Identification of the polycentric urban structure based on multi-source geographic big data
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摘要: 地理大数据的出现为城市空间结构研究提供了新的数据源,如何利用地理大数据识别城市多中心空间结构是目前学术界研究热点。文章提出了一种基于多源地理大数据的城市多中心识别方法,使用基于分水岭的区域分割算法获取研究区内部空间单元,使用两阶段城市中心识别算法识别了城市的主中心与次中心,并对所提方法的识别结果进行了对比验证,研究结果表明: ①基于分水岭的区域分割算法可以有效地挖掘夜间灯光数据的空间特征,获取的基础空间单元可适用于识别城市空间结构; ②微博签到数据可以较好地反映城市人类活动,基于微博签到数据与两阶段城市中心识别方法获取的城市中心与城市规划设定的城市中心基本吻合。文章提出的应用地理大数据识别城市多中心的方法,对拓展地理大数据的应用领域、丰富现有城市空间结构研究的方法具有重要意义。Abstract: The emergence of geographic big data provides a new data source for the study of urban spatial structures. Identifying the polycentric urban structure based on geographic big data is currently a hot research topic in academic communities. This study proposed a method for identifying the polycentric urban structure based on multi-source geographic big data. First, the spatial units in the study area were determined using a region segmentation algorithm based on drainage divides. Then, the urban centers and subcenters were identified using the two-stage algorithm for urban center identification. Finally, the identification results were compared and verified. The results of this study are as follows: ① The region segmentation algorithm based on drainage divides can effectively identify the spatial features of nighttime light data, and the basic spatial units acquired using this algorithm can be used to identify urban spatial structures; ② The urban centers identified based on the Weibo (MicroBlog) check-in data, which can effectively reflect urban human activities, and the two-stage algorithm for urban center identification are roughly consistent with those set in the urban planning. Therefore, the method proposed in this study is of great significance for expanding the application scope of geographic big data and enriching the existing research methods for urban spatial structures.
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Key words:
- nighttime lighting /
- Weibo check-in /
- watershed algorithm /
- spatial unit /
- polycentric structure
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