| 流动量与关注度视角下的城市网络结构——以广州、深圳为例 |
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| 引用本文： | 吴炫,杨家文.流动量与关注度视角下的城市网络结构——以广州、深圳为例[J].地理科学进展,2019,38(12):1843-1853. |
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| 作者姓名： | 吴炫 杨家文 |
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| 作者单位： | 北京大学深圳研究生院城市规划与设计学院,广东 深圳 518055 |
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| 基金项目： | 国家自然科学基金项目(51678004) |
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| 摘 要： | 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。
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| 关 键 词： | 城市网络 微博 流动量 关注度 粤港澳大湾区 |
| 收稿时间： | 2019-01-02 |
| 修稿时间： | 2019-03-19 |
City network by mobility and attention indices: A comparison of Guangzhou and Shenzhen |
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| WU Xuan,YANG Jiawen.City network by mobility and attention indices: A comparison of Guangzhou and Shenzhen[J].Progress in Geography,2019,38(12):1843-1853. |
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| Authors: | WU Xuan YANG Jiawen |
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| Institution: | School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, Guangdong, China |
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| Abstract: | 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. |
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| Keywords: | city network Microblog mobility index attention index Guangdong-Hong Kong-Macao Greater Bay Area |
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