1 引言
城市职能是城市在一定地域内的经济、社会发展中所发挥的作用和承担的分工，是城市对城市本身以外的区域在经济、政治、文化等方面所起的作用[1]。随着时代变迁，城市职能已从生产制造职能、管理协调职能转向创新职能[2]。城市作为知识创新资源密集和知识转移交流的中心，以知识为重要战略资源形成的创新职能在国家或区域经济发展中承担重要任务[3⇓-5]。知识创新是经济增长的原动力，也是推动区域发展的基本动力[6-7]，知识创新职能作为城市创新职能的重要组成部分和发展基础，不仅承担着知识创新的任务还影响着城市其他创新职能的发展。因此，探讨中国城市知识创新职能的空间分异、分析影响城市知识创新职能发展的区域差异因素，对精准打造知识创新城市、制定中国城市知识创新发展战略有重要的参考价值。
近些年来，知识创新成为经济学、管理学、地理学等学科研究的热点领域，其中经济学、管理学集中在对知识创新内涵的理解[8]，从知识创造、知识传播和知识应用等方面评价区域知识创新能力[9⇓-11]，地理学侧重从创新环境等方面分析区域知识创新水平的差异性及影响因素[12⇓-14]、基于专利合作探究城市群知识创新的空间结构[15]、借用专利分析城市创新能力的空间格局和影响因素[16]。学者们虽然对城市知识创新空间格局研究取得了一些进展，但多以专利进行测度，根据经济学和管理学对知识创新的理解，城市知识创新是一个从知识创造、知识传播到知识应用的复杂过程，专利只是知识创造的产物且并非所有专利都能转化为经济价值，不足以全面刻画城市知识创新职能及空间格局特征。
随着国家创新驱动战略的实施，各城市集聚多样化人才等创新资源，较多的创新合作伙伴、面对面的交流机会以及高度的工作流动性促进知识传递和创新产生，创新也成为城市最为主要的职能并引起城市空间体系的重构[2,17]。学者也以各行业的论文和专利分别测度北京市不同行业的创新职能指数和专门化指数[18]，基于基本职能和非基本职能，采用区位商等方法，对北京和上海的科技创新职能进行比较研究[19]；利用城市创新流强度分析广东省城市创新职能[20]。知识创新职能是城市创新职能的重要组成部分和形成基础[21-22]，学者从知识的丰富度、知识获取和知识产出构建出7项指标，基于层次分析法赋予权重得出城市知识创新职能得分[22]，由于考虑的维度仍然较少且测度方法未准确按照城市职能的测度方法进行计算，也难以精确反映城市的知识创新职能。
城市职能形成和发展的因素也随着城市人类活动发生了重大的变化。在城市形成初期，地形、气候、水文、土壤、资源、生态环境等自然因素和人口因素是影响城市职能形成的基础[23-24]。随着社会不断进步，城市也在逐渐适应社会生产力变革而承担不同的职能，人文因素对职能的影响力明显增强，技术、交通、信息化、全球化、政府等人文环境成为城市职能演变的重要因素[1,25]，尤其是信息技术通过影响城市的经济、社会、文化、管理体制、基础设施等方面直接或间接地影响城市职能[26]。虽然有学者提出城市职能演变主要受城市自身内在性因素和外来驱动性因素多个要素综合影响[27-28]，但缺少对现代城市职能的相关实证分析，少数学者对城市管理职能、研发职能、知识密集型职能专业化水平的因素进行定量分析[23,29]，但指标选取不全面，且目前还未涉及城市知识创新职能的因素分析。在创新时代背景下，需要更进一步研究影响城市知识创新职能的因素，有利于培育城市创新职能的发展。
综上所述，知识创新职能已成为城市创新职能形成和发展的重要基础，但还没有系统的分析和测度知识创新职能。本文基于中国182个地级及以上城市，结合管理学和经济学的知识创新和地理学的城市职能研究成果，构建了城市知识创新职能的研究框架，建立一套城市知识创新职能的测度方法，采用空间自相关分析方法探讨中国城市知识创新职能的空间格局，并使用地理探测器分析影响城市知识创新职能空间差异化的因素，为中国知识创新型城市建设及优化国家创新发展格局提供参考，也丰富城市职能和知识创新的理论研究。
2 城市知识创新职能及测度框架
美国学者Amidon首次提出知识创新的概念，认为知识创新是以国家经济正常运转和企业健康发展为目标，创造、演化、交换和应用新思想并使其转变成市场化的产品和服务[30]。Drucker认为知识创新是赋予知识资源以新的创造财富能力的行为[31]，国内学者认为狭义的知识创新是指基础研究的创新，广义的知识创新指新知识的创造、传播和应用于实践并开发出新产品实现其经济价值的全过程[32-33]，这些过程不是线性关系，而是链环过程和反馈模式，每个阶段都离不开其他阶段的参与[34]。城市是人口的集中地，也是知识的主要生产、传播与应用之地，其知识创新主要通过3种形式实现：① 通过研究和发展（R&D）活动进行知识创新，人才、企业和科研机构是城市知识创新至关重要的主体[35-36]；② 创新主体在知识的生产、传播、交换和应用过程中发生的知识创新[37-38]；③ 企业将新知识引入经济用途促进知识转化[39]。根据城市职能和知识创新的涵义，本文认为城市知识创新职能是以内在知识存量和外在实践条件为基础，以满足人类新时代生存和发展需求而在知识创造、知识传播及知识应用过程中所承担的任务和所起的作用。内在的知识存量包括城市自身的知识规模和通过外部实践条件不断创造的新知识[40]，外在实践条件涉及各类创新主体的创造能力、吸收传播能力、应用能力以及城市为其提供的创新环境[41]。
对城市职能的测度，周一星系统提出“城市职能三要素”包括专业化部门、职能规模和职能强度[42]，张复明等认为专业化部门对单个城市来说是某一行业与其他行业比较中的地位标识，若某部门的专业化水平越高，则职能强度就越大，因此专业化部门无法与职能强度并列[43]。城市经济活动的影响具有空间性，为城市以外地区服务的职能为基本职能，为城市本身地区服务的职能为非基本职能，因此城市职能也具有职能尺度特征[43-44]。本文从不同侧面综合衡量城市知识创新职能，职能规模是知识创新的基础，表现在城市知识量的积累，职能强度是知识创新专业化水平的测度，职能尺度是城市知识创新在空间上的影响力，职能活力是城市在知识创新过程中表现的积极性和行动力。
职能活力：学者将城市活力定义为城市对其自身功能、经济社会等不同资源与发展要素的支持程度，在一定程度上体现城市发展的能力和潜力[52]。城市知识创新职能活力是该类职能对城市知识创新活动的支持程度及推动其持续健康发展的潜力，在知识创新的整个过程中表现为知识创造的活力、知识更新的速度以及知识应用的水平，三者共同决定知识创新的活力。知识创造的活力是城市对知识发现、知识学习和知识创造的支持程度，提供必要的人力、财力和物力支持会激发其创造的活力[53]。有活力的重要标准之一是城市的知识不断更新变化，知识更新速度是城市内部知识创新主体对原有知识的吸收、转移和加工后创造出新知识的速度[54]。知识创新的最终实现形式是知识转化成产品并投放市场获得相应利润，知识应用能力越高越能促进知识转化，知识密集型产业产值是知识应用的重要体现[55]。据上所述，建立城市知识创新职能与测度的理论分析框架（图1）。
图1
图1
城市知识创新职能及测度框架
Fig. 1
Urban knowledge innovation function and measurement framework
3 数据来源与研究方法
3.1 数据来源
本文选取2015—2019年的5年数据的平均值综合分析城市知识创新职能及影响因素，其中知识密集型服务业细分行业的产值数据，由于大部分城市缺少2018年和2019年数据，因此只选取2015—2017年3年数据的平均值来衡量各城市的知识密集型服务业产值状况。论文发表数据来源于中国知网（CNKI），专利申请授权数据来源于国家专利信息服务平台，知识密集型服务业企业数据来自企查查，PM2.5数据来源华盛顿大学圣路易斯分校网站（
表1 知识密集型产业的行业范围及权重系数
Tab. 1
| 知识密集型制造业 | 权重系数 | 知识密集型服务业 | 权重系数 |
|---|---|---|---|
| C26化学原料及化学品制造业 | 0.031 | I信息传输、软件和信息技术服务业 | 0.122 |
| C27医药制造业 | 0.015 | J金融业 | 0.192 |
| C34通用设备制造业 | 0.031 | L租赁和商务服务业 | 0.045 |
| C35专用设备制造业 | 0.032 | M科学研究和技术服务业 | 0.312 |
| C36汽车制造业 | 0.049 | ||
| C37铁路、船舶、航空航天和其他运输设备制造业 | 0.049 | ||
| C38电气机械和器材制造业 | 0.037 | ||
| C39计算机、通信和其他电子设备制造业 | 0.073 | ||
| C40仪器仪表制造业 | 0.009 |
3.2 研究方法
3.2.1 城市知识创新职能的测度方法
（1）职能规模的评价方法。本文采用某城市的“论文发表数”和“专利申请数”与样本城市论文和专利平均数的比值来衡量该城市的显性知识存量（X1），通过知识密集型服务业企业数与城市建成区（不包括居住区）面积的比值表示知识密集型服务业的密集度，用某城市的知识密集型服务业就业人数乘以知识密集型服务业密集度与样本城市该指标平均值的比值表示城市隐性知识存量（X2）。知识存量（X）计算公式为：
式中：X1为i地区显性知识存量；n为样本城市的个数；mi为i地区论文发表数和专利申请数；X2为i地区隐性知识存量；mj为i地区知识密集型服务业就业人数与知识密集型服务业企业密集度的乘积，Pj为知识密集型服务业就业人数，Ej为i地区知识密集型服务业企业数；Sj为i城市建成区面积（不包括居住区面积）；X为i城市知识总存量，即知识创新职能规模。
（2）职能强度的评价方法：
式中：CCij为i城市j产业的集中系数；j为知识密集型产业；Qij为i城市j产业的产值；Pi为i地区的人口；Qj为样本城市j产业总产值；P为样本城市总人口数。如果系数大于1，说明该产业比较集中。
式中：Rij为i城市j产业的专业化指数；j为知识密集型产业；mij为i城市j产业的产值；m为i城市所有产业总产值；Mij为样本城市j产业总产值；M为样本城市所有产业总产值。专业化指数大于1，则认为该产业是地区的专业化部门。
式中：Xi为i城市的专利授权量和论文发表量；mean(X)为样本城市的专利授权量和论文发表量的平均值；σ为标准差；Yi代表i城知识创新活动的中心性指数，若值大于1，说明具备一定的全国地位，值越大，说明城市知识创新的对外服务能力越强。
城市知识流强度是指一城市在区域城市体系中向其他城市输出的知识流量，它表征的是该城市对外知识服务能力的强弱[61]。本文以知识密集型产业的就业人数和产值来衡量知识流强度大小，公式为：
式中：F为城市知识流强度；Nij为城市知识功能效益，即一城市单位外向服务功能量所产生的实际影响；Eij为城市外向知识服务功能量。
借助区位商的原理，可以计算出城市的知识密集型产业部门从业人员的基本部分，即城市的外向知识服务功能量。设i城市j部门从业人员的区位商为Rij：
式中：j为知识密集型产业；mij为i城市j产业的就业人数；m为i城市所有产业就业总人数；Mij为样本城市j产业总就业人数；M为样本城市所有产业总就业人数。区位商Rij > 1，则认为i城市j部门存在外向服务功能，因为i城市的总从业人员中分配给j部门的比例超过全国的分配比例，该部门可为城市以外区域提供服务[62]。
Eij表示i城市j部门的外向服务功能量，它可定义为j部门从业人员中的基本活动部分，即i城市j部门中具有对外服务能力的人数。当Rij > 1时，则有：
式中：Gij为i城市j部门从业人员数量；Gi为i城市从业人员数量；Gj为样本城市j部门从业人员数量；G为样本城市总从业人员数量。
Nij表示i城市j部门的外向服务功能效率，这里用i城市j部门从业人员的人均GDP来表征，则有：
式中：GDPij表示i城市j部门的国内生产总值。
Fi为i城市全部具有对外知识创新服务能力产业部门的知识流强度，则有：
式中：O为知识创造能力指数；n为样本城市数量；fij为i城研究与试验发展（R&D）人员数、在校大学生和教师数；pij为i城科研财政支出额和研究与试验发展（R&D）经费内部支出额；hij为i城互联网宽带接入端口数。
新知识往往以论文和专利形式产生，因此选取论文和专利每年增加量来衡量知识更新速率。公式为：
式中：V为知识更新速率值；xi为该城市最近第i年的论文发表量和专利产出量；Xi为样本城市最近第i年论文总发表量和专利总产出量；m为样本城市数量；n为年数。V = 1，表示该城市知识更新速度等于样本城市平均更新速度；V > 1，表示该城市知识更新速度超过样本城市平均更新速度；V < 1，表示该城市知识更新速度低于样本城市平均更新速度。
本文从两个路径综合衡量城市的知识应用能力，一是从城市路径考虑本城市与其他城市的比值，分析本城市的知识应用能力在全国的地位如何，二是从行业路径考虑本城市知识密集型产业产值与其他所有产业产值的比值，分析本城市的知识应用能力在城市内部的地位如何。公式为：
式中：G为知识应用能力指数；gi为i市的知识密集型产业产值；g为该城市该产业总产值；G1为该城市该产业与所有样本城市该产业产值的比值；pi为i市知识密集型产业产值；p为该城市所有产业总产值；G2为该城市知识产业与城市内所有产业总产值的比值。
借用熵值法计算出上述8种方法的权重系数（表2），按照线性加权综合方法求得各基本层（八大方法）的得分，以基本层得分为基础，计算出各基本层对于目标层（职能规模、职能活力、职能强度和职能尺度）的权重系数，得到目标层的得分，最后得到每个城市知识创新职能的综合得分。
式中：Si表示城市i的知识创新职能总分时，Zij是通过线性加权算出的目标层指标得分，Wj表示目标层指标权重；而Si为某一基本层指标分值时，Zij是基本层数据，此时Wj为各类职能测度方法相对于所属基本层的权重；m为指标数量。
表2 城市知识创新职能评价指标权重赋值
Tab. 2
| 目标层 | 权重 | 基本层 | 权重 |
|---|---|---|---|
| 职能规模 | 0.237 | 显性知识存量(X1) | 0.637 |
| 隐性知识存量(X2) | 0.363 | ||
| 职能强度 | 0.247 | 知识密集型产业集中系数(CCij) | 0.562 |
| 知识创新职能专业化指数(Rij) | 0.438 | ||
| 职能尺度 | 0.265 | 知识创新中心性指数(Yi) | 0.582 |
| 知识流强度(F) | 0.418 | ||
| 职能活力 | 0.251 | 知识创造能力指数(O) | 0.304 |
| 知识更新速率值(V) | 0.349 | ||
| 知识应用能力指数(G) | 0.348 |
3.2.2 空间自相关
空间自相关分析是研究空间单元观测值是否与其相邻单元的观测值存在相关性的一种分析方法，是空间单元观测值聚集程度的一种度量[63]。全局空间自相关分析能够描述城市知识创新职能的整体空间分布状态及其显著性，常用Moran's I来进行衡量，其取值为[-1, 1]，大于0表示各城市间存在空间正相关，值越大，空间单元间的联系越紧密，小于0表示整体分布呈负相关，绝对值越大，空间差异性越大，等于0表示不存在空间自相关性，观测对象在空间上随机分布。为进一步识别异常值，采用局部空间自相关（LISA）度量每个空间单元之间的关联程度，识别各城市与其他城市知识创新的空间溢出特征。
3.2.3 地理探测器
式中：q为各影响因素对城市知识创新职能的解释力大小探测指标；m=1, 2, …, n；n为变量Y或因子X的分层，即分类或分区；Nm和N为层m和所有样本城市数量；按照自然断点将各自变量自大到小分为7类，转化为类型变量。
表3 地理探测器交互作用类型及判别依据
Tab. 3
| 交互作用类型 | 判别依据 |
|---|---|
| 双因子增强 | q(X1∩X2) > Max[q(X1), q(X2)] |
| 非线性增强 | q(X1∩X2) > q(X1)+q(X2) |
| 非线性减弱 | q(X1∩X2) < Min[q(X1), q(X2)] |
| 单因子非线性减弱 | Min[q(X1), q(X2)] < q(X1∩X2) < Max[q(X1), q(X2)] |
| 独立 | q(X1∩X2) = q(X1)+q(X2) |
4 中国城市知识创新职能测度与空间特征
4.1 知识创新职能规模
表4 前10名城市知识创新职能各指标得分及综合得分
Tab. 4
| 城市 | 显性知识存量 | 隐性知识存量 | 产业集中系数 | 专业化指数 | 知识创新中心性指数 | 知识流 强度 | 知识 创造 | 知识 更新 | 知识 应用 | 职能规模得分 | 职能强度得分 | 职能尺度得分 | 职能活力得分 | 综合 得分 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 北京 | 28.435 | 47.707 | 6.683 | 1.750 | 7.710 | 11734818 | 24.222 | 8.097 | 1.645 | 0.237 | 0.187 | 0.265 | 0.150 | 0.838 |
| 深圳 | 13.986 | 11.874 | 11.753 | 1.588 | 2.934 | 10444050 | 14.173 | 25.153 | 2.162 | 0.095 | 0.236 | 0.164 | 0.182 | 0.678 |
| 上海 | 18.152 | 19.773 | 4.969 | 1.425 | 4.640 | 7730270 | 21.297 | 11.214 | 2.068 | 0.132 | 0.145 | 0.170 | 0.160 | 0.607 |
| 南京 | 12.057 | 3.200 | 3.521 | 1.295 | 3.034 | 9791998 | 10.222 | 5.807 | 3.653 | 0.069 | 0.119 | 0.159 | 0.148 | 0.496 |
| 广州 | 14.092 | 11.472 | 4.239 | 1.316 | 3.393 | 2909989 | 15.207 | 18.936 | 1.460 | 0.095 | 0.129 | 0.101 | 0.149 | 0.475 |
| 苏州 | 9.520 | 2.674 | 4.443 | 1.393 | 1.906 | 7820685 | 9.841 | 2.227 | 2.229 | 0.055 | 0.137 | 0.120 | 0.101 | 0.412 |
| 东莞 | 5.462 | 0.319 | 6.662 | 1.180 | 0.767 | 5386130 | 5.845 | 10.816 | 2.400 | 0.029 | 0.149 | 0.075 | 0.118 | 0.372 |
| 天津 | 9.903 | 2.665 | 3.028 | 1.289 | 2.231 | 2492951 | 12.402 | 4.008 | 0.676 | 0.057 | 0.113 | 0.076 | 0.076 | 0.321 |
| 成都 | 10.956 | 12.456 | 1.595 | 1.254 | 2.572 | 467761 | 13.064 | 0.535 | 1.275 | 0.080 | 0.094 | 0.063 | 0.082 | 0.319 |
| 杭州 | 9.008 | 5.328 | 3.319 | 1.269 | 1.902 | 1638348 | 9.771 | 2.432 | 1.307 | 0.057 | 0.115 | 0.061 | 0.078 | 0.312 |
图2
图2
中国城市知识创新职能的空间分布格局
注：基于自然资源部标准地图服务网站审图号为GS(2016)2936号的标准地图制作，底图边界无修改。
Fig. 2
Spatial distribution pattern of urban knowledge innovation function
4.2 知识创新职能强度
4.3 知识创新职能尺度
4.4 知识创新职能活力
总体来看，中国知识创新职能综合得分较高的城市主要集中在东部沿海地区和中西部的少数省会中心城市，其中京津、长三角和珠三角地区是高值分集聚地，在空间上形成以京津、长三角、珠三角、陕成渝和中部武汉合肥为四顶点和中心的菱形结构，构成中国稳固的五大知识圈。为进一步判断城市知识创新职能的空间分布特征，基于全局Moran's I分析城市知识创新职能的空间集聚特征。结果显示全局Moran's I为0.212，且Z值为4.205，P值为0.005即通过显著性检验，具有统计学意义，表明中国城市知识创新职能在地理空间上的总体分布并不是随机分散的，而是呈现出显著的空间聚集特征。基于邻接关系权重矩阵下形成的局部空间自相关（LISA）特征，主要表现为高—高与低—低集聚特征，京津及长三角、珠三角地区高—高集聚的态势较为明显，重庆与邻近城市之间呈现出显著的高—低集聚的空间格局，中部少数城市呈现出低—低集聚的空间格局。
根据Jenks自然断点法将全部样本城市划分4个等级，城市知识创新职能等级自下而上逐渐增高。北京、深圳、上海无论从知识创新职能的规模、活力、强度还是尺度都处于中国的前列，可划分为国家级知识创新中心城市。南京、广州、苏州等19个城市分值虽低于第一等级的城市，但其自身具备一定的知识创新基础和优势条件，能够对周边区域产生一定影响力，可归为区域级知识创新中心城市。排在第三等级的包括沈阳、常州、珠海等34个城市的知识创新职能综合得分较低，但均在平均分0.122分以上，对地区仅有一定的知识创新影响力，可定义为地区知识创新中心城市。对于排在第四等级的城市，知识创新能力均低于平均水平，这些城市一般依赖于全国或区域知识创新中心的知识扩散，受周边区域知识创新活动的影响而进行创新，知识创新条件正在形成，可称之为知识创新发展型城市（表5）。“塔顶”城市的发展主要得益于创新要素投入和国家政策的强力支持，如直辖市、省会城市等，“塔身”城市主要依托知识密集型产业集群发展，如长三角城市群的嘉兴、徐州、扬州等，珠三角城市群中的珠海、中山和江门，京津冀城市群的石家庄、保定，共同构成相对稳定的金字塔形结构。
表5 中国182个城市知识创新职能等级类型
Tab. 5
| 等级体系 | 特征描述 | 得分范围 | 城市 |
|---|---|---|---|
| 第一等级 | 国家知识创新中心城市(Ⅰ) | [0.5, 1) | 北京、深圳、上海 |
| 第二等级 | 区域知识创新中心城市(Ⅱ) | [0.20, 0.50) | 南京、广州、苏州、东莞、天津、成都、杭州、西安、武汉、重庆、郑州、佛山、长沙、青岛、无锡、合肥、长春、宁波、厦门 |
| 第三等级 | 地区知识创新中心城市(Ⅲ) | [0.12, 0.20) | 沈阳、常州、珠海、济南、大连、中山、扬州、泰州、南通、镇江、吉林、福州、徐州、潍坊、南昌、太原、嘉兴、哈尔滨、昆明、盐城、烟台、江门、石家庄、惠州、温州、威海、贵阳、襄阳、保定、东营、泉州、洛阳、南宁 |
| 第四等级 | 知识创新发展型城市(Ⅳ) | [0, 0.12) | 宜昌、连云港、十堰、聊城、金华、邵阳、吉安、清远、绵阳、秦皇岛、滁州、邢台、河源、景德镇、赣州、台州、南阳、桂林、绍兴、淄博、德州、新乡、肇庆、濮阳、宁德、信阳、汕头、安庆、岳阳、湖州、汕尾、荆州、梅州、咸宁、柳州、兰州、孝感、上饶、郴州、丽水、西宁、淮安、济宁、安阳、云浮、乌鲁木齐、邢台、沧州、宿迁、漳州、衢州、滨州、宜城、马鞍山、菏泽、毕节、晋中、遂宁、三明、廊坊、眉山、安康、佳木斯、阜阳、运城、亳州、邯郸、南平、银川、临沂、黄冈、承德、衡水、开封、乐山、自贡、九江、阳江、泸州、齐齐哈尔、定西、荆门、龙岩、韶关、宜宾、安顺、呼和浩特、唐山、内江、泰安、临汾、资阳、海口、石嘴山、淮北、蚌埠、平顶山、长治、宿州、莆田、枣庄、咸阳、茂名、黄石、晋城、吕梁、铜陵、张掖、黄山、汉中、四平、商丘、淮南、盘锦、来宾、六安、抚州、三门峡、日照、铜川、鹰潭、随州、包头、池州、鄂尔多斯、新余、榆林、嘉峪关。 |
5 中国城市知识创新职能空间特征的影响因素分析
城市舒适性通过吸引人才来此定居和就业间接推动城市创新发展，舒适性的城市环境包括自然和人文两大方面，高素质人才对城市自然环境的追求更体现在健康养生的生态环境，包括绿化环境和空气质量[67]。在人文环境上，城市基础设施包括促进知识创造和知识传播的信息基础设施和交通基础设施是知识创新的必要条件；医疗环境和社会保障是城市为居民提供身体和心理层面的安全感，医疗环境包括城市的医院数量、医师质量及医院环境的舒适性，养老保险、医疗保险和失业保险是对创新人才的生活保障[67]。政府政策和产业环境不仅影响劳动者的迁移和流动，同时也影响企业产业结构调整和产业集聚[68]。区域文化中关于对冒险的精神、对机会的把握、对创新的追求等文化观念影响人的价值观和行为动机，动态上表现为人的创造力过程[41,69]。经济发展水平差异影响各城市知识创新能力与知识流动，经济水平高的城市承受知识合作与知识创新交易成本的能力更强，本文选择人均地区生产总值、金融机构的各项存贷款余额表示城市对知识创新企业的资金支持。经济开放的城市通过嵌入全球生产网络参与国际分工，影响城市内部生产要素的流动和资源配置[31]，交通和信息网络通达度高可以有效地降低知识流动的不确定性以及知识创新合作的交易成本[70]，本文对外开放度选取外商直接投资表示城市对外经济开放水平，用公路和航空客运量表示城市对外联系度。借助熵值法计算出城市创新职能影响因素各指标的权重（表6），依据各权重系数计算出各城市各要素层的得分。
表6 城市知识创新职能影响因素各指标的权重
Tab. 6
| 目标层 | 要素层 | 指标层 | 权重 | 功效性 |
|---|---|---|---|---|
| 城市知识 创新职能 影响因素 | 自然环境(NAT) | X1 每万人所占绿地面积(hm2) | 0.411 | 正向指标 |
| X2 PM2.5(μg/m3) | 0.589 | 负向指标 | ||
| 文化环境(CUL) | X3 每万拥有人公共图书馆藏书量(万册) | 0.234 | 正向指标 | |
| X4 每万人拥有博物馆数(个) | 0.263 | 正向指标 | ||
| X5 中小学教师数(人) | 0.289 | 正向指标 | ||
| X6 普通高等学校数(个) | 0.214 | 正向指标 | ||
| 基础设施(INF) | X7 每万人拥有公共汽(电)车营运车辆数(辆) | 0.324 | 正向指标 | |
| X8 每万人拥有出租车营运车辆数(辆) | 0.315 | 正向指标 | ||
| X9 每万人接入互联网端口数(个) | 0.361 | 正向指标 | ||
| 医疗环境(MED) | X10 医院数(个) | 0.329 | 正向指标 | |
| X11 每万人拥有医院床位数(张) | 0.340 | 正向指标 | ||
| X12 每万人拥有执业(助理)医师数(人) | 0.331 | 正向指标 | ||
| 社会保障环境(SOC) | X13 每万人参与城镇职工基本养老保险人数(人) | 0.362 | 正向指标 | |
| X14 每万人参与城镇职工基本医疗保险人数(人) | 0.338 | 正向指标 | ||
| X15 每万人参与失业保险人数(人) | 0.300 | 正向指标 | ||
| 经济环境(ECO) | X16 人均地区生产总值(元) | 0.429 | 正向指标 | |
| X17 年末金融机构人民币各项存款余额(万元) | 0.276 | 正向指标 | ||
| X18 年末金融机构人民币各项贷款余额(万元) | 0.295 | 正向指标 | ||
| 产业环境(IND) | X19 第三产业占地区生产总值的比重(%) | 0.505 | 正向指标 | |
| X20 第三产业就业人数占地区总就业人数比重(%) | 0.495 | 正向指标 | ||
| 对外开放度(OPE) | X21 外商直接投资金额(万美元) | 0.347 | 正向指标 | |
| X22 公路客运量(万人) | 0.478 | 正向指标 | ||
| X23 民用航空客运量(万人) | 0.175 | 正向指标 | ||
| 政策环境(POL) | X24 科技财政支出占总支出的比重(%) | 1 | 正向指标 |
5.1 因子探测
本文将城市知识创新职能综合得分作为因变量，2015年到2019年各影响因子得分均值作为自变量，采用自然断点法对自变量进行分层，将数值量转化为类型量进行因子探测，得出各变量对城市知识创新职能的影响程度（表7）。以显著性检验p < 0.01为条件，q > 0.1为判别影响程度显著的标准，则影响城市知识创新职能发展的主导因子为自然环境、文化环境、基础设施、医疗环境、社会保障、经济环境、产业环境、开放环境和政策环境，对应的q值分别为0.116、0.637、0.561、0.534、0.436、0.436、0.257、0.593和0.344。文化环境、对外开放度、基础设施环境和医疗环境相较其他主导因子是影响城市知识创新职能发展的重要因子，社会保障环境、经济环境和政策环境对城市知识创新职能的解释力较高，自然环境相比其他因子对城市知识创新职能的影响较弱，表明在创新时代，人文社会因素对城市知识创新职能的影响占据主导地位。
表7 因子探测器结果
Tab. 7
| 影响因子 | NAT | CUL | INF | MED | SOC | ECO | IND | OPE | POL |
|---|---|---|---|---|---|---|---|---|---|
| q | 0.116 | 0.637 | 0.561 | 0.534 | 0.436 | 0.436 | 0.257 | 0.593 | 0.344 |
| p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
5.2 交互探测
单因子分析能够探测出对城市知识创新职能空间差异具有显著影响的单因子及其影响方式，交互作用探测器却可以通过识别不同因子间对城市知识创新职能发展的交互作用，分析是否会增加或减弱对因变量的解释力，或这些因子对城市知识创新职能的影响是否相互独立。结果表明（表8），双因子交互作用对城市知识创新职能的解释力均比单因子作用强，交互作用类型以双因子增强为主，自然环境（NAT）多表现为非线性增强。这表明城市知识创新职能空间分异特征并非由单一因素或单类因素所控制，而是同时受到人文环境各因素共同影响。其中经济环境（ECO）和对外开放环境（OPE）与其他因子交互作用的影响最强，其次是文化环境（CUL）与其他因子交互作用也具有较大影响，表明城市的经济基础、开放包容性和文化氛围对知识创新的贡献力最大，是城市知识创新职能形成和发展的基础。对外开放环境（OPE）和经济环境（ECO）的交互解释力度最大为0.845，其次是文化环境（CUL）、医疗环境（MED）、自然环境（NAT）和经济环境（ECO）的交互解释力度也较大分别为0.832、0.808、0.770，自然环境（NAT）与产业环境（IND）交互作用时对城市知识创新职能影响解释力一般，表明在知识创新时代，自然环境只有与人文社会环境结合才能有效吸引人才移居和企业区位选择，进而促进知识创新活动产生。社会保障（SOC）与对外开放环境（OPE）交互作用对城市知识创新职能的影响也较大为0.812，政策环境（POL）与基础设施环境（INF）的交互作用远大于自身影响力，解释力度为0.807，表明政府政策制定要倾向于知识创新的物质条件才能极大推动城市知识创新发展。产业环境（IND）与文化环境（CUL）的非线性交互作用对城市知识创新职能的影响解释力达0.730，也远大于自身单独的解释力（0.257），说明产学研合作更有助于实现知识信息的创造、加工、传播和应用的有机整合，共同进行知识的创新与转移等知识活动。
表8 交互作用探测结果解释
Tab. 8
| NAT | CUL | INF | MED | SOC | ECO | IND | OPE | POL | |
|---|---|---|---|---|---|---|---|---|---|
| NAT | 0.116 | ||||||||
| CUL | 0.768* | 0.637 | 解释力度 | ||||||
| INF | 0.698* | 0.750+ | 0.561 | < 0.3 | |||||
| MED | 0.742* | 0.747+ | 0.671+ | 0.534 | 0.3~0.5 | ||||
| SOC | 0.658* | 0.800+ | 0.681+ | 0.659+ | 0.436 | ≥ 0.5 | |||
| ECO | 0.770* | 0.832+ | 0.762+ | 0.808+ | 0.730+ | 0.436 | |||
| IND | 0.341+ | 0.730+ | 0.689+ | 0.695+ | 0.520+ | 0.708+ | 0.257 | ||
| OPE | 0.706+ | 0.762+ | 0.798+ | 0.771+ | 0.812+ | 0.845+ | 0.700* | 0.593 | |
| POL | 0.586* | 0.798+ | 0.807+ | 0.737+ | 0.660+ | 0.749+ | 0.510* | 0.743+ | 0.344 |
注：*表示非线性增强，+表示双因子增强。
6 结论与讨论
6.1 结论
知识创新职能是城市创新职能的重要组成部分和发展基础，本文结合经济学、管理学的知识创新研究，从城市地理学城市职能视角出发，建立了城市知识创新职能研究框架，从职能规模、职能强度、职能尺度和职能活力四大维度构建城市知识创新职能测度框架，对中国182个城市的知识创新职能进行实证分析，同时采用空间自相关分析方法探讨中国城市知识创新职能的空间格局，并借用地理探测器分析其影响因素，丰富了城市创新职能的研究内容和研究方法。结论如下：
（1）城市知识创新职能是以内在知识存量和外在实践条件为基础，以满足人类新时代生存和发展需求，在知识创造、知识传播及知识应用过程中所承担的任务和所起的作用。职能规模是知识创新的基础，表现在城市知识总量的多少，职能强度是知识创新专业化水平的测度，职能尺度是城市知识创新在空间上的影响力，职能活力是城市在知识创新过程中表现的积极性和行动力，这四大维度可综合反映城市的知识创新职能水平。
（2）中国的城市知识创新职能水平发展不均衡，知识创新职能规模、职能强度、职能尺度和职能活力较高的主要分布在东部沿海及中西部少数发达地区，形成以京津、长三角、珠三角、陕成渝和中部武汉合肥为四顶点和中心的菱形知识创新结构，具有大集中、小分散的空间分布特点。根据Jenks自然断点法将全部样本城市划分4个等级，北京、深圳、上海为国家级知识创新中心城市，南京、广州等19个城市归为区域级知识创新中心城市，沈阳、常州等34个城市为地区知识创新中心城市，宜昌等126个城市知识创新条件正在形成，归为知识创新发展型城市。
（3）影响城市知识创新职能发展的主导因子为自然环境、文化环境、基础设施、医疗环境、社会保障、经济环境、产业环境、开放环境和政策环境，其中人文社会因素对城市知识创新职能的影响占据主导地位，文化环境、对外开放环境、基础设施环境和医疗环境相较其他主导因子是影响城市知识创新职能发展的重要因子，自然环境相比其他因子对城市知识创新职能的影响较弱。双因子交互作用显示城市知识创新职能空间分异特征是同时受到人文环境各因素共同影响，其中经济环境和对外开放环境与其他因子交互作用的影响最强，其次是文化环境与其他因子交互作用也具有较大影响，而自然环境只有与人文社会环境结合才能有效吸引人才移居和企业区位选择，进而促进知识创新活动产生。
6.2 讨论
根据上述研究结论，在知识经济时代培育与发展城市的创新职能是城市获得竞争力的重要动力来源，主要的政策建议是：① 应全方位提升城市知识创新职能，不仅关注城市的知识规模，也要关注城市知识创新的强度、活力及对外影响力。城市要从人才和企业的成长和发展需求出发营造符合创新主体发展的知识创新环境，科学配置创新资源，激发创新主体的创造活力，在一些领域形成较强知识专业化及优势，强化知识的中心化指数，形成较强的知识辐射力。② 城市要根据知识经济发展的时代要求，充分考虑城市自身知识经济发展的现状及特点，制定适合本城市知识经济发展的政策与措施。国家级知识中心城市要努力成为全球、全国的知识创新策源地，建设全球科技创新中心，带动国家科技发展、影响全球科技发展。区域级的知识创新城市要在一些知识领域形成优势与影响力，推动产学研内部及内外部之间知识创新过程的紧密结合，通过合理布局产学研区位和政策激励，打造知识创新产业区；对于地区级的知识创新城市及知识创新发展型城市，根据各地的市场需求和经济发展环境，建立不同创新主体主导的知识创新网络，努力建成知识输入“管道”及环境，促进地区知识吸收率的提升，充分发挥知识创新的溢出效应，既要加强对外创新联系的数量，也要重视对外创新合作的模式，将集群内部的本地扩散和城市之间的管道扩散相结合，构建城市间协同治理机制降低创新要素跨区域流动的地理距离限制和制度壁垒，拓宽知识创新职能尺度。在空间上应以京津、长三角、珠三角、陕成渝和中部武汉合肥五大知识创新圈为核心引领，带动周边地区知识创新中心城市协调发展。③ 要加强知识创新职能主导因素的建设与完善。强化人文社会因素在城市知识创新职能建设的主导地位，加强城市文化环境、对外开放环境、基础设施环境和医疗环境等建设，形成较好的企业与人才的宜居地，促进知识活动的产生。知识创新职能的理论研究和实践建设尚处于起步和探索阶段，对知识创新职能进行评价并建立科学、合理的评价指标体系更是当前乃至未来相当长一段时期内研究的热点、难点问题。在今后的研究中还需进一步修正城市知识创新职能评价体系，将不同职能类别如劳动密集型产业、自然资源密集型行业等同时纳入评价指标体系，加入职能结构维度全面分析城市的知识创新职能。
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In recent years, the emergence of the network paradigm has led to a large and growing body of scholarly research in economic geography focused on analysing the impact of innovation networks structures on knowledge flows and innovation outcomes. From a theoretical perspective, this paper aims to consider the link between networks, knowledge and innovation. Using the notion of 'network capital', whereby networks are considered to potentially offer benefits to network actors in terms of knowledge they are able to access, our paper takes 292 prefecture-level cities as the object, by using Ucinet, ArcGIS. We analyze the inter-city innovation networks structure and measure the innovation networks capital indirectly based on a unique co-patent dataset issued by the State Intellectual Property Office of P.R.China in 2014. The main findings of this study are drawn as follows: (1) The structure of the overall innovation linkages across 292 prefecture-level cities in China features 'small-world' network properties, whereby dense clusters of network actors are linked to other clusters via a relatively small number of bridging links. The city degree distribution of innovation networks is characterized by dissortative, whereby the inter-city innovation networks present a preferential attachment rule when the cities choose their innovation cooperation partners. The results demonstrate that the key nodes of innovation networks and innovative urban agglomerations can effectively improve knowledge spillovers and the value cities gain from networks. (2) The networks structure is diamond-shaped and anchored by four major metropolitan areas (Beijing-Tianjin in the North; Nanjing-Shanghai, East; Guangzhou-Shenzhen, South; Chengdu, West), which reveals a significant spatial heterogeneity. The spatial pattern of city innovation capability is degressive gradient from east to west and the high level innovation cities are in the obviously centralized distribution. The levels of city innovation capability show consistent spatial heterogeneity law with the 'structural network capital', which refers to the advantages accrued based on the structural position of cities within innovation networks. (3) The analysis strongly suggests that the centralities and structural holes of cities within innovation networks are significantly associated with the overall innovation performance of the respective cities at 0.01 confidence level. It is concluded that network structures, and resulting stocks of 'structural network capital', influence city innovation capability, indicating that network capital may be an important indicator of city innovation capability. The results of this study may provide reference for the construction of innovative cities and inter-regional innovation networks.
中国城市创新网络结构与创新能力研究
DOI:10.11821/dlyj201707009
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网络范式的兴起引起了经济地理学者对于同网络结构相关的知识流动和创新产出的关注。基于“网络资本”视角,以国家知识产权局2014年中国292个地级以上城市间合作发明专利信息为原始数据,借助Ucinet、ArcGIS、SPSS等分析工具,刻画中国城市创新网络结构,间接测度创新网络资本,评价城市创新能力,进而对网络资本与城市创新关系进行探讨。研究表明：① 城市创新网络具有小世界特征和择优连接性,培育网络中心城市和创新城市群有益于优化创新网络结构,增加网络资本;② 城市创新网络空间格局呈现京津、宁沪、广深、成都等核心节点构成的菱形结构,城市创新能力空间格局与“结构性网络资本”空间分布较为一致;③ 网络资本与城市创新在0.01的水平上显著相关,据此认为,网络结构以及由此产生的网络结构资本影响城市创新能力。研究结论可为创新型城市建设和跨区域创新网络构建提供一定的参考。
Spatial-temporal characteristics of urban innovation capability and impact factors analysis in China
DOI:10.13249/j.cnki.sgs.2017.07.006
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Based on patent and dissertation database of China’s 287 cities, the evaluation system of urban innovation capability was established in the perspective of innovation output, which is concerning the temporal-spatial evolution of innovation in China during 2001-2014. Then the article constructed the spatial econometric model to analyze influencing factors. The results are as follows: from 2001 to 2014, there are great differences of regional innovation output in China, and the output weakened from east to west, which showed an obvious trend of strengthen of western. The Gini Index of regional innovation capability in China raised at first, then decreased, which indicated that the innovation spatial patterns has evolved from polarized development to balanced development. The Gini Index of eastern where innovation output mainly concentrated in showed little change, in contract, the Gini Index of western showed declined. High level innovation hotspots widely distributed in developed cities, and the cities in the innovation secondary level are distributed in the form of agglomeration. Spatial dependence characteristic of city innovation level was significant, and further strengthen over time. H-H cluster areas are mainly distributed in Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta, and the central and western provincial capital cities as regional innovation, didn’t have obvious driven effect to neighboring city and had limited radiation effect. Economic base, human capital, education level, FDI scale, institutional factors and infrastructure could promote the development of regional innovation. Especially, economic base and human capital were the most important factors, followed by education level and institutional factors.
中国地级以上城市创新能力的时空格局演变及影响因素分析
DOI:10.13249/j.cnki.sgs.2017.07.006
[本文引用: 1]
基于中国287个地级以上城市的专利、论文数据测度中国城市创新能力,揭示2001~2014年中国创新格局的时空演变特征,并分析城市创新能力的影响因素。研究表明：① 中国创新格局刻有明显的经济地带性差异的烙印,呈“东–中–西”逐渐衰减的态势,且随着时间推移,东部的压倒性地位进一步强化。② 基尼系数呈现先增后降的倒U型变化趋势,反映了整体由极化增长向优化均衡发展的空间过程。东部地区基尼系数维持相对稳定;创新能力较弱的中西部地区,城市间的创新能力差异却在不断缩小。③ 高水平和较高水平的创新城市分布具有很强的经济依赖性,广泛分布于发达城市,而中等水平以上的城市呈集聚分布态势,表现出明显的“集群化”特征,与中国主要城市群的分布高度吻合。④ Moran’s I值均为正,并呈不断上升之势,反映了城市间显著的空间相关性。高高集聚区主要分布于京津冀、长三角和珠三角地区,而中部和西部省会城市作为区域性的创新极,对周围城市的创新带动效应并不明显,辐射作用有限。⑤ 经济基础、人力资本、教育水平、FDI规模、制度因素、基础设施6方面因素不同程度地影响城市创新能力的形成。其中经济基础和人力资本因素影响较大,教育水平和制度因素次之,而FDI规模和基础设施水平对区域的创新能力影响相对较小,但仍表现为正向影响。
A research on Chinese renovation urban system based on urban renovation function
DOI:10.11821/xb201002005
[本文引用: 1]
Based on the theoretical foundation of urban innovation function and the urban system theory in knowledge economy, this paper examines the Chinese urban innovation pattern, rank system and inter-urban innovation relationships by using the methods of the questionnaire, the interviews, factorial analysis, mathematics model and so on. The study indicates that the Chinese innovation urban system consists of five levels of tower innovation urban system structure headed by Shanghai and Beijing. Generally, the eastern coastal cities have the important positions in the Chinese urban innovation city system, the capital cities of each province and the economically developed cities have been the centers of the regional innovation. The Chinese innovation urban system were driven by factors, such as urban innovational scale, the urban scientific research scale and efficiency, the urban innovation potential and urban innovation environment and so on. By using the number of co-authored papers among the cities to measure interurban innovational relationship, the study shows that Beijing has been in the central positions of the knowledge dissemination and knowledge cooperation innovation, much more knowledge dissemination among high level cities have occurred than that among the low level cities as well as between the low level cities and high level cities, provincial capital cities and the regional central cities with strong economy have played a vital role in knowledge dissemination.
基于城市创新职能的中国创新城市空间体系
Beijing's urban innovational function based on knowledge output
DOI:10.11821/dlyj201410003
[本文引用: 1]
With the coming of the era of the knowledge economy, innovation has become one of the most important functions for cities. However, the role of cities in the regional innovation system has rarely been studied. This paper focuses on Beijing's urban innovation function to demonstrate its functional structure and strength and compares with other top Chinese cities in innovation such as Shanghai, Shenzhen, Guangzhou and Tianjin. Using the urban innovation function index and urban innovation specialization index, the paper examines Beijing's urban innovation structure, and innovation intensity compared with those of Shanghai, Shenzhen, Guangzhou and Tianjin. The results show that there exist some differences, but not substantiality, in urban innovation based on publications and granted patents. Beijing's innovation index ranks from high to low based publications from sectors of science-education-culture, transportation and information, finance and real estate, industry and construction, trade and business, and corporate departments. Based on patents granted, Beijing innovation index ranks from high to low in the order of industry, transportation and information sector, science-education-culture sector, construction sector, trade and business sector, corporate departments sector, and finance and real estate sector. Beijing has higher level innovation capabilities in almost all the sectors. Compared with Shanghai, Tianjin, Guangzhou and Shenzhen, Beijing's innovation functions in finance and real estate and construction sectors is weaker than those of Shanghai. Beijing's innovation intensity is the highest in science-education-culture industry, and is the lowest in the trade and business sector. Beijing's innovation intensity is stronger than that of Guangzhou and Shenzhen, but weaker than that of Shanghai and Tianjin based on publications. However, based on patents granted, Beijing's innovation intensity is the highest in industry, while the weakest in trade and business. This paper provides a basic method to study urban innovation functions through urban innovation structure and intensity to enrich the theoretical understanding of national and regional innovation systems.
基于知识产出的北京城市创新职能
DOI:10.11821/dlyj201410003
[本文引用: 1]
随着知识经济的发展,城市创新职能成为研究热点。基于知识产出,参考城市职能的研究方法进行城市创新职能研究,利用城市职能创新指数及城市职能专门化指数,在与中国城市创新能力位居前列的上海、深圳、广州、天津等城市比较的基础上,对北京城市创新职能结构和强度进行分析。研究表明：以论文发表量和专利授权量分别测度的城市创新职能指数和专门化指数表现出一定的差别,但总体上,北京交通信息业、科教文卫业的创新职能指数均较高。北京各行业部门的创新专门化指数均大于平均值,除少数部门外,基本优于上述四城市,但北京的创新强度并不占绝对优势。
Urban innovation system and function in Guangdong Province in the perspective of flow of urban innovation
基于创新流视角的广东省城市创新体系与职能
A review of research on the characteristics and formation mechanism of urban functions
DOI:10.18306/dlkxjz.2021.11.013
[本文引用: 1]
Urban functions are an important basis for city identity and the formulation of development strategies. Urban functions have development stage, hierarchical, and regional characteristics. It is urgent to analyze and summarize the research results of different scholars in order to grasp the pattern of urban functional characteristics and formation mechanisms. The results show that: 1) The functional characteristics of different stages of urban development include political and commercial functions in the agrarian society; and manufacturing, trade, and logistics functions in the industrial society. The post industrialization stage is characterized by the service industry, and productive services are the main functions. 2) With regard to the functional characteristics of different city scales, the higher the city level, the more comprehensive the urban functions and the stronger the service functions. 3) At different spatial scales, cities present different functional characteristics. 4) Traditional factors such as natural conditions, population, transportation, government guidance, and location play a fundamental role in the evolution of urban functions, while new factors such as science and technology, globalization, and informatization play an increasingly important role. The evolution of urban functions is also affected by regional division of labor, industrial upgrading, industrial transfer, convergence development, and other mechanisms. 5) In the future, we should include and improve the long-temporal scale and recent research on urban functions, deepen the research on urban function effect, urban function evolution mechanism, and function optimization, and promote the integration with national strategies. In addition to statistical analysis, the research methods should be supplemented by field survey, questionnaire, interview, and other methods.
城市职能特征及其形成机理研究进展与展望
DOI:10.18306/dlkxjz.2021.11.013
[本文引用: 1]
城市职能是城市定位、制定发展战略的重要依据,城市职能具有阶段性、等级性、地域性等特征,论文通过对不同学者的相关研究成果进行归纳总结,以期把握城市职能特征及其形成机理的普遍性规律。结果表明：① 不同城市发展阶段的职能特征表现为城市主导职能不断更替,农业社会阶段主要是政治和商业职能,工业社会阶段主要是制造业、商贸和物流职能,后工业化阶段是服务业职能且以生产性服务业职能为主。② 不同城市规模等级的职能特征表现为城市等级越高,城市职能综合性越强,服务业职能越强。③ 不同空间层面城市职能特征表现为,全球层面,发达国家本土通过城市职能升级,全球产业转移,形成以服务业为主导职能,发展中国家通过承接制造业转移,加速工业化,形成工业职能为主导职能,服务业职能地位不断提升;全国层面,城市职能地带性差异较大,东部工业职能依然突出,西部矿业职能、科技职能、行政职能较明显;城市群层面,世界发育成熟的城市群,在区域中心城市与外围城市之间,以及外围城市之间已经形成紧密的联系和职能分工。中国的许多城市群在中心和外围城市之间也具有明确的职能分工,中心城市以服务业职能为主,但外围城市之间职能分工还不明确。④ 城市职能演变影响因素方面,自然条件、人口、交通、政府引导、区位等传统因素起基础作用,科技、全球化、信息化等新因素作用越来越大。城市职能演变还受区域分工、产业升级、产业转移、趋同发展等机制共同作用。⑤ 未来研究方面,研究时间上应补充完善长时间尺度和近期研究的城市职能研究,研究内容上应加深城市职能效应、城市职能演变机制和职能优化方面的研究,与国家战略进行融合;研究方法上除了统计分析,还可采用调研、问卷、访谈等方法进行补充。
Analysis on influencing factors of functional specialization in PRD city group
珠三角城市群内部职能专业化的影响因素分析
The evolution mechanism of urban function in Beijing-Tianjin-Hebei urban agglomeration
京津冀城市群城市职能演变机理
The study on the functional classification of valley-cities in the western China
中国西部河谷型城市职能分类初探
The influence of information technology on urban functions
信息技术对城市职能的影响: 兼论信息化下广州城市职能转变与城市发展政策应对
Characteristics and influencing factors of urban function evolution in the Guangdong-Hong Kong-Macao Greater Bay Area
DOI:10.13284/j.cnki.rddl.003183
[本文引用: 1]
The Guangdong-Hong Kong-Macao Greater Bay Area has become the most important national strategic zone in China because of its key engine-like role for economic growth as well as being the most advanced global mega-city region in China. The mega-city region development is based on reasonable labor division and cooperation between cities. Cities should focus on their strengths and boost regular competition to promote the coordinated development of the entire region. This study examines the Guangdong-Hong Kong-Macao Greater Bay Area, which includes 19 national economic industries that are divided into 11 city functional types. Based on the data concerning the industry practitioners in each city in 2003 and 2017, this study uses the location quotient index, urban basic service scale measurement method, and Nelson urban functional intensity index to analyze the dynamic evolution of city functions in 2003 and 2017 from the perspective of three elements of urban functions. Further, the influence factors of evolution are analyzed according to the economic, political, and social aspects. First, the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area is dominated by the manufacturing and production services, with a high specialization level being observed for the manufacturing and service functions, and the evolution of these functions exhibits obvious spatial heterogeneity and industry differences. Second, at the regional level, the basic functions of the Guangdong-Hong Kong-Macao Greater Bay Area include the manufacturing industry, commerce, real estate industry, leasing business services, and software information services, whereas the non-basic functions include transportation, finance, scientific research and technology, education, culture and health, and other services and industrial functions. The functions of the service industry are observed to be significantly enhanced from 2003 to 2017. Third, at the city-scale level, the urban functions of the Greater Bay Area are primarily undertaken by the four major central cities of Hong Kong, Macao, Guangzhou, and Shenzhen, with uneven development being observed between the central and peripheral cities. However, the status of the manufacturing function in central cities declined with the rapid growth of the producer service functions and high specialization levels. In particular, Guangzhou and Shenzhen, which are two core cities in the Pearl River delta, have rapidly improved their functional status and exhibit comparatively increasing advantages with respect to transportation, software information, leasing businesses, and scientific research and technology, challenging the functional status of Hong Kong and Macao. However, the remaining node cities are dominated by the manufacturing functions, and their service functions exhibit a relatively low level of specialization. Foshan, Dongguan, Zhongshan, and Huizhou are cities that dominate with respect to the manufacturing industries, whereas Zhuhai exhibits a relatively good service function development and can build a sub-center of services in the bay area. Jiangmen and Zhaoqing have improved their functions; however, their urban influences and strengths are weak. Fourth, the evolution of urban functions in the Guangdong-Hong Kong-Macao Greater Bay Area is primarily influenced by the urban location conditions, the economic development level, the regional development policies and strategies, the economic globalization, and the most recent round of technological revolutions. In the future, policymakers and planners should focus on achieving a reasonable division of the urban landscape by relying on information technology to promote the development of the integration of manufacturing and services, promoting the transformation and upgradation of industries, and accelerating the regional system innovation and cooperation to achieve high-quality integrated development in the Guangdong-Hong Kong-Macao Greater Bay Area.
粤港澳大湾区城市职能演变特征与影响因素
DOI:10.13284/j.cnki.rddl.003183
[本文引用: 1]
以粤港澳大湾区城市群为研究对象,将19个国民经济行业归并为11个城市职能类型,基于各个城市2003和2017年的分行业从业人员数据,运用区位商指数、城市基本服务规模测算法和纳尔逊城市职能分类法,从城市职能的专业化部门、职能强度和职能规模三要素角度,对2003—2017年粤港澳大湾区城市职能演变进行动态分析,并从经济、政治、社会等方面分析了职能演变的影响因素。结果表明：粤港澳大湾区城市职能演变表现出较为明显的空间异质性和行业差异性。从区域整体来看,粤港澳大湾区城市群的基本职能为制造业、商业、房地产业、租赁商务服务以及软件信息服务,2003—2017年服务业职能明显增强。从各城市来看,粤港澳大湾区的城市职能主要由香港、澳门、广州和深圳四大中心城市承担,中心城市与外围城市发展不均衡,表现为中心城市制造业职能地位下降,生产性服务业职能增长较快,专业化水平较高;而其他节点城市则以制造业职能为主导,服务业职能专业化水平较低。粤港澳地区城市职能演变主要受城市区位条件、经济发展水平、区域发展政策与定位、经济全球化及新一轮技术革命的影响。
Study on the evolution and influencing factors of urban functional structure in Zhongyuan urban agglomeration
中原城市群城市体系职能结构的演化与影响因素分析
Formation mechanism and influencing factors of the specialized division of labor in urban functions: An empirical test based on the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area
城市职能专业化分工形成机制及影响因素: 基于粤港澳大湾区城市群的实证检验
Knowledge innovation based on knowledge fermentation
基于知识发酵的知识创新
On the connotations and their relationship of knowledge creation and knowledge innovation
知识创造与知识创新的涵义及其关系论
Knovledge renovation ability
论知识创新的能力
Benchmarking knowledge-based urban development performance: Results from the international comparison of Helsinki
DOI:10.1016/j.cities.2012.11.005 URL [本文引用: 1]
The measurement research of regional knowledge capability based on the method of entropy weight
区域知识吸收能力测度研究
Intermediaries in regional innovation systems: High-technology enterprise survey from Northern Finland
DOI:10.1080/09654310903491556 URL [本文引用: 1]
A study of the features and mechanism of knowledge innovation in university based on triple helix theory
Knowledge bases and regional innovation systems: Comparing Nordic clusters
DOI:10.1016/j.respol.2005.03.013 URL [本文引用: 1]
Research on the evolution of knowledge stock distribution in knowledge network: As the example of Henan Gongyi refractory knowledge network
知识网络中知识存量分布演化过程研究: 以河南省巩义市耐火材料知识网络为例
Constructing the innovation environment of strategic emerging industry cluster in China based on the perspective of knowledge innovation theory A case study on Hunan province
基于知识创新理论的我国战略性新兴产业集群创新环境构建研究: 以湖南省为例
Classification of industrial functions in Chinese cities (including blind counties): Theories, methods and results
DOI:10.11821/xb198804001
[本文引用: 1]
China is still at an early stage of industrialization and urbanization. At present industry is the main economic activity within most Chinese cities. This paper represents the first attempt to classify all of China’s 295 cities according to industrial functions, using 1984 data. Working within the framework of the economic base theory of urban development, the authors define city industry functions as consisting of the following three elements: (1) Specialized department of the city; (2) functional intensity (degree of specialization), and (3) functional scale (the size of industrial output of the city). The industrial function classification method used here is based on a combination of all three elements.In the present study a number of different techniques were originally applied including principal components analysis, several methods of hierarchical cluster analysis, the Nelson measure and a variety of more traditional methods. On the basis of these preliminary studies it was eventually decided to base the classification on a composite measure consisting of the ward’s Error Method of hierarchical cluster analysis and a supplementary application of the Nelson measure. In this manner the 295 Chinese cities have been grouped into 3 main categories with 19 sub-categories and 54 functional groups.
中国城市(包括瞎县)的工业职能分类: 理论、方法和结果
DOI:10.11821/xb198804001
[本文引用: 1]
在城市发展的经济基础理论的指导下,作者提出城市工业职能的概念包括城市的专业化部门、职能强度和职能规模三个要素。城市工业职能分类就是按照上述三要素的相似性和差异性对城市进行分类。本研究曾采用主因素分析、聚类分析和纳尔逊的统计分析等多种方法。最后以沃德误差法的聚类分析结果作为分类的基础,稍加修正,并以纳尔逊法分析结果作补充。中国1984年的295个城市被分成三个大类、十九个亚类和五十四个职能组。
Some theoritical thingkings on urban function system
城市职能体系的若干理论思考
An analytical concept frame on urban function: Yangtze delta area as an example
城市职能的概念性分析框架: 以长三角为例
Knowledge, Innovation and Space
Knowledge bases, talents, and contexts: On the usefulness of the creative class approach in Sweden
DOI:10.1111/j.1944-8287.2009.01051.x URL [本文引用: 1]
Business services as actors of knowledge transformation: The role of KIBS in regional and national innovation systems
DOI:10.1016/S0048-7333(01)00164-0 URL [本文引用: 1]
Residence, workplace and commute: Interrelated spatial choices of knowledge workers in the metropolitan region of Munich
DOI:10.1016/j.jtrangeo.2017.05.012 URL [本文引用: 1]
The impact of foreign direct investment on the specialization of urban functions: An evidence based on the Pearl River Delta urban agglomeration
外商直接投资对城市职能专业化的影响: 基于珠三角城市群的证据
Function contents of S&T innovative city and its evaluation index system and criterion
科技创新城市的功能内涵、评价体系及判定标准
Research on the coupling relationship between urban vitality and urban expansion based on multi-source night light data: A case study of urban agglomerations on the west bank of the Taiwan Straits
基于多源夜间灯光数据的城市活力与城市扩张耦合关系研究: 以海峡西岸城市群为例
Evaluation and analysis on regional innovation capability in middle China
中部六省区域创新能力的评价与分析
Measurement of regional knowledge absorptive capacity in China
中国区域知识吸收能力测度
A study of the functional features of China's urban service industries
DOI:10.11821/dlyj201509007
[本文引用: 1]
A study of the functional features of China's urban service industries can provide scientific support for the development of the urban service sector to an appropriate scale, industrial make-up and spatial structure, and for exploring different routes for development from city to city. Based on a survey of 287 cities at prefecture level and above in China, this paper uses Nelson methods, flow measurement models, B/N ratio and spatial autocorrelation to conduct quantitative research on the functional scale, specialized units and functional strength of urban service industries in various cities in China. It presents an analysis of the level of labor division, development trends and spatial patterns of different service industries. The research demonstrates that China's exported services are highly concentrated in high-end service centers, and that the functional structures of service centers at various levels differ sharply. Geographically, both the import and export of urban services progressively diminish in scale from eastern to central and western China. Sources of exported urban services show no tendency to concentrate in a certain area, but the import of urban services displays a slight concentration in space, where cities are clustered in the same way as major urban agglomerations of China. In different service industries, the levels of labor divisions vary, but are deepening overall. Finally, it was found that imported service industries are of relatively low concentration, and because all cities demand the development of service industries, it is asserted that tailored development strategies should be taken in individual cities.
中国城市服务业职能特征研究
DOI:10.11821/dlyj201509007
[本文引用: 1]
中国城市服务业职能特征研究可为构建合理的城市服务业职能规模结构、行业结构和空间结构及各城市服务业差异化发展提供科学依据。以中国287个地级及以上城市为单元,应用纳尔逊方法、城市服务业流量测度模型、服务业行业B/N比和空间自相关等方法,定量测算各城市服务业职能规模、专业化部门、职能强度,分析各服务业行业的分工水平、演变趋势和空间格局。研究表明：① 对外服务业输出流量高度集中于高级服务业中心,各等级服务业中心的服务业职能结构差异较大;② 城市服务业输出和输入流量规模在地带性尺度上都呈“东中西”三级递减空间格局。城市服务业输出流量空间没有显著集聚区域,而城市服务业输入流量空间上有弱集聚特征,呈“大分散小集中”格局,集聚城市呈“群”状分布,与重要城市群分布较一致;③ 各服务业行业分工强度存在差异,服务业整体分工在深化,具体行业分工有快速深化、缓慢深化、稳定三种类型;④ 服务业输入流量规模集中性比较低,各城市有普遍服务业需求,应根据需求类型采取不同发展策略。
Urban flows in the HuNingHang urban compact district
沪宁杭城市密集区城市流研究
Spatial distribution pattern and environmental impact of lung cancer in Henan
DOI:10.11821/dlxb202201017
[本文引用: 1]
Lung cancer is one of the most common malignant tumors and the main cause of death related with cancer. The incidence rate and mortality rate of lung cancer in Henan are the highest of the cancers. Therefore, it is of great significance to study the spatial distribution pattern of lung cancer and the effects of environmental factors on it for the prevention and control of lung cancer. Based on the data of lung cancer incidence in Henan from 2016 to 2018, the spatial distribution pattern of lung cancer in the province was studied by using spatial autocorrelation analysis. Using the software Geodetector, we quantified the explanatory power of each factor and its interactions on lung cancer incidence rate. The results showed that lung cancer has obvious characteristics of agglomeration, the high incidence areas are concentrated in the plains and basins of central, eastern and southern Henan. Among the 12 environmental factors selected, PM2.5 concentration, O3 concentration, annual average wind speed, proportion of employees in mining industry and per capita GDP have decisive power, while the per capita GDP and proportion of medical staff have a non-linear enhancement effect on the determination of various factors. The results can provide technical support for research on pathogenesis, prevention and treatment of lung cancer in Henan.
河南省肺癌空间分布格局及环境因素影响
DOI:10.11821/dlxb202201017
[本文引用: 1]
肺癌是最常见的恶性肿瘤之一,也是主要的肿瘤死因,河南省肺癌发病率和死亡率常年居恶性肿瘤首位,研究肺癌的空间分布格局及其与环境因子的关系对肺癌的相关防控工作意义重大。本文以2016—2018年河南省肺癌发病数据为研究对象,使用空间自相关分析方法研究河南省肺癌的空间分布格局,基于地理探测器量化各个环境因子及其两两交互作用对肺癌发病率的解释力。结果表明：空间上肺癌具有明显的集聚特征,高发区集中分布于豫中、豫东和豫南的平原和盆地地区。在所选的12种环境因子中,PM<sub>2.5</sub>浓度、O<sub>3</sub>浓度、年均风速、采矿业从业人员占比、人均GDP具有更高的决定力,人均GDP和医护人员占比则对多种要素的决定力均具有明显的非线性增强的作用。研究结果可以为河南省肺癌发病机理研究和相关防治工作提供科学支撑。
Geodetector: Principle and prospective
DOI:10.11821/dlxb201701010
[本文引用: 1]
Spatial stratified heterogeneity is the spatial expression of natural and socio-economic process, which is an important approach for human to recognize nature since Aristotle. Geodetector is a new statistical method to detect spatial stratified heterogeneity and reveal the driving factors behind it. This method with no linear hypothesis has elegant form and definite physical meaning. Here is the basic idea behind Geodetector: assuming that the study area is divided into several subareas. The study area is characterized by spatial stratified heterogeneity if the sum of the variance of subareas is less than the regional total variance; and if the spatial distribution of the two variables tends to be consistent, there is statistical correlation between them. Q-statistic in Geodetector has already been applied in many fields of natural and social sciences which can be used to measure spatial stratified heterogeneity, detect explanatory factors and analyze the interactive relationship between variables. In this paper, the authors will illustrate the principle of Geodetector and summarize the characteristics and applications in order to facilitate the using of Geodetector and help readers to recognize, mine and utilize spatial stratified heterogeneity.
地理探测器: 原理与展望
DOI:10.11821/dlxb201701010
[本文引用: 1]
空间分异是自然和社会经济过程的空间表现,也是自亚里士多德以来人类认识自然的重要途径。地理探测器是探测空间分异性,以及揭示其背后驱动因子的一种新的统计学方法,此方法无线性假设,具有优雅的形式和明确的物理含义。基本思想是：假设研究区分为若干子区域,如果子区域的方差之和小于区域总方差,则存在空间分异性;如果两变量的空间分布趋于一致,则两者存在统计关联性。地理探测器q统计量,可用以度量空间分异性、探测解释因子、分析变量之间交互关系,已经在自然和社会科学多领域应用。本文阐述地理探测器的原理,并对其特点及应用进行了归纳总结,以利于读者方便灵活地使用地理探测器来认识、挖掘和利用空间分异性。
Impact of urban innovation environment on the performance of industry-university-research institute collaborative innovation
城市创新环境对产学研合作创新的影响
Study on determinants of knowledge innovation capacity in industrial clusters
产业集群内知识创新能力影响因素的实证分析
The relationship between urban amenity,difference and innovation ability in Beijing
DOI:10.13249/j.cnki.sgs.2022.01.011
[本文引用: 2]
Urban amenity has an important impact on urban innovation and development, but there is a lack of research on the impact of metropolitan internal amenity and differences on urban innovation ability. Taking Beijing as the research area, this article uses coupling coordination degree model and multiple linear regression models to analyze the relationship between urban amenity and innovation ability. The main conclusions of this article are as follows. 1) The overall level of urban amenity distribution of Beijing gradually decreases with the increase of the distance from the central urban area to suburbs. 2) The northwestern districts in Beijing has better ecological environment amenity than the southeastern districts. However, artificial environment and social atmosphere decrease from the urban center to the surrounding areas and the urban amenity level of each district shows a phenomenon of agglomeration in spatial distribution, with high-high clustering concentrated in Dongcheng District, Xicheng District, Chaoyang District, Haidian District and Fengtai District and forming a “high urban amenity” area in Beijing. 3) Based on the coupling coordination degree model, it is found that except Pinggu District, Miyun District and Yanqing District, other urban areas in Beijing are in the high-level coupling stage. Haidian District, Dongcheng District, Xicheng District, Chaoyang District and Fengtai District have a high degree of coupling and coordination of innovation ability and amenity development, but there is also a lag between the development of urban amenity and urban innovation in all districts of Beijing. 4) There is a positive correlation between urban amenity and innovation ability in Beijing, but different urban amenity factors have different effects on innovation ability. Cultural and educational amenity has the greatest impact on urban innovation ability, and traffic amenity and social atmosphere have the second effect on innovation ability. The policy implication of this paper is that Beijing should promote the relative balanced development of artificial amenity level such as culture, education and traffic amenity to improve the coordination of urban amenity to enhance urban innovation ability of each district.
北京城市舒适性、差异性与创新能力的关系
DOI:10.13249/j.cnki.sgs.2022.01.011
[本文引用: 2]
城市舒适性对城市创新发展有重要影响，但对大都市区内部舒适性及差异性对城市创新能力的影响研究较少。以北京市为研究区域，利用耦合协调度模型和多元线性回归模型分析城市舒适性与创新能力的关系。研究发现：① 北京市总体舒适性分布特点是以中心城区为核心，随着距中心城区距离增加，城区的舒适性水平逐渐降低。② 北京生态环境舒适性呈现从西北地区向东南地区逐渐递减的特点，而人工环境和社会氛围的舒适性由中心向四周地区递减，各区的舒适性水平在空间分布上呈集聚状态，高-高型聚类集中在东城区、西城区、朝阳区、海淀区及丰台区，形成北京市“高舒适性”区域。③ 结合耦合协调度模型发现北京市除平谷区、密云区和延庆区，其他城区目前均处于高水平耦合阶段，海淀区、东城区、西城区、朝阳区、丰台区的创新能力和舒适性发展耦合协调度较高，但北京市各区也存在舒适性与城市创新之间发展相对滞后问题。④ 北京城市舒适性与创新能力存在正相关关系，但不同舒适性要素对创新能力的作用不同，文化教育条件对城市创新能力的影响强度最大，交通便利性和社会氛围对创新能力的作用次之。政策建议是要推进文化教育、交通条件等人工舒适性水平的相对均衡发展，提高各区的城市舒适性，以城市舒适性推动创新能力的提升。
Cultural competence: Part Ⅰ of the 5Cs framework for twenty-first century key competences
文化理解与传承素养: 21世纪核心素养5C模型之一
Network structure evolution and influencing factors of collaborative knowledge innovation in Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration
粤港澳大湾区城市群知识创新合作网络结构演化及影响因素研究
