| 基于POI数据的郑东新区服务业空间聚类研究 |
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| 引用本文： | 李江苏,梁燕,王晓蕊.基于POI数据的郑东新区服务业空间聚类研究[J].地理研究,2018,37(1):145-157. |
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| 作者姓名： | 李江苏 梁燕 王晓蕊 |
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| 作者单位： | 1. 河南大学黄河文明与可持续发展研究中心暨黄河文明传承与现代文明建设河南省协同创新中心,开封 4750012. 河南大学环境与规划学院,开封 475001 |
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| 基金项目： | 国家自然科学基金项目（41401129,41430637,41401133）;教育部人文社会研究规划基金项目（13YJA-ZH042）;河南省高等学校重点科研项目（17A790012）;河南省博士后科研项目（2015039） |
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| 摘 要： | 探讨城市新区的服务业空间格局,对城市新区规划和服务业空间布局的优化具有重要指导意义。采用POI数据对郑东新区服务业的总体、分行业空间布局进行聚类,结果显示：① 在总体上,聚类呈现“414”的空间体系,各聚类所在区域的通达性较好;服务业在功能区内部聚集和跨越功能区聚集并存;噪声点分布零散,局部出现了服务业聚集的潜力区域;空间临近效应、行政力量带动和市场导向作用导致服务业空间极化特征明显。② 从分行业来看,部分行业的重要空间节点分布存在一定差异,CBD核心区和商住物流区成为各行业空间节点的分布区域;部分行业的空间节点与功能区的产业定位存在吻合与错位特征。最后,从规划视角提出了郑东新区不同功能区产业结构优化的方向。
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| 关 键 词： | POI数据 DBSCAN算法 服务业 郑东新区 功能区 |
| 收稿时间： | 2017-07-02 |
| 修稿时间： | 2017-11-21 |
Spatial clustering analysis of service industries in Zhengdong New District based on POI data |
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| Jiangsu LI,Yan LIANG,Xiaorui WANG.Spatial clustering analysis of service industries in Zhengdong New District based on POI data[J].Geographical Research,2018,37(1):145-157. |
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| Authors: | Jiangsu LI Yan LIANG Xiaorui WANG |
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| Institution: | 1. Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, Henan, China;2. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China |
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| Abstract: | Exploring the spatial layout of service industries in new urban districts is significant for guiding the planning of these districts and optimizing the spatial layout of service industries. This study examined the overall and sub-industry spatial distribution of service industries in Zhengdong New District, Zhengzhou, Henan province, China. An analysis was carried out using points of interest (POI) data (Big Data closely related to human-economic geography) and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in MATLAB and ArcGIS. The main findings from this research were: (1) In terms of the overall spatial layout, the clusters showed an obvious "414" spatial hierarchy and the regions with these clusters had better accessibility. Most clusters were located in only one functional area, but some others were located in two or more functional areas. The distribution of noise points was relatively scattered and isolated from the potential regions of service industry agglomeration in future. The effect of spatial proximity, administrative drivers and market orientation led to the apparent spatial polarization of service industries, which were centered in the lower left corner of a diagonal from the northwest to the southeast of Zhengdong New District. Future urban planning should target the construction of 'multi-clustering centers' to avoid excessive clustering of service industries (especially low-level service industries) in the CBD and residential/commercial logistics areas, and to avoid increasing the pressure on population, transportation, resources and the environment in these two functional areas. (2) The analysis by sub-industry showed that some industries had differences in the distribution of spatial nodes, although the CBD and residential/commercial logistics areas were the gathering points of spatial nodes for all industries. Some industries showed features of coincidence and dislocation between the spatial nodes and the location of functional areas. From the planning point of view, the positioning of each functional area should be clearly differentiated, the overlap of service industries should be avoided, and the location of each sub-industry should be considered carefully. In the end, the paper proposed the optimization of the industrial structure of functional areas, which provides the basis for improving the spatial structure of service industries in Zhengdong New District. |
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| Keywords: | POI data DBSCAN algorithm service industry Zhengdong New District functional area |
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