全球化时代人口、产业、能源、贸易等关键生产要素跨区域快速流动,人地关系由静态走向动态,由孤立化走向网络化,地理尺度被充分放大,标志着人地关系从区域性向全球性转变。粤港澳大湾区作为我国对外开放的前沿阵地之一,改革开放四十年来其人地关系已经发生了深刻的变化,亟需重新认识和研究粤港澳大湾区人地关系的全球配置与区域响应模式,为粤港澳大湾区在全球和区域尺度合理配置人地关系核心要素,建设世界一流湾区提供理论支撑。本文在把握新时代人地关系的全球性转变基础上,对粤港澳大湾区人地关系研究进行了展望：① 从理论上探讨世界一流湾区人地关系从区域性转向全球性的一般模式和规律;② 分析改革开放以来粤港澳大湾区人地关系的全球配置过程及其驱动因素变化;③ 剖析全球模式下湾区内部的区域响应差异与区域协同机制;④ 探索“创新-产业-环境”关键系统全球模式的重构路径;⑤ 提出新时代粤港澳大湾区人地关系的优化与调控路径。

基于对全球创新格局与趋势的探讨,以及对国际科技创新中心内涵与特性的解读,剖析了粤港澳大湾区当前建设国际科技创新中心所具备的优势与面临的挑战,并对科技创新中心建设提出几点战略性的思考。当前粤港澳大湾区正处于整体迈进知识经济时期,创新要素在地理空间上高度集聚,创新生态条件不断完善,创新全球化的影响在区域内初显,拥有雄厚的制造业基础,市场对创新的需求极为可观,基本具备建设国际科技创新中心的条件。但同时,我们应该看到由于粤港澳一体化的制度障碍以及巨大的区域内部差距,粤港澳大湾区的创新发展还存在创新要素聚而不联、创新要素流动不畅、体制机制转换对接困难、关键核心技术受制于人等现实挑战。在充分发挥粤港澳互补优势、补齐短板的基础上,粤港澳大湾区应立足源头创新,将建设国际产业创新策源地作为立区之本;实施产业驱动,将建设国际产业科技创新中心作为核心功能;推进制度创新,将建设协同创新示范区作为关键突破。以源头创新促产业创新,以制度改革推协同创新,最终实现粤港澳大湾区创新协同与一体化融合发展。

As capital’s pursuit of knowledge has become an increasingly significant global trend, scientific and technological innovations have gradually replaced the development mode of factor-driven and resource consumption, supporting as the driving force of economic development in the new era by influencing the industry transformation and promoting new formats, along with fundamentally guiding the transformation of the leading functions of cities or regions toward scientific and technological innovation. Furthermore, the economic competition among cities or regions around the world is more prominently manifested in the competition of science and technology. As the embodiment and core support of a country’s comprehensive scientific and technological strengths, a scientific and technological innovation center could play a significant value-added role by occupying a leading, dominant position in the global value grid. This is an important measure for many countries and regions to cope with the challenges of each new round of scientific and technological innovations. Moreover, it also enhances national competitiveness. Based on the discussion of global innovation patterns and trends, as well as the interpretation of the connotation and characteristics of international technology innovation centers, this paper analyzes the advantages and challenges that the Guangdong-Hong Kong-Macao Greater Bay Area might face in constructing a global technology innovation center, and some strategies are suggested for the future development. At present, the Guangdong-Hong Kong-Macao Greater Bay Area are in a period of entering knowledge economy as a whole, wherein innovation elements are highly concentrated geographically, and innovation ecological conditions are constantly improved. The impact of innovation globalization is emerging in this region, and the demand for innovation is as strong as the manufacturing industry foundation within this area. The basic conditions for the construction of an international science and technology innovation center are sufficient. However, owing to institutional obstacles in integrating Guangdong-Hong Kong-Macao Greater Bay Area and the huge intra-regional gap, some practical challenges in innovation development of this region still exist, such as the disconnection of innovation elements, the unsmooth internal flow of innovation elements, the difficulty of system-mechanism transformation, and the dilemma of relying on importation of core technologies. To take benefit from the favorable characteristics of Guangdong, Hong Kong and Macao, and mitigate any inherent shortcoming, the construction should be based on the establishing of a hotbed of international industrial innovation. In the meantime, this region should choose the construction of an international industrial science and technology innovation center as its core priority. This will accelerate institutional innovation and consider the construction of collaborative innovation demonstration zone as the key to breakthrough. Boost industrial innovation by enhancing the ability of original innovation, promote collaborative innovation by institutional reform, and finally realize the coordinated and integrated development of Guangdong-Hong Kong-Macao Greater Bay Area’s innovation.

建设国际科技创新中心是粤港澳大湾区新时代最有共识、最有优势、最富挑战的战略方向,亟需国际科技创新中心建设的理论探索。国内外经典创新系统理论更加专注于创新系统内部,注重单一空间的创新要素与创新活动组织问题,忽视了全球和区域之间要素的关联模式,缺乏在全球视野下宏观与微观结合的综合观察。由此,本文在总结国内外经典创新系统理论的基础上,构建了全球视野下的以“科技”和“人才”为核心,以“科技-产业-全球生产网络”和“人才-环境-世界城市网络”为链条的国际科技创新中心理论模式,认为国际科技创新中心是全球创新网络、全球生产网络和世界城市网络三重网络结构的核心节点,建设国际科技创新中心需要实现三重网络的协同效应。在这样的理论框架下,分析了粤港澳大湾区双核心与双链条的发展情况,并以此提出了粤港澳大湾区建设国际科技创新中心的路径和相关建议。

Developing the region into an international innovation and technology hub is the most common, the most advantageous, and the most challenging strategic direction for the Guangdong-Hong Kong-Macao Greater Bay Area in the new era. There is an urgent need for the theoretical research and developing studies of the international innovation and technology hubs. Classical theories of the innovation systems focus more on the interior of innovation systems, paying attention to innovation elements and organization of innovation activities in a single space, but neglect the inextricable link between global and local elements and organizations, lacking comprehensive observations combining macro and micro perspectives. Therefore, on the basis of summarizing the classic innovation system theories, this paper constructs a theoretical model with global vision for the Guangdong-Hong Kong-Macao Greater Bay Area constituting international innovation and technology hubs: "Science and technology" and "talent" are the cores, and "science and technology-industry-global production network" and "talent-environment-world city network" are two chains. Focusing on science and technology and the pooling of talents, it is needed to gather elements of innovative resources, enhance regional innovation capabilities, drive industrial transformation and upgrading, and improve urban functions and environments, so as to reshape the role in the global production network and the world city network. It is considered that the international innovation and technology hubs are the core nodes of the triple networks: global innovation network, global production network and world city network. To develop into an international science and technology innovation center, it is necessary to upgrade its strength and position in the three networks at the same time. Compared with the existing innovation system theories, the new model attaches importance to the internal and external relations with both global and local visions, in which the chain structure avoids the simple listing of elements, but emphasizes the mechanisms of the innovation system. Under this theoretical framework, the double-cores (talent & science and technology) and double-chains (science and technology-industry-global production network & talent-environment-world city network) of the Guangdong-Hong Kong-Macao Greater Bay Area are analyzed, and the paths for the Guangdong-Hong Kong-Macao Greater Bay Area developing into an international technological innovation center are proposed. This paper provides an explanatory tool for the organizing similarities and differences between international innovation and technology hubs under the background of globalization, which is a supplement to the theories of innovation systems.

创新活动发生在具体地点上,其空间黏性已有广泛关注,但当前研究却未能深入微观尺度的创新空间集聚,难以精细化识别创新潜力。文章结合知识、技术、环境三维创新的角度搭建基于兴趣点（POI）微观数据的科技创新潜力空间指标体系,所得结果与粤港澳大湾区都市发展现状高度一致。同时发现：1）科技创新潜力的空间集聚显著,其中知识创新向核集聚,技术创新呈轴带式外溢,但创新环境存在较大协同缺失；2）热点分布呈现出点群集聚、组团联动、多中心并存等特征,形成智慧科研型、产业科技型、环境孵化型、综合发展型等16个创新集聚区,受到“亲水性”布局与传统行政力量的重要影响；3）微观视域下同时体现了空间上的功能性分异及较强的边界模糊效应,广–莞–深–港创新廊道基本成型,不过区域多层次非均衡分布与创新深度融合仍需改善。根据研究结果,需要进一步加大创新的环境营造与三维融合,增强重要创新节点的辐射,明确创新产业差异化定位,深化创新活动的无界互动融通,从而推动湾区创新协同发展。

Innovation regularly appears at different venues and areas within cities, and its spatial stickiness has been widely studied and confirmed. However, most current research on spatial innovation has not been conducted in a micro-scale manner to decode the inner-city potentials. From an urban research perspective, this makes understanding the internal clustering mechanism a significant challenge. Thus, this article uses Points Of Interest (POI) data to explore a Scientific and Technological (S&T) spatial index system focusing on innovation potential with a consideration of three dimensions: knowledge, technology, and environment. It analyzes the micro-scale agglomeration structure of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). First, an inverted U-style innovative core belt embracing the Pearl River in the middle is formed. This area has more substantive innovation potentials on the east than the west coast. Guangdong-Foshan and Shenzhen-Dongguan innovation clusters are then built simultaneously. In both practice and theory, these research findings are consistent with the metropolitan agglomeration pattern and research on the GBA. Additional results are: 1) A notable cluster effect emerges in the spatial distribution of GBA's innovation potentials. Considering the division of dimension, knowledge-based innovation is likely to be led by Guangzhou and Hong Kong, while technological innovation stretches in strong belt-style spillovers along the Guangzhou-Shenzhen S&T Innovation Corridor. However, there is a dearth of innovation in these incubators' environments, and collaboration between them needs to be drastically improved. 2) The hotspot structure shows characteristics such as node clustering, group linking, and multicenter coexistence, while 16 Innovation-intensive Zones (IIZs) emerged in four distinct innovative pathways. These are knowledge-and-research-based, industry-led, environment-incubated, and comprehensively developed, and being close to rivers and streams are significantly influenced by the hydrophilic clustering effect, with a free and comfortable atmosphere inspiring innovation. Traditional administrative forces also have a significant impact, especially on the peripheral areas where innovative activities rely on government planning and the pull-forces from downtown. 3) From the perspective of industry differences, a robust functional differentiation is spatially mirrored. The innovation of intelligent equipment manufacturing has a strong outward diffusion, and the energy, chemical and core electronic industries display an inward node effect. While biomedicine innovation spreads across the two core areas, it is necessary to strengthen information and communication in a broader region with higher potential. 4) In a micro-scale way, an indistinct boundary shapes the Guangzhou-Dongguan-Shenzhen-Hong Kong innovation corridor. However, the need is still urgent to calibrate the regional imbalance and intensify deeply-integrated innovation in light of the vast spatial differentiation and insufficient cooperation between the east and west coasts, the Pearl River Delta and Hong Kong-Macao, and the central and peripheral areas of innovation development. Based on these issues, it is essential to strengthen the emergence of an innovative environment and integrate it with knowledge-based and technology innovation. The aim is to promote the diffusion of pivotal innovative nodes and then specify the differentiated positions of innovative industries to create a region free from boundary constraints conducive to innovation, communication, and cooperation. Thus, for spatial planning in the GBA, the quest for a higher level of innovative potential is imperative, and the integration of collaborative innovation needs to be pursued vigorously.

基于1990-2016年“Web of Science”核心合集所收录的科研论文合著数据,借助基尼系数测度属性和功能多中心性的方法,对粤港澳大湾区城市群的知识多中心性及其知识网络的演化进行了研究。结果发现：① 伴随着粤港澳大湾区城市群知识生产总量的持续增长,其多中心性程度呈现出阶段性、阶梯式提升的特征,分别经历了波动、增长和分化的发展阶段,港澳回归后的2000-2010年间是多中心性快速增长的重要阶段。② 粤港澳大湾区城市群在区域、国家和全球尺度上的功能多中心性程度随着尺度增加逐级递减,进一步证实了功能多中心性的尺度规律性;而且发现了多中心在演化中的尺度敏感性,全球尺度上的多中心性会存在不确定的峰值,而区域尺度上的多中心性可能会持续增加。③ 城市群多中心的演化是受制度接近、地理接近和等级接近影响,在研究人员移动、科研单位联动和政府政策推动及其行动主体间的相互作用下实现的,多中心程度的增加有助于推动粤港澳大湾区城市群构建科研协同创新共同体。

The concept of megalopolis, since its original inception six decades ago, has inspired many new terms that mainly describe large-scale urbanized forms such as megaregions and polycentric urban regions. However, recent studies have increasingly focused on the two key functions that megalopolises act as an incubator of new ideas and trends and as a hub that articulates knowledge exchange at the megalopolitan, national, and global scales. While the recent studies have mainly analyzed the functional aspects of megalopolis based on China's Yangtze River Delta region, this paper investigates the evolving process and mechanisms of knowledge collaboration within and beyond Guangdong-Hong Kong-Macao Greater Bay Area (GBA) - one of the most promising and vibrant megalopolises in China. In addition, the GBA megalopolis is unique because it contains Hong Kong and Macao, which have a different political system from China's mainland. Drawing upon a dataset of publications that were indexed in Web of Science Core Collection during the 1990-2016 period, this paper uses the Gini coefficient to measure the degree of knowledge polycentricity of the GBA megalopolis. Here, knowledge polycentricity is further classified into attribute polycentricity of knowledge production and functional polycentricity of knowledge collaboration within and beyond the GBA megalopolis. Whereas the attribute polycentricity refers to the distribution inequality of the total publications of GBA cities, the functional polycentricity represents the distribution inequality of GBA cities' knowledge collaboration at different geographical scales. Our empirical results show: (1) knowledge production of the GBA megalopolis as a whole has experienced a robust and continuous growth. The degrees of both attribute polycentricity and functional polycentricity have also been on the increase in general, although there are some fluctuations in early years and some deviations in recent years. During the ten years after Hong Kong and Macao returned to China (the 2000-2010 period), the degree of knowledge polycentricity of the GBA megalopolis especially enjoyed the fastest rise; (2) The degree of functional polycentricity decreased with the expansion in the geographical scales at which it is measured, confirming the findings of previous studies that functional polycentricity is scale-dependent. Moreover, we find that the degree of functional polycentricity becomes more fluctuated at the global scale while it tends to increase continuously at the megalopolitan scale; (3) The evolving process of knowledge polycentricity of the GBA megalopolis is influenced by institutional proximity, geographical proximity and status proximity between cities. Specifically, the mobility of researchers, the collaboration of universities and research institutes, and the coordination of local governments are three major forces promoting the evolution of knowledge polycentricity of the GBA megalopolis. Overall, the increasing knowledge polycentricity would be of significance for the GBA megalopolis to form a knowledge-driven region of collective collaboration.

以2000—2018年国内外期刊数据库合作论文数据为基础,借助社会网络分析和空间结构指数法分析了粤港澳大湾区知识空间网络结构演化特征与影响因素,结果发现：1）知识网络格局由广州的“一家独大”逐渐演变为广州、深圳、香港“齐头并进”的发展态势。香港虽然处于知识网络的核心位置,但受行政壁垒的影响,主要与广州、深圳高等级的城市建立紧密的知识合作联系。2）粤港澳大湾区知识联系网络呈现“核心—边缘”结构,西部地区知识联系远低于东部地区,虽然研究期内湾区的知识网络的极化特征得到一定的缓解,但不均衡性仍然显著。3）湾区知识活动主体的自身需求是促进城市间知识合作的内在驱动力,知识环境和知识联系通道是区域知识合作网络外在推动力,在内生作用和外生作用的共同影响下,知识合作产出得以实现,粤港澳大湾区知识网络得以发展。

Increasing globalization and informatization has enhanced the intercity exchange of information, materials, and energy. Cities no longer represent isolated systems. Instead, they are closely linked to each other, forming regional or global city network systems. Therefore, the study of urban networks has attracted massive attention in human geography and urban planning. In particular, the emergence of the concept of “space of flow” provides a new perspective and paradigm for the interpretation of regional spatial structure. Based on the data collected from domestic and foreign journal database published from 2000 to 2018, this paper uses social network analysis method and spatial structure index method to explore the evolution process of the overall characteristics, organizational structure, and the spatial pattern of the knowledge network in Guangdong-Hong Kong-Macao Greater Bay Area. Furthermore, it identified the evolution trend of factors influencing the knowledge network in the Bay Area. The results also revealed the following: 1) Over the duration of the research, publications in the Greater Bay Area significantly increased. The pattern of the knowledge network gradually evolved from the “single power” represented by Guangzhou to “simultaneous development” that included Guangzhou, Shenzhen, and Hong Kong. Although Hong Kong is at the core of the knowledge network, it establishes close knowledge cooperation primarily with Guangzhou and Shenzhen due to administrative barriers. 2) The knowledge network of the Guangdong-Hong Kong-Macao Greater Bay Area represents a “core-edge” structure with the knowledge connection in the western region significantly lower than that in the eastern region. The knowledge network densities and spatial structure indices of the Guangdong-Hong Kong-Macao Greater Bay Area suggest an increasing volatility. In 2016, the knowledge network density of the Bay Area attained the maximum value, indicating the development and maturity of the overall knowledge connection of the Guangdong-Hong Kong-Macao Greater Bay Area. In addition, the spatial structure indices demonstrate an alleviation of polarization characteristics of knowledge networks in the Bay Area, despite persistent significant imbalance. 3) The demand of the knowledge activity actors such as universities and scientific research institutions in the Bay Area is the internal driving force promoting knowledge cooperation among cities. The knowledge environment and the knowledge connection channels are the external driving forces of the regional knowledge cooperation network. The influence of endogenous and exogenous factors is responsible for the output of knowledge cooperation, resulting in the development of the knowledge network in the Guangdong-Hong Kong-Macao Greater Bay Area. This study provides a reference for the development of innovative collaborative paths in the Guangdong-Hong Kong-Macao Greater Bay Area by refining the characteristics of Bay Area’s knowledge network.

在城市群创新网络视角下,采用粤港澳大湾区城市群创新相关数据,基于主成分分析法、地理引力模型与社会网络分析方法,研究了粤港澳大湾区创新水平及城市创新分工。研究结果表明,从创新分工来看,创新研发集中于珠三角9市,创新转化集中于香港和澳门。进一步分析创新研发合作情况,深圳以企业应用型创新为主,广州以基础创新为主,二者处于湾区创新网络的中心位置,带动了交互创新的4个城市子群,并在空间上形成创新分工三大片区。粤港澳大湾区已初步形成创新集群式发展格局,为湾区经济增长提供了有力支撑,成为中国经济高质量发展的重要空间载体。

From the perspective of innovation network of urban agglomerations, using the relevant data of innovation of urban agglomerations in the Guangdong-Hong Kong-Macao Greater Bay Area, and based on the principal component analysis, geographical gravity model and social network analysis, this paper studies the innovation level and division of urban innovation in the Guangdong-Hong Kong-Macao Greater Bay Area. The results show that from the perspective of innovation division of labor, innovation research and development are concentrated in nine cities in the Pearl River Delta, and innovation transformation is concentrated in Hong Kong and Macao. Further analysis of innovation and R & D cooperation shows that Shenzhen focuses on enterprise application-oriented innovation, while Guangzhou focuses on basic innovation, and they are in the center of innovation network in the bay area, driving four urban subgroups of interactive innovation, and forming three areas of innovation division in space. The Guangdong-Hong Kong-Macao Greater Bay Area has initially formed an innovative cluster development pattern, which provides a strong support for the economic growth of the bay area and becomes an important space carrier for the high-quality development of China's economy.

知识经济时代人才是建设粤港澳大湾区世界级城市群的重要生产要素。本研究采用2005年、2010年和2015年广东省人口普查和1%抽样调查数据,以及香港和澳门对应口径的统计数据,以县市为基本单元,提出从受教育程度和职业技能两个口径测度人才集聚水平,系统解析粤港大湾区高学历与高技能人才集聚的演化格局及影响因素。结果表明：① 粤港澳大湾区作为中国经济高度发达地区,人才集聚优势高度集中在香港、澳门,内地珠三角城市群的人才集聚水平低于京津冀城市群和长三角城市群。② 2005—2015年,粤港澳大湾区高学历人才集聚持续均衡化,而高技能人才集聚优势仍然体现在香港、澳门,内地因为发展教育提升的高学历人力资本尚未完全有效转化为高技能人力资本。③ 香港人才集聚水平处于绝对领先,澳门、广州、珠海和深圳次之,而外围县市相对处于人才洼地,特别是制造业发达的佛山、东莞人才集聚水平相对偏低。④ 面板模型表明,服务业对高技能人才集聚的拉动效应强于高学历人才,而制造业的拉动作用并不突出。高等教育对高技能人才集聚的带动作用要弱于高学历人才。高薪资待遇有利于促进高学历人才集聚,但对高技能人才集聚的促进作用有限。新时期,亟需推动粤港澳三地管理制度衔接、产业转型升级和优质高等教育建设,推动粤港澳大湾区建设国际创新中心。

In the era of knowledge economy, talents concentration plays a key role in the development of a world-class urban agglomeration of Guangdong-Hong Kong-Macao Greater Bay Area (GHM). Based on the population census of Guangdong Province in 2010, the 1% population sampling survey in 2005 and 2015, and employment statistics in Hong Kong and Macao, this study measures the level of talents concentration from two perspectives of educational attainment and occupation on the county scale, and analyzes the evolution pattern and motivations of talents concentration in the GHM. The results show that: (1) GHM is one of the highly developed economic areas in China, and there is the absolute advantage of talents concentration in Hong Kong and Macao, while the level of talents concentration in the Pearl River Delta urban agglomeration is lower than that in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. (2) From 2005 to 2015, the spatial distribution of highly-educated talents in the GHM tends to be balanced, and there is also the advantage of the concentration of highly-skilled talents in Hong Kong and Macao. The increasing human capital defined by educational attainment in the mainland, which is caused by the expansion of college enrollment in China, has not been fully and effectively transformed into the advantage of human capital defined by occupation. (3) The level of talents concentration in Hong Kong plays an absolute leading role, followed by Macao, Guangzhou, Zhuhai and Shenzhen, while the counties and cities on the periphery of GHM have a low level of talents concentration. In particular, although Foshan and Dongguan have some developed manufacturing industries, their talents concentration level is relatively low. (4) The panel model shows that the service industry has a greater promoting impact on the concentration of highly-skilled workers than that of highly-educated labors, and manufacturing industry does not influence the talents concentration. Higher education plays a less important role in promoting the agglomeration of highly-skilled workers than that of highly-educated labors. High salary helps promote the concentration of highly-educated workers, while it does not boost the concentration of highly-skilled labors. In the new era, it is urgent to promote the cohesion of management systems in Guangdong, Hong Kong and Macao, the industrial transformation and upgrading, and the establishment of high-quality higher education, ultimately, building GHM into an international innovation center.

本文以粤港澳大湾区各城市作为案例,对2000—2017年各城市创新投入强度及绿色经济效率进行了测度,并探讨了不同时期城市创新投入对提升绿色经济效率影响方式和大小的变化。本文主要得出了以下结论：① 大湾区绝大多城市在2000—2017年期间创新投入强度都实现了显著提升,在此期间深圳城市创新投入强度始终处于领先地位,广州、香港创新投入强度增长较慢。② 2000年—2017年间大湾区各城市绿色经济效率的变化情况存在较大差异,处于稳定发展阶段的城市绿色经济效率通常稳步提升,而处于高速增长阶段的城市绿色经济效率多出现波动。③ 从2000年—2017年,大湾区城市绿色经济效率的主要影响因素由规模效应逐渐过渡到创新效应,创新投入强度提升对于提升城市绿色经济效率的贡献度明显上升;④ 创新效应会随着城市发展成熟、产业规模的增大,对提升城市绿色经济效率起到的作用不断增强,应将提升创新投入作为提升城市绿色经济效率最重要的手段。

China's economy has achieved rapid development since the reform and opening up in 1978, but large amounts of resource consumption and pollutant emissions have significantly affected the ecological environment of some regions, therefore attention has been paid to the concept of green development and innovative development. Technological progress and innovation investment is an important way to improve the level of green development. The Guangdong-Hong Kong-Macao Greater Bay Area is a good case study area for examining the contribution of innovation investment to improving green development level in different periods, as this area, one of the regions with the highest degree of openness and the strongest economic vitality in China, has experienced a rapid developing process driven by labor-intensive industries and high-energy consumption industries, and has started the transformation to green development. Taking the cities in the Greater Bay Area as examples, this paper measures the intensity of innovation input and the efficiency of green economy in each city from 2000 to 2017, and discusses the changes in the ways and sizes of the impact of urban innovation input on improving the efficiency of green economy in different periods. This paper mainly comes to the following conclusions. (1) Most cities in the Greater Bay Area have achieved significant improvement in innovation input intensity from 2000 to 2017. During this period, Shenzhen was in the leading position in innovation input intensity, while Guangzhou and Hong Kong had slow growth in innovation input. (2) From 2000 to 2017, there was a big difference in the change of the efficiency of green economy among cities in the Greater Bay Area. The efficiency of green economy in cities in the stage of stable development usually increased steadily, while that in cities in the stage of rapid growth often fluctuated. (3) From 2000 to 2017, the main influencing factors of urban green economy efficiency in the Greater Bay Area gradually transitioned from scale effect to innovation effect, and the contribution of innovation input intensity to the improvement of urban green economy efficiency increased significantly. (4) With the maturity of urban development and the increase of industrial scale, the innovation effect will play a more important role in improving the efficiency of urban green economy.

以粤港澳大湾区58个县区单元的住房平均租金为基本数据,通过“住房租金等级金字塔”构建、租金空间格局展示、空间自相关分析、跨境租金差距对比、售租比研究等方法总结粤港澳大湾区住房租金的空间差异格局与特征。从“租赁需求+城市基本面”的理论视角构建由新增人口、人均住房面积、收入水平、经济水平、产业结构、学历结构组成的租金差异影响因素模型。通过模型对比,采用空间滞后模型测度粤港澳大湾区住房租金的主要影响因素,并基于地理探测器分析其因素的影响强度差异。结果表明,粤港澳大湾区住房租金总体呈现以港澳与珠三角九市之间的境内外差异为主、以广深核心区与其他区域差异为辅的双层次差异格局。跨境租金差异程度最高,广州、深圳、珠海的“售租比”较高;收入水平、经济水平、人均住房面积和产业结构对粤港澳大湾区的住房租金差异有显著影响,其中,收入水平的影响强度最高。

"Livability" is at the core of building a high-quality living circle in the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), and the excessive housing burden costs have become an important obstacle to livability. The differentiation of the rental market is an indispensable and important part of the housing market in the GHMGBA and is inseparable from the creation of a livable life circle. Based on the average housing rent of 58 counties in the GHMGBA, this study summarizes the patterns and characteristics of the spatial differences in housing rents through the construction of a “grading pyramid of housing rents”, and displays the spatial pattern of housing rents through spatial autocorrelation analysis, cross-border rent gap comparison, and price-to-rent ratio analysis. From the theoretical perspective of leasing demand and urban fundamentals, this study constructs a model of factors influencing rent differences, consisting of population growth, per capita housing area, income level, economic level, industrial structure, and education structure. Through model comparison, a spatial lag model was used to measure the main factors influencing the housing rents in the GHMGBA. Based on the geographical detector, the study further analyzed the differences in the intensity of the factors' influence. The results showed that the housing rents in the GHMGBA generally presented a two-level difference pattern. The pattern was dominated by domestic and foreign differences between Hong Kong, Macao, and nine cities in the Pearl River Delta, as well as the differences between the core areas of Guangzhou, Shenzhen, and other regions. The cross-border rent difference was the highest. Higher price-to-rent ratios were observed in Guangzhou, Shenzhen, and Zhuhai. Income level, economic level, per capita housing area, and industrial structure had a significant impact on housing rent differences in the GHMGBA. Among them, income level had the highest impact intensity. This study responds to cross-border regional differences within the country from the perspective of housing rent. Cross-border differences are not only reflected in the population′s economic, income, and institutional levels, but also in the housing rent. The key issue for the regional linkage development of the housing market in the GHMGBA and the construction of a livable and quality living area is the coordinated development across borders.

以广州都市区2013年全部类别银行的1 637个银行网点为基本数据,利用平均最邻近距离、核密度函数、缓冲区分析、空间模式提炼等方法探索广州市银行业的空间布局特征及其类别差异,总结其空间分异模式。结果表明：① 广州市银行业空间不均衡性布局显著,并呈现中心集聚特征;② 不同类型银行的分布特征差异显著。国有商业银行的服务便利性和数量等级高,网点密度大,构成了广州市银行业的主体;③ 广州市银行业布局总体呈现由中心向外围逐渐递减的“圈层+扇形”空间模式。其中,核心商圈是银行业高度集聚区,中心城区密度递减最为显著,近郊区密度最低;④ 不同类型银行的空间密度模式差异显著。国有商业银行、全国性股份制银行和城市商业银行分别呈现由中心到外围非均匀递减的倒“S”型、“L”型和“阶梯型”曲线模式。外资港资银行呈现为核心商圈集聚模式。农村商业银行为均质的“一”字型直线模式。

In this article, based on the average nearest neighbor method, kernel density function, buffer analysis and spatial pattern extraction method, we explore the characteristics of spatial layout and class differences of the banking industry in Guangzhou City and summarize its spatial differentiation model. All of the data are from 1 637 banking outlets distributed in Guangzhou metropolitan areas. The results are shown as follows: 1) The spatial layout of Guangzhou banking shows a significant imbalance and center cluster characteristic. 2) Significant difference is found in distribution characteristics of different types of banks. State-owned commercial bank constitutes the main body of the banking sector in Guangzhou for its convenient service, enormous quantity, high dot density and high grade. 3) The quantity of bank outlets has diminished gradually from the city center to the periphery and shows a ‘circle layer + fan shaped’ space model: the core commercial circle is banking highly concentrated area, the city center is the most significant regional banking density decrease area and the least number of bank is located in the suburb. 4) Different types of banks have different spatial density model. The spatial density of state-owned commercial banks, national joint-stock banks and city commercial banks present non-uniform decline from center to the periphery, and respectively show inverted ‘S’ type, ‘L’ type and ‘ladder’ type curve model. Foreign funded banks and Hong Kong funded banks present a core business agglomeration mode, while the rural commercial bank presents a homogeneous linear model.

利用2004年数字城市数据,研究了广州大都市区产业内部、产业之间的空间关系,比较了广州大都市区中心城区和新城区各种产业的空间格局.将广州大都市区行业分为制造业、批发和运输、零售、生产服务业、房地产业、管理服务、教育、医疗保健及社会扶助和娱乐设施等10类.利用1 km²格网画出了各行业点状密度,并通过分区产业百分比及区位商分析了各产业企业的空间分布,中心城区的主导产业是管理服务、房地产、零售及金融保险等服务行业,而在新城区其主导功能是制造业、批发与运输及生产服务业等.利用平均最邻近距离分析广州大都市区中心城区和新城区各产业内企业之间的空间关系,广州大都市区各产业企业都呈凝聚分布,在中心城区金融行业分布最集中,其次是房地产、生产服务业、娱乐、管理服务等.利用邻近性指数分析了各产业之间的空间关系,发现生产服务业和管理服务业、教育和医疗保健与社会扶助、娱乐和零售等邻近性较大.

This paper studies the spatial pattern of intra-sectoral and inter-sectoral industries in Guangzhou metropolitan area using the digital city data of 2004. The industries of Guangzhou metropolitan area are classified as manufacture, wholesale and transportation, retail, producer service, real estate, administration service, education, health care and social assitance, entertainment and accommodation. The cartographic map of 1km2, percentage and location quotient are applied to study spatial distribution of establishments. In the central urban areas, administration service, real estate, retail, finance and insurance are the major industries while manufacture, wholesale and transportation and producer service are the major industries in the new cities. Average nearest neighbor distance (R) is used to study the intrasectoral spatial pattern. The establishments of Guangzhou are cluster distributed and finance and insurance are concentrated in central urban areas. Proximity index (PI) is used to study intersectoral pattern. The proximity of entertainment and accommodation and retail, education and health care and social assistance, producer service and administration service are more obvious.

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。

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.

以粤港澳大湾区点尺度的发明专利空间数据为基础,通过建立战略性产业与专利IPC分类号之间的联系,提取了大湾区6类主要行业的发明专利,利用核密度分析、标准差尾值检验、熵值法和平均最邻近距离分析等方法,识别粤港澳大湾区多类型技术创新的空间分布特征与差异。结果表明：粤港澳大湾区技术创新的地理格局呈现显著的空间不均衡性,区域尺度形成了广州与深圳2个集聚核心,珠江东西两岸在创新能力上存在较大差距,技术多样性的区位主要分布在大湾区核心城市的核心区；新一代电子信息、先进材料、绿色石化3类产业的创新占大湾区整体的51.67%,不同技术类型的创新在空间上呈现显著分异,主要集中在广州、深圳、东莞、珠海的核心区,其中,新一代电子信息产业创新的空间集聚度最高。

The dynamics of innovation in geographical space is closely related to the regional development trajectory. Evolutionary economic geography points out the path dependence characteristics of regional development, and emphasizes that the direction of regional development is rooted in the original industrial and technological structure characteristics. Therefore, the geographical pattern of technological innovation and the identification of its diversity characteristics are of significance to the realization of regional innovation and the evolution of industrial structure. From the perspective of strategic industry, this paper aims to reveal the geographical pattern and diversity characteristics of technological innovation in strategic industry in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the point scale spatial data of invention patents in the GBA and by means of connecting strategic industry with national economy industry classification code and patent IPC classification code, the invention patents of six major industries in the GBA are extracted, which are the new generation of electronic information industry, automobile industry, green petrochemical industry, new energy industry, advanced material industry and intelligent robot industry. And the nuclear density analysis, standard deviation tail value test, entropy method and average nearest neighbor distance analysis are used to identify the spatial distribution characteristics and differences of multiple types of technological innovation in the GBA. The results show that the geographic pattern of technological innovation in the GBA presents a significant spatial imbalance. At the regional scale, Guangzhou and Shenzhen are the two agglomeration cores. There is a large gap in the innovation ability between the east and west sides of the Pearl River. The accumulation of technological innovation on the east bank is much higher than that on the west bank. The innovation profile line on the east bank has formed two obvious peaks in Guangzhou and Shenzhen. The high value of the innovation profile line on the west bank is mainly concentrated in Guangzhou and Zhuhai, and the peak value is far lower than that on the east bank. The location of technological diversity is mainly distributed in the core areas of the GBA, mainly including Yuexiu District and Tianhe District in Guangzhou, Nanshan District and Futian District in Shenzhen, Binhai District in Dongguan and Xiangzhou District in Zhuhai. And the distribution characteristics of technological innovation data of different industrial types are different. The innovation of the new generation of electronic information industry, advanced materials industry and green petrochemical industry accounts for 51.67% of the total innovation in the GBA. The innovation of different technological types shows obvious spatial differentiation. The innovation agglomeration degree of the new generation electronic information industry is the highest, with the R value of 0.0576. And its observed value of the average nearest neighbor distance between invention patents is 32.03 m, which belongs to the minimum value in the selected analysis industries, showing strong agglomeration characteristics. There are differences in the distribution characteristics of the agglomeration core of the six major industries in the GBA. The common ground is that they have formed the largest two agglomeration cores in the core areas of Guangzhou and Shenzhen, covering Yuexiu-Tianhe area in Guangzhou and Futian-Nanshan-Luohu area in Shenzhen. And the spatial distribution is mainly concentrated in the core areas of Guangzhou, Shenzhen, Dongguan and Zhuhai. Among them, the spatial agglomeration degree of the new generation of electronic information industry innovation is the highest. Agglomeration and multi-dimensional proximity, diversity and path dependence are the main mechanisms for the formation of the geographical pattern of technological innovation in the GBA, and these two key mechanisms promote the formation of current multi-type technological innovation space in the GBA.

粤港澳大湾区是全球高度城市化地区之一。本文引介巨型城市区域理论来审视粤港澳大湾区的空间结构发展,从边界、功能、核心区、区域基础设施4个维度进行分析。未来粤港澳大湾区将呈现四大发展趋势：一是去边界化趋势显著,大湾区内部边界地区将快速发展;二是产业区将成为网络化的功能区块,并取代城市成为地区参与全球竞争的基本单元;三是广佛、港深两大核心区将形成;四是轨道公交化趋势,大湾区共享交通枢纽与公服设施。按发展趋势预测,未来粤港澳大湾区空间结构将形成“两核+若干功能区”的新格局。

The Guangdong-Hong Kong-Macao Greater Bay Area is one of the most urbanized city regions in the world. This study aimed to understand the development of the Guangdong-Hong Kong-Macao Greater Bay Area by applying the theoretical framework of mega-city region. We regarded the Guangdong-Hong Kong-Macao Greater Bay Area as a mega-city region and analyzed its spatial structure from four dimensions: intraregional boundaries, functional network, core area, and regional infrastructure integration. Four development trends were identified: (1) Boundary effect is reducing remarkably and border areas will grow rapidly inside the Greater Bay Area. (2) Industrial clusters become functional areas in a functional network, replacing cities to be the basic unit in global competition. (3) Two core areas can be identified, including the Guangzhou-Foshan core area and the Hong Kong-Shenzhen core area. (4) Rail transit station density tends to increase and a global transportation hub is emerging, which make it convenient for people in the Greater Bay Area to share transportation hub and public service facilities. In the end, the article presents a vision of spatial structure for the Guangdong-Hong Kong-Macao Greater Bay Area, concluding that this mega-city region will form a new structure of 'two cores and functional areas.'

在城市与区域转型发展的背景下,城市网络经历着剧烈的重构,广州和深圳是推动粤港澳大湾区一体化的主导力量,明晰其在网络中的发展定位与动态联系,对于引领区域协调发展具有重要的战略意义。然而,现有城市网络研究较缺乏对多尺度差异与实虚映射关系的关注,因此论文基于微博数据,从流动量、关注度出发,运用社会网络分析,探究了广深在粤港澳大湾区、全国、全球网络中的节点地位与联系特征。结果表明：① 多尺度网络下,广深不同的联系导向塑造了差异化的要素组织能力,广州辐射范围较广、联系相对均衡,深圳联系相对集中、与香港联系尤为紧密;② 实虚网络之间,广深的对外联系存在协同补充、路径依赖效应,且在各尺度下具有不同程度的体现,多重效应的叠加交融推动着区域联系趋向柔性化;③ 基于上述网络格局,广深应立足于不同的联系模式与发展实际,分别发挥区域交通枢纽及创新制度高地的优势,引领打造有序高效的区域网络系统。

City networks have experienced rapid reconstruction in the past decades due to the development of city-regions. In the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou and Shenzhen are two pivotal cities. They play key roles in promoting regional development. Therefore, it is of great significance to identify their influence areas, which can inform urban management and regional planning. Meanwhile, increasing availability of social media data creates opportunities for relevant research. The pervasive presence of location-based services and the associated content make it possible for researchers to gain an unprecedented access to the direct records of human activities and perceptions. Much of existing literature, however, pays little attention to the differences in multi-scale network or to the relationship between the real-world network and virtual network, which are both presented in datasets of this kind. Our research contributes to the literature in both the methodological and the empirical aspects. First, we investigated the node and link characteristics of the influence areas of Guangzhou and Shenzhen by computing social network indicators with a dataset of almost 10 million Sina Microblog records between January 1 and February 6, 2018. Indices of mobility and attention were computed based on characteristics such as consecutive locations, degree centrality, closeness centrality, and average radius of gyration. These indices help to catch the interaction between real and virtual networks. Second, in order to understand inter-city mobility and attention characteristics of Guangzhou and Shenzhen, we mapped city networks of multi-scale, where edge weights denote interaction strengths. Third, our analysis confirmed that the Sina Microblog data exhibit similar statistical properties as other city network datasets. Based on the result of analyses, we argue that Guangzhou had more balanced influence in various directions, representing efficiency in hinterland connection and resource integration. Shenzhen's area of influence was relatively concentrated, with a strong tie with neighboring Hong Kong. Overall, Guangzhou competes better in the mobility network while Shenzhen competes better in the attention network. A complementary relationship was also identified between those two networks. In conclusion, we propose that Guangzhou and Shenzhen took advantage of their respective role as the hubs of regional transportation and innovation as well as what they have already accumulated and their connections with other parts of the world. They should help to build a coordinated and competitive Guangdong-Hong Kong-Macao Greater Bay Area. Our research results offer some insights for policymakers to interpret the geographic dynamics and make relevant decisions in this region. It also provides some references and inputs for analyzing social media data for the research community.