基于大数据的城市群空间演化特征与机制研究

Urban agglomeration, as said in reports of the 19th National Congress of the CPC, has become the main form of new urbanization in China; however, a series of “urban agglomeration diseases” have been induced by the blurring boundary of urban agglomeration, regional spatial barriers and the lack of effective interaction. Nevertheless, it is necessary to scientifically identify the boundary and the characteristics of urban agglomeration, as well as to clarify the underlying mechanisms for spatial concentration and dispersion of urban agglomerations. Big data can monitor the inter-region factor flowing objectively, which compensates for the lack of traditional data and provide beneficial tools for research on urban agglomeration. This study involves the big data on individual-level smartphone’ location, nighttime light data and industrial enterprises database, to investigate a) the evolutive characteristics of the urban agglomerations in China, b) and the mechanism by which urban agglomerations concentrate and disperse, with approaches of spatial analysis, network analysis, expanded NNEG models and spatial econometrics. The study will focus on four scientific problems. The first one is to establish a more objective identification and delineation on the current urban agglomeration in our country with big data. The second one is to characterize the urban agglomeration with a combination of multiple big data and summarize the features and trends in their spatial concentration and dispersion. Third, an updated NNEG model based on heterogeneous micro subjects will be created theoretically, to investigate the main theoretical underpinnings of spatial concentration and dispersion of urban agglomerations, and sequentially to verify the theory with location-based-services data and industrial enterprises database. Fourth, our study will use an expanded spatial econometric interaction model to analyze potential factors which might affect spatial concentration and dispersion of urban agglomerations, and to predict the spatial evolution patterns of urban agglomerations in future, which will provide scientific evidence for policies aiming to promote the development of high quality urban agglomeration.

十九大报告把城市群作为我国城镇发展的主体，而城市群界定模糊、区域空间壁垒和有效互动不足带来了一系列的“城市群病”，有必要科学合理识别城市群的范围和演化特征、理清城市群的集散机制。大数据可以更客观、真实地测度区域要素流动，弥补了传统数据的不足，为城市群研究提供了有力工具。本研究将结合手机位置大数据、夜间灯光数据和微观企业数据，利用空间分析、网络分析、模型演绎以及空间计量等方法，研究我国城市群的演化特征和集散机制。重点聚焦四个问题：一是利用大数据更加客观精准地识别我国城市群发展格局；二是集成多元大数据分析我国城市群的基本特征事实，归纳集散的特点和趋势；三是构建基于异质性微观主体的“新”新经济地理模型，探寻城市群集散的内在机理，并首次利用企业和位置大数据实证其集散行为；四是基于大数据利用空间计量交互模型分析城市群集散的影响因素，预测未来城市群空间演化趋势，为推动城市群高质量发展提供决策参考。

城市群是引领我国新型城镇化和区域协调发展的重要抓手，党的十九大报告把城市群作为我国城镇发展的主体。进入新发展阶段，城市群也迫切需要转向高质量发展，有必要科学合理识别城市群的范围和演化特征、理清城市群的集散机制，不断提升城市群治理能力。大数据可以更客观、真实地测度区域要素流动，弥补了传统数据的不足，为城市群研究提供了有力工具。本研究将人口迁徙、人口密度、夜间灯光、产业园区、交通运输、资源环境等多维大数据进行有效整合，结合微观企业数据和宏观统计数据形成了完善的研究基础数据库。在此基础上，开展了城市群空间识别、演化特征与机制分析、集散影响因素分析等研究。发现：一是利用大数据可更加客观精准地识别我国城市群发展格局，目前形成的比较成熟的城市群有17个。大数据确定的城市群与“十三五”规划城市群既有共性也存在一定的差异，部分规划的城市群现实空间基础尚不具备。二是利用大数据开展了我国城市多中心结构分析，发现我国大多数城市结构仍以单中心为主，多数人口分布在主中心，大城市往往更加分散，城市走向多中心是重要趋势。三是构建基于异质性微观主体的“新”新经济地理模型，探寻城市群集散的内在机理，并首次利用企业和位置大数据实证其集散行为，发现异质性企业的迁移行为决定了区域的集聚和扩散趋势，且负外部性对异质性企业的主动迁移行为有重要影响。四是基于人口迁徙大数据，定量评估了区域房价、交通、环境、教育、包容性等因素对人口集散的影响，发现高房价对人口既存在“吸引效应”又存在“驱赶效应”，且对小城市和中西部城市的作用更为明显，但并未显著地驱使人们“逃离”北上广深；高铁开通显著提高了沿线城市的创新水平且拉大了区域创新差距；地方品质的提升不仅显著促进本地创新，还对周边地区有显著的空间溢出；包容性对城市创新具有显著的正向影响，并通过促进人才集聚，进一步提升城市创新水平。此外，大数据在政策研究上也有广泛的应用前景。