.9760	0.8962	0.9349	0.9701	0.9658	0.9345	0.9024	0.8921
X₁₂	0.5903	0.4887	0.9159	0.9106	0.9030	0.9713	0.7149	0.7300	0.7163	0.6557	0.9093	0.2499

交互作用类型	因素
非线性减弱	—
单因子非线性减弱	—
双因子增强	X₁∩X₂、X₁∩X₃、X₁∩X₄、X₁∩X₅、X₁∩X₆、X₁∩X₉、X₁∩X₁₀、X₁∩X₁₁、X₁∩X₁₂、X₂∩X₃、X₂∩X₄、X₂∩X₅、X₂∩X₆、X₂∩X₇、X₂∩X₁₀、X₂∩X₁₁、X₃∩X₄、X₃∩X₅、X₃∩X₆、X₃∩X₇、X₃∩X₈、X₃∩X₉、X₃∩X₁₀、X₃∩X₁₁、X₃∩X₁₂、X₄∩X₅、X₄∩X₆、X₄∩X₇、X₄∩X₉、X₄∩X₁₀、X₄∩X₁₁、X₄∩X₁₂、X₅∩X₆、X₅∩X₇、X₅∩X₈、X₅∩X₉、X₅∩X₁₀、X₅∩X₁₁、X₅∩X₁₂、X₆∩X₇、X₆∩X₈、X₆∩X₉、X₆∩X₁₀、X₆∩X₁₁、X₆∩X₁₂、X₇∩X₁₀、X₇∩X₁₁、X₈∩X₉、X₈∩X₁₁、X₉∩X₁₀、X₉∩X₁₁、X₉∩X₁₂、X₁₀∩X₁₁、X₁₀∩X₁₂、X₁₁∩X₁₂
独立	—
非线性增强	X₁∩X₇、X₁∩X₈、X₂∩X₈、X₂∩X₉、X₂∩X₁₂、X₄∩X₈、X₇∩X₈、X₇∩X₉、X₇∩X₁₂、X₈∩X₁₀、X₈∩X₁₂