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| 基于手机信令数据的北京市职住空间分布格局及匹配特征 | |
| 引用本文： | 王蓓,王良,刘艳华,杨波,黄晓春,杨明.基于手机信令数据的北京市职住空间分布格局及匹配特征[J].地理科学进展,2020,39(12):2028-2042. |
| 作者姓名： | 王蓓 王良 刘艳华 杨波 黄晓春 杨明 |
| 作者单位： | 1.北京市城市规划设计研究院,北京 100045 2.浙江财经大学经济学院,杭州 310018 3.北京市社会科学院,北京 100101 |
| 基金项目： | 国家自然科学基金项目(41601168);国家自然科学基金项目(41601122);国家自然科学基金项目(51878052);北京市自然科学基金项目(9182007) |
| 摘 要： | 职住空间作为城市系统最重要的组成部分,直接影响了城市的形态结构、居民的行为体验以及社会的和谐宜居,长期以来受到城市研究者的关注和重视。论文利用覆盖北京全市域并持续1个月的1亿多条手机信令数据,基于DBSCAN的聚类方法,通过OD定向联系,识别出同时具备居住—就业关系特征的职住空间。在此基础上,针对北京市辖区、环路、街道乡镇等不同空间尺度,综合运用空间错位指数、职住偏离度、职住分离率、通勤流动率等计算方法,研究北京职住空间分布格局及匹配特征。研究发现：① 北京市居住空间呈现大分散、小集聚特征,就业空间呈现大集聚、小分散特征;② 基于各个空间尺度、不同测度方法的分析结果均表明,职住空间的不匹配程度呈现出由中心城区向外围逐渐降低的态势,但基于街道乡镇尺度呈现出由内向外更细化的就业集聚—居住集聚—二者均衡的三段式变化特征;③ 无论是就业空间高度集聚导致的非集聚区就业岗位数量不足,还是包括就业高集聚区在内大量区域出现的双向通勤现象,均说明居住功能和就业功能空间重组的必要性。 |
| 关 键 词： | 职住空间 空间匹配特征 手机信令 北京 |
| 收稿时间： | 2019-11-25 |
| 修稿时间： | 2020-04-22 |
Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data |
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| WANG Bei,WANG Liang,LIU Yanhua,YANG Bo,HUANG Xiaochun,YANG Ming.Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data[J].Progress in Geography,2020,39(12):2028-2042. | |
| Authors: | WANG Bei WANG Liang LIU Yanhua YANG Bo HUANG Xiaochun YANG Ming |
| Institution: | 1. Beijing Municipal Institute of City Planning & Design, Beijing 100045, China 2. School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China 3. Beijing Academy of Social Sciences, Beijing 100101, China |
| Abstract: | As the most important parts of urban systems, jobs and housing spaces and their balance directly affect the spatial structure of cities, the behavior and experience of the residents, and the harmony and livability of the society. This study used more than 100 million records of mobile phone signaling data, covering the whole city of Beijing and over a period of one month, to identify the jobs-housing spaces by targeting the origin-destination (OD) oriented connections applying the density-based spatial clustering of applications with noise (DBSCAN) method. Furthermore, this study explored the spatial distribution pattern and matching characteristics of Beijing's jobs and housing spaces from different spatial scales by using various measurement methods of jobs-housing balance. The spatial scales for analysis cover the whole city, ring roads, districts, and residential community and town and townships, and the methods applied include the spatial mismatch index, deviation degree, commuting flow rate, and so on. The results show that: 1) Mainly influenced by the spatial layout of the Beijing Master Plan, Beijing's housing space is characterized by dispersion at the large scale and agglomeration at the small scale, showing a pattern of scattered groupings. In contrast, its working space presents features of agglomeration at the large scale and dispersion at the small scale, retaining a significant single-centered layout. 2) Although working in local areas is the first choice for people at both the ring road scale and the district scale, there are still a great number of people works outside their residential areas. The degree of jobs-housing mismatch gradually decreases from the central city to the periphery no matter which method was adopted or at which scale. At the residential community and town and township scale, however, a more detailed feature of three-zones, with job agglomeration inside, housing agglomeration in between, and balanced distribution outside, was observed. 3) Both the general lack of jobs in certain areas caused by the high concentration of working space and the two-way commuting phenomenon in the majority of the areas caused by the high spatial concentration of jobs indicate the necessity of spatial reorganization of residential function and employment function. Specifically, for the regions with a high proportion of two-way commuting flow between them, such as Chaoyang-Changping, Tongzhou-Daxing, Haidian-Changping, Mentougou-Shijingshan, and Chaoyang-Tongzhou, further in-depth investigation should be conducted to find out the reasons for its formation and then possible industrial adjustment or functional reconstruction from the city level should be coordinated. Relying on big data, on the one hand, the job preference and demand of local residents can be identified, so the types and number of jobs in each region could be adjusted accordingly; on the other hand, the proper locations for new job and residential centers may be identified to help rearrange the land use of the whole city. |
| Keywords: | jobs-housing space jobs-housing mismatch mobile phone signaling data Beijing |
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