上海市人口分布与空间活动的动态特征研究——基于手机信令数据的探索

doi:10.11821/dlyj201705013

摘要/Abstract

摘要：

对城市人口空间分布的动态把握是了解人口活动规律、认识城市空间结构、配置城市基础设施和公共服务设施及制订城市公共安全应急保障方案的重要依据。由于目前国内缺少系统的人口动态变化统计数据,城市内部层面的人口空间分布和活动的动态特征方面的相关研究难以开展,研究成果较为有限。移动电话是目前普及率最高的通讯终端设备,其用户的动态分布信息可以准确地反应整个城市人口的空间分布与活动的动态特征。利用手机信令数据,以上海市为例,构建“人口—时间—行为”关系的人口空间动态分析框架,分析上海市人口分布和活动的动态特征。结果表明：上海整体人口密度呈单中心的圈层空间分布结构,昼夜空间分布经历“白天向中心集聚、夜晚向郊区分散”的流动过程;人的各类活动（如通勤、消费休闲）会产生人口空间分布的动态变化,职住关系的不匹配和活动对中心的高度依赖使得人口的空间分布不均,形成向心流动模式。消费休闲行为对中心城区的依赖度明显高于就业活动,且集中体现在紧邻中心城区的外围近郊呈圈层分布。

关键词: 手机信令数据, 昼夜人口, 居住与就业, 土地利用强度, 上海

Abstract:

Analysis of the dynamic characteristics of Shanghai's population distribution is an important basis for recognizing people's behaviors, allocating urban infrastructures, and making safety emergency plans. Be short of statistical data of temporal and spatial dynamic distribution of population, research on this topic is limited in China. Due to cell phone is the most popular communication terminal equipment, the distribution of cell phone users is able to reflect the distribution of population accurately. Using datasets of cell phone signaling records from Shanghai, this study builds a framework based on the relationship among population, time, and behavior to analyze dynamic characteristics of Shanghai's population distribution. The results show that: (1) there is a single center where the density of population at daytime and nighttime is the highest; (2) people gather in the center at daytime and flow to the suburbs at nighttime; (3) different types of people's behavior result in dynamic changes of population distribution; (4) spatial mismatch between employment and place of residence, and the dependence on city center cause a large number of people flow to city center; (5) the degree of dependence of leisure consumption behavior on city center is obviously higher than that of employment, especially in the ring areas adjacent to city center.

Key words: cell phone signaling data, population at daytime and nighttime, residence and employment, land use intensity, Shanghai

钟炜菁, 王德, 谢栋灿, 晏龙旭. 上海市人口分布与空间活动的动态特征研究——基于手机信令数据的探索[J]. 地理研究, 2017, 36(5): 972-984.

Weijing ZHONG, De WANG, Dongcan XIE, Longxu YAN. Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J]. GEOGRAPHICAL RESEARCH, 2017, 36(5): 972-984.

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参考文献 37

[1]	王露, 杨艳昭, 封志明, 等. 基于分县尺度的2020-2030年中国未来人口分布. 地理研究, 2014, 33(2): 310-322. doi: 10.11821/dlyj201402010
	[Wang Lu, Yang Yanzhao, Feng Zhiming, et al.Prediction of China's population in 2020 and 2030 on county scale. Geographical Research, 2014, 33(2): 310-322.] doi: 10.11821/dlyj201402010
[2]	秦贤宏, 魏也华, 陈雯, 等. 南京都市区人口空间扩张与多中心化. 地理研究, 2013, 32(4): 711-719. doi: 10.11821/yj2013040014
	[Qin Xianhong, Wei Yehua, Chen Wen, et al.Population expansion and polycentric development of Nanjing city in a period of hyper-growth. Geographical Research, 2013, 32(4): 711-719.] doi: 10.11821/yj2013040014
[3]	杨传开, 宁越敏. 中国省际人口迁移格局演变及其对城镇化发展的影响. 地理研究, 2015, 34(8): 1492-1506. doi: 10.11821/dlyj201508008
	[Yang Chuankai, Ning Yuemin.Evolution of spatial pattern of inter-provincial migration and its impacts on urbanization in China. Geographical Research, 2015, 34(8): 1492-1506.] doi: 10.11821/dlyj201508008
[4]	饶烨, 宋金平, 于伟. 北京都市区人口增长的空间规律与机理. 地理研究, 2015, 34(1): 149-156.
	[Rao Ye, Song Jinping, Yu Wei.Spatial pattern and mechanism of population growth in metropolitan Beijing. Geographical Research, 2015, 34(1): 149-156.]
[5]	边雪, 陈昊宇, 曹广忠. 基于人口、产业和用地结构关系的城镇化模式类型及演进特征: 以长三角地区为例. 地理研究, 2013, 32(12): 2281-2291. doi: 10.11821/dlyj201312010
	[Bian Xue, Chen Haoyu, Cao Guangzhong.Patterns of regional urbanization and its implications: An empirical study of the Yangtze River Delta region. Geographical Research, 2013, 32(12): 2281-2291.] doi: 10.11821/dlyj201312010
[6]	李扬, 刘慧, 汤青. 1985-2010年中国省际人口迁移时空格局特征. 地理研究, 2015, 34(6): 1135-1148. doi: 10.11821/dlyj201506012
	[Li Yang, Liu Hui, Tang Qing.Spatial-temporal patterns of China's interprovincial migration during 1985-2010. Geographical Research, 2015, 34(6): 1135-1148.] doi: 10.11821/dlyj201506012
[7]	潘倩, 金晓斌, 周寅康. 近300年来中国人口变化及时空分布格局. 地理研究, 2013, 32(7): 1291-1302.
	[Pan Qian, Jin Xiaobin, Zhou Yinkang.Population change and spatiotemporal distribution of China in recent 300 years. Geographical Research, 2013, 32(7): 1291-1302.]
[8]	Foley D L.Urban daytime population: A field for demographic-ecological analysis. Social Forces, 1954, 32(4): 323-330. doi: 10.2307/2574113
[9]	Foley D L.The daily movement of population into central business districts. American Sociological Review, 1952, 17(5): 538-543. doi: 10.2307/2088219
[10]	Akkerman A.The urban household pattern of daytime population change. The Annals of Regional Science, 1995, 29(1): 1-16. doi: 10.1007/BF01580360 pmid: 12319441
[11]	Roddis S M, Richardson A J. Construction of daytime activity profiles from household travel survey data. In: Transportation Research Record 1625, TRB. National Research Council, 1998: 102-108. doi: 10.3141/1625-13
[12]	Sleeter R, Wood N. Estimating daytime and nighttime population density for coastal communities in Oregon. In: Proceedings of 44th Urban and Regional Information Systems Association Annual Conference. Columbia, British, 44th Urban and Regional Information Systems Association Annual Conference, 2006: 906-920.
[13]	Kavanaugh P.A method for estimating daytime population by small area geography. Proceedings of 18th Urban and Regional Information Systems Association Conference. Edmonton, Alberta, Canada, 18th Urban and Regional Information Systems Association Conference, 1990: 150-164.
[14]	戚伟, 李颖, 刘盛和, 等. 城市昼夜人口空间分布的估算及其特征: 以北京市海淀区为例. 地理学报, 2013, 68(10): 1344-1356.
	[Qi Wei, Li Ying, Liu Shenghe, et al.Estimation of urban population at daytime and nighttime and analyses of their spatial pattern: A case study of Haidian District, Beijing. Acta Geographica Sinica, 2013, 68(10): 1344-1356.]
[15]	赵晔琴. 国外大都市“白天人口”研究及其对我国的启示. 南方人口, 2010, 25(6): 24-31. doi: 10.3969/j.issn.1004-1613.2010.06.004
	[Zhao Yeqin.International studies on "daytime population" and its implications for China. South China Population, 2010, 25(6): 24-31.] doi: 10.3969/j.issn.1004-1613.2010.06.004
[16]	毛夏, 徐蓉蓉, 李新硕, 等. 深圳市人口分布的细网格动态特征. 地理学报, 2010, 65(4): 443-453. doi: 10.11821/xb201004006
	[Mao Xia, Xu Rongrong, Li Xinshuo, et al.Fine grid dynamic features of population distribution in Shenzhen. Acta Geographica Sinica, 2010, 65(4): 443-453.] doi: 10.11821/xb201004006
[17]	Liu Yu, Liu Xi, Gao Song, et al.Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 2015, 105(3): 512-530. doi: 10.1080/00045608.2015.1018773
[18]	甄峰, 王波. “大数据”热潮下人文地理学研究的再思考. 地理研究, 2015, 34(5): 803-811. doi: 10.11821/dlyj201505001
	[Zhen Feng, Wang Bo.Rethinking human geography in the age of big data. Geographical Research, 2015, 34(5): 803-811.] doi: 10.11821/dlyj201505001
[19]	吴志峰, 柴彦威, 党安荣, 等. 地理学碰上“大数据”: 热反应与冷思考. 地理研究, 2015, 34(12): 2207-2221. doi: 10.11821/dlyj201512001
	[Wu Zhifeng, Chai Yanwei, Dang Anrong, et al.Geography interact with big data: Dialogue and reflection. Geographical Research, 2015, 34(12): 2207-2221.] doi: 10.11821/dlyj201512001
[20]	甄峰, 秦萧, 席广亮. 信息时代的地理学与人文地理学创新. 地理科学, 2015, 35(1): 11-18.
	[Zhen Feng, Qin Xiao, Xi Guangliang.The innovation of geography and human geography in the information era. Scientia Geographica Sinica, 2015, 35(1): 11-18.]
[21]	杨振山, 龙瀛, Douay Nicolas.大数据对人文—经济地理学研究的促进与局限. 地理科学进展, 2015, 34(4): 410-417. doi: 10.11820/dlkxjz.2015.04.002
	[Yang Zhenshan, Long Ying, Douay Nicolas.Opportunities and limitations of big data applications to human and economic geography: The state of the art. Progress in Geography, 2015, 34(4): 410-417.] doi: 10.11820/dlkxjz.2015.04.002
[22]	郭璨, 甄峰, 朱寿佳. 智能手机定位数据应用于城市研究的进展与展望. 人文地理, 2014, 29(6): 18-23.
	[Guo Can, Zhen Feng, Zhu Shoujia.Progress and prospect of the application of smart phone LBS data in urban researches. Human Geography, 2014, 29(6): 18-23.]
[23]	刘瑜, 肖昱, 高松, 等. 基于位置感知设备的人类移动研究综述. 地理与地理信息科学, 2011, 27(4): 8-13, 2, 31.
	[Liu Yu, Xiao Yu, Gao Song, et al. A review of human mobility research based on location aware devices. Geography and Geo-Information Science, 2011, 27(4): 8-13, 2, 31.]
[24]	钮心毅, 丁亮, 宋小冬. 基于手机数据识别上海中心城的城市空间结构. 城市规划学刊, 2014, (6): 61-67.
	[Niu Xinyi, Ding Liang, Song Xiaodong.Understanding urban spatial structure of Shanghai central city based on mobile phone data. Urban Planning Forum, 2014, (6): 61-67.]
[25]	Calabrese F, Colonna M, Lovisolo P, et al.Real-time urban monitoring using cell phones: A case study in Rome. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 141-151. doi: 10.1109/TITS.2010.2074196
[26]	Reades Jonathan, Calabrese Francesco, Ratti Carlo.Eigenplaces: Analysing cities using the space-time structure of the mobile phone network. Environment and Planning B: Planning and Design, 2009, 36(5): 824-836. doi: 10.1068/b34133t
[27]	王德, 钟炜菁, 谢栋灿, 等. 手机信令数据在城市建成环境评价中的应用: 以上海市宝山区为例. 城市规划学刊, 2015, (5): 82-90.
	[Wang De, Zhong Weijing, Xie Dongcan, et al.The application of cell phone signaling data in the assessment of urban built environment: A case study of Baoshan district in Shanghai. Urban Planning Forum, 2015, (5): 82-90.]
[28]	钮心毅, 丁亮. 利用手机数据分析上海市域的职住空间关系: 若干结论和讨论. 上海城市规划, 2015, (2): 39-43.
	[Niu Xinyi, Ding Liang.Analyzing Job-housing spatial relationship in Shanghai using mobile phone data: Some conclusions and discussions. Shanghai Urban Planning Review, 2015, (2): 39-43.]
[29]	Yuan Yihong, Raubal Martin, Liu Yu.Correlating mobile phone usage and travel behavior: A case study of Harbin, China. Computers, Environment and Urban Systems, 2012, 36(2): 118-130. doi: 10.1016/j.compenvurbsys.2011.07.003
[30]	Ahas Rein, Aasa Anto, Silm Siiri, et al.Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data. Transportation Research Part C: Emerging Technologies, 2010, 18(1): 45-54. doi: 10.1016/j.trc.2009.04.011
[31]	丁亮, 钮心毅, 宋小冬. 利用手机数据识别上海中心城的通勤区. 城市规划, 2015, 39(9): 100-106. doi: 10.11819/cpr20150917a
	[Ding Liang, Niu Xinyi, Song Xiaodong.Identifying the commuting area of Shanghai central city using mobile phone data. City Planning Review, 2015, 39(9): 100-106.] doi: 10.11819/cpr20150917a
[32]	王德, 王灿, 谢栋灿, 等. 基于手机信令数据的上海市不同等级商业中心商圈的比较: 以南京东路、五角场、鞍山路为例. 城市规划学刊, 2015, (3): 51-61.
	[Wang De, Wang Can, Xie Dongcan, et al.Comparison of retail trade areas of retail centers with different hierarchical levels: A case study of East Nanjing Road, Wujiaochang, Anshan Road in Shanghai. Urban Planning Forum, 2015, (3): 51-61.]
[33]	Calabrese F, Diao Mi, Lorenzo Gi D, et al.Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 2013, 26: 301-313. doi: 10.1016/j.trc.2012.09.009
[34]	Deville P, Linard C, Martin S, et al.Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 2014, 111(45): 15888-15893. doi: 10.1073/pnas.1408439111
[35]	Vieira M R, Inez V Fr I, Oliver N, et al. Characterizing dense urban areas from mobile phone-call data: Discovery and social dynamics. In: Proceedings of 2010 IEEE Second International Conference, 2010: 241-248. doi: 10.1109/SocialCom.2010.41
[36]	Ahas Rein, Mark Ülar.Location based services-new challenges for planning and public administration? Futures, 2005, 37(6): 547-561. doi: 10.1016/j.futures.2004.10.012
[37]	Wang P.Understanding the spreading patterns of mobile phone viruses. Proceedings of the National Academy of Sciences, 2009, 106(20): 8380-8385.