| 基于多源数据的城市功能区识别及相互作用分析 |
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| 引用本文： | 谷岩岩,焦利民,董婷,王艳东,许刚.基于多源数据的城市功能区识别及相互作用分析[J].武汉大学学报(信息科学版),2018,43(7):1113-1121. |
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| 作者姓名： | 谷岩岩 焦利民 董婷 王艳东 许刚 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉, 430079 |
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| 基金项目： | 国家自然科学基金41571385国家自然科学基金41271399测绘地理信息公益性行业科研专项经费201512015国家重点研发计划2016YFB0501400 |
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| 摘 要： | 随着经济的快速发展，城市内部空间结构不断优化。识别城市功能区空间分布及其相互作用规律，对于把握城市空间结构以及制定科学合理的规划具有重要意义。采用重尾打断分类法和核密度聚类法对兴趣点（points of interest，POI）进行分析，识别城市功能区，并结合出租车轨迹数据进行时空挖掘，定量分析典型城市功能区交通吸引规律及其相互作用强度和方向。以北京市五环内主城区为例进行分析，可得:①该方法可以识别典型功能区西单、国贸、中关村是以商业为主的混合城市功能区，望京是以居住为主的混合功能区，且居民通勤出行特征明显；②国贸对自身的引力较强（39.4%），说明国贸区域城市功能更加齐全；③典型功能区对居民出行距离范围内的区域吸引力随着距离的增加而减弱，符合经验认知和地理空间衰减规律。结果表明，利用POI和移动大数据采用重尾打断分类法和核密度聚类法进行城市功能区识别与分析是可行和有效的。
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| 关 键 词： | POI 出租车轨迹数据 城市功能区 空间结构 多源数据 |
| 收稿时间： | 2016-09-05 |
Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data |
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| Institution: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China3.Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China |
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| Abstract: | With the rapid development of economy, urban internal space structure has been optimized significantly. It is of great significance to identify the spatial distribution and interaction rules of diffe-rent functional regions (DFR) for urban structure analysis and rational planning. We identify the spatial distribution of DFR by analyzing points of interest(POI) data based on kernel density estimation and head/tail breaks. On this basis, we analyze the spatio-temporal discipline of attraction and mutual relationship between typical DFR based on taxi trajectory data. Inside the 5th ring road of urban region of Beijing, the study reveals that:①Typical DFR Xidan, Guomao, Zhongguancun are business-oriented districts, Wangjing is a residential district with a significantly commuting characteristic. ②Guomao has the robust gravity (39.4%) on itself, which indicates that Guomao has more comprehensive urban functions. ③The attraction of DFR within the scope of resident trip distance decreases with the increase of distance, which conforms to the experience cognition and geographic spatial attenuation law. The results show that using kernel density estimation and head/tail breaks to analyze POI data and taxi trajectory data and identify the spatial distribution of DFR is reasonable and effective. |
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