| 基于潜在语义信息的城市功能区识别————广州市浮动车GPS时空数据挖掘 |
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| 引用本文： | 陈世莉,陶海燕,李旭亮,卓莉.基于潜在语义信息的城市功能区识别————广州市浮动车GPS时空数据挖掘[J].地理学报,2016,71(3):471-483. |
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| 作者姓名： | 陈世莉 陶海燕 李旭亮 卓莉 |
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| 作者单位： | 1. 中山大学地理科学与规划学院 综合地理信息研究中心,广州 5102752. 广东省城市化与地理环境空间模拟重点实验室,广州 5102753. 中山大学城市化研究院,广州 510275 |
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| 基金项目： | 广国家高技术发展计划(863) (2013AA122302);广东省自然科学基金项目(S2013010012554);国家自然科学基金项目(41371499, 41271138) |
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| 摘 要： | 随着中国城市化进程的不断推进和深入,城市内部空间结构正发生不断的变化.城市内部形成的不同功能区标识研究,对城市结构理论以及政策制定,资源配置等方面具有非常重要的意义.这些不同的功能区包括住宅区,工业区,教育区以及办公区等.本文以大数据为依托,重点研究城市功能区的特点和分布状态,选取广州市6个区为样本,以最新道路网络为分割依据把研究样本分为439个区域.对历时一周的海量浮动车(GPS)数据以及兴趣点数据采用时空语义挖掘方法,建立潜在的狄利克雷模型(LDA)以及狄利克雷多项式回归模型(DMR);通过OPTICS聚类方法对不同模型的结果进行聚类,进而利用POI类别密度,居民出行特征等方法进行分区结果识别.同时,参考百度地图的地理信息,将研究得到的广州市功能分区结果与广州市城镇用地现状图,居民日常出行特征进行对比验证分析.研究表明,该方法基本能识别出具明显特征的城市功能区,如成熟居住区,科教文化区,商业娱乐区,开发区等.识别出的广州市不同类型的功能区呈现了以居住区和商业区为主导,其他类型功能区围绕其展开的特点.研究证明,利用大规模,高质量的个体时空数据开展人们移动行为和日常活动组织及社会空间的研究,能从一个新的视角揭示城市功能区的形成及其机制.
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| 关 键 词： | 主题模型 功能区 地理大数据 GPS数据 兴趣点 广州 |
| 收稿时间： | 2015-07-30 |
| 修稿时间： | 2015-11-27 |
Discovering urban functional regions using latent semantic information: Spatiotemporal data mining of floating cars GPS data of Guangzhou |
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| Shili CHEN,Haiyan TAO,Xuliang LI,Li ZHUO.Discovering urban functional regions using latent semantic information: Spatiotemporal data mining of floating cars GPS data of Guangzhou[J].Acta Geographica Sinica,2016,71(3):471-483. |
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| Authors: | Shili CHEN Haiyan TAO Xuliang LI Li ZHUO |
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| Institution: | 1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China2. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China3. Urbanization Institute of Sun Yat-sen University, Guangzhou 510275, China |
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| Abstract: | China has been experiencing rapid urbanization at an unprecedented rate and as a result, urban internal space structure has evolved significantly. It is of great significance to label different functional regions (DFR) inside a city for urban structure analysis, policy making, and resource allocation. These DFRs include residential district, industrial district, education district, and the administration district. This paper explored the characteristics and distribution of urban functional regions based on big geographic data. With the latest road network data, the study area (i.e., 6 districts of Guangzhou city in Guangdong Province, China) was partitioned into 439 segments. By applying the employment of spatial and temporal semantic mining method to the one-week massive floating cars GPS data and the point of interest data, we developed a Latent Dirichlet Allocation (LDA) and Dirichlet Multinomial Regression (DMR) model. Moreover, OPTICS clustering method was employed to process the results of LDA and DMR to identify different functional zones. Meanwhile, status map of Guangzhou urban planning, and resident travel characteristics were used to verify the verification of mentioned results. The results show that this method can identify the obvious characteristics of urban functional areas, such as mature residential area, science and education culture area, commercial area, and development zone. The results also show that residential and commercial areas are dominant DFRs in Guangzhou city, which are surrounded by other types of functional regions. This paper brings a new perspective on using large-scale and high quality individual space-time data to study human migration and daily activities, as well as to explore social space to unveil the formation and mechanism of urban functional zones. |
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| Keywords: | latent dirichlet allocation functional regions big geographic data GPS data point of interest Guangzhou |
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