| 基于抖音粉丝量的中国城市网络关注度空间差异及其影响因素 |
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| 引用本文： | 丁志伟,马芳芳,张改素.基于抖音粉丝量的中国城市网络关注度空间差异及其影响因素[J].地理研究,2022,41(9):2548-2567. |
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| 作者姓名： | 丁志伟 马芳芳 张改素 |
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| 作者单位： | 1.河南大学地理与环境学院,开封 4750042.河南大学黄河中下游数字地理技术教育部重点实验室,开封 4750043.河南大学区域发展与规划研究中心,开封 4750044.河南大学环境与规划国家级实验教学示范中心,开封 475004 |
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| 基金项目： | 国家自然科学基金项目(41701130);河南省高校科技创新人才支持计划项目（人文社科类：2021-CX-016(2021-CX-016);河南省哲学社会科学规划年度项目(2020BJJ018);河南大学本科教学改革研究与实践项目(YB-JFZX-08);河南大学本科教学改革研究与实践项目(HDXJJG2021-034);河南大学研究生教育教学改革研究与实践项目(YJSJG2022XJ028);河南大学研究生培养创新与质量提升行动计划项目资助(SYLKC2022003) |
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| 摘 要： | 基于抖音粉丝量数据,运用位序-规模法则、核密度估计、领域划分与模式组合等方法,对中国城市网络关注的空间差异进行分析,并对比了其与传统百度指数的差异。研究发现：① 关注度排名靠前的城市分别是北京、上海、广州、深圳、成都、杭州、重庆、西安、天津、南京等,并形成了长三角、珠三角、京津三大核心集聚区,反映出核心经济发展区的网络优势度。而中西部地区城市的关注度普遍不高,仅在成渝、中原、长江中游等地区形成微弱的集聚中心。② 从位序-规模法则看,拟合曲线偏离理想状态且q值大于1.3,反映出高关注城市在空间上的强影响作用并表现出一定的网络空间集聚效应。从四大分区看,东北和西部地区与整体类似,东部地区的高等级集聚效应进一步强化,而中部则较符合理想形态。③ 从领域划分与地域模式看,北京、上海、广州等高关注城市的核心领域是探店、旅游、美食、街拍、同城,方言、旅游类话题亦有较大的关注。中西部地区核心城市除了在特色美食、探店、街拍与东部地区有些类似外,还表现出房产、地铁等方面的特色,从侧面反映出该区域在城镇化建设、产业转型等方面面临的一些现实问题。从地域模式看,长三角属于多中心网络化,京津冀、珠三角、成渝、长江中游属于双中心点轴状,关中、中原属于单中心放射状,其余城市群则属于单中心或无中心离散状。④ 与百度指数对比看,抖音粉丝量在高水平区的集聚程度高,而在中西部城市群地区的集聚效应不明显。⑤ 从影响因素看,除了与经济发展水平尤其是现代服务业水平、信息化水平、交通物流保障相关外,与城市的创新性、高素质人才或高学历网民的活跃性参与、数字化推广、专业化运营相关性较大。
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| 关 键 词： | 抖音粉丝量 城市网络关注 空间差异 影响因素 中国 |
| 收稿时间： | 2022-02-21 |
Spatial differences and influencing factors of urban network attention by Douyin fans in China |
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| DING Zhiwei,MA Fangfang,ZHANG Gaisu.Spatial differences and influencing factors of urban network attention by Douyin fans in China[J].Geographical Research,2022,41(9):2548-2567. |
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| Authors: | DING Zhiwei MA Fangfang ZHANG Gaisu |
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| Institution: | 1. College of Geography and Environmental Science, Henan University, Kaifeng 475004, Henan, China2. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of Education, Henan University, Kaifeng 475004, Henan, China3. Research Center of Regional Development and Planning, Henan University, Kaifeng 475004, Henan, China4. National Demonstration Center for Environment and Planning, Henan University, Kaifeng 475004, Henan, China |
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| Abstract: | Based on the data of Douyin fans and Baidu index, we adopted the rank-size model, kernel density estimation, field division, and regional combination to examine the spatial differences of urban network attention, and carried out a comparative analysis. Some conclusions can be drawn as follows. (1) Municipalities and provincial capitals, such as Beijing, Shanghai, Guangzhou, Shenzhen, Chengdu, Hangzhou, Chongqing, Xi'an, Tianjin, and Nanjing, are cities that draw high attention, which are in accordance with some agglomeration regions such as the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin region. However, cities with low attention are concentrated in agglomeration regions with small and low-density population, such as Chengdu-Chongqing, Central China Plains, and middle and lower reaches of the Yangtze River. (2) From the perspective of the rank-size model, the fitting curve deviates from the ideal state and the q-value is greater than 1.3, reflecting the strong spatial agglomeration effect of high-grade cities in network space. Specifically, the curves of northeastern and western China are similar; that of central China is close to the ideal form; and the high-level agglomeration effect in eastern China is further strengthened according to the data. (3) From the perspective of field division and regional model, the core fields of high attention cities such as Beijing, Shanghai, and Guangzhou are shop exploration, tourism, food, street photography, and entertainment. As for central and western China, the main attractions are real estate, and subways, as well as special food, shop exploration, and street photography. (4) From the perspective of spatial combination, the Yangtze River Delta belongs to multi-center network type, Beijing-Tianjin-Hebei region, Pearl River Delta, Chengdu-Chongqing region, and the middle reaches of the Yangtze River belong to double-center point-axis type, Guanzhong and Zhongyuan urban agglomerations belong to single-center radiation type, and other urban agglomerations belong to single-center or has no-center type. (5) Based on the comparison of Baidu index, cities with high-level network attention are concentrated in eastern China, while the network effect in central and western China is not obvious. (6) In terms of influencing factors, the formation of all these patterns is closely related with the innovation level, digital promotion, and specialization operation of high-quality talents or highly-educated netizens, in addition to economic level with regards to the levels of the modern service industry, informatization process, and transportation. |
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| Keywords: | Douyin fans urban network attention spatial differences influencing factors China |
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