北京市多尺度中心特征识别与群聚模式发现
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摘要
城市多中心的研究是揭开城市系统复杂空间结构的重要研究内容,然而受传统调查数据时空精度的局限,现有研究缺乏足够细致的实证分析。LBS服务的广泛应用为高效、实时、微观地刻画个体和城市之间的空间活动提供了充足的信息,使得对城市多中心的微观量化机制的研究成为可能。基于此原因,本文结合兴趣点数据以及微博签到数据和出租车GPS轨迹数据对北京市五环内的不同尺度、不同社会功能的城市中心进行了识别,并对其居民行为的群聚模式进行了分析。结果表明,北京市五环内包括4个市级中心,16个区级中心以及45个街道级中心。市级中心包括海淀科教中心,西单休闲中心,北京文化中心和国贸金融中心4大中心;区级中心主要包括文娱中心、商业中心、教育中心以及交通中心4类中心;街道级中心除了区级的四类中心外,还包括行政中心、购物中心、商住中心等9类中心。不同中心在同一时段呈现相似的群聚模式,其中工作日城市不同尺度中心的群聚模式持续时间较长,群聚密度较大,主要以通勤和饮食为主;非工作日城市不同尺度中心的群聚模式相对较分散,持续时间较短,主要以休闲娱乐为主。
With the development of urbanization, the problem of "big city disease" is becoming more and more prominent in recent years. Since the strategy of polycentric city has been proposed, polycentric characteristics of city has become one of the most important issue in revealing spatial structure of a city. Due to the rough resolution of traditional census data, most studies have not get an insight into the fine structure of city centers and lacked empirical researches. With the widely application of LBS services, most trajectory data of human activities have been recorded to provide a profile of daily urban dynamics in real time. This information has opened up a new way to analyze the mechanism of polycentric city. Based on the reason, this study proposes a new method to identify the multi-centers in Beijing within rings, and analyzes the spatial-temporal characteristics and interactions in each center, using sina micro blog check-in data and taxi GPS data with POI data. In our study, groups of centers with different social functions can be identified from the clusters of residents' activities. From these results, 4 city centers, 16 district centers and 45 community centers have been identified within 5 th rings in Beijing. The district centers can be divided into 4 groups, including cultural and recreational center, business center, education center and transportation center. And the representativeness for each types of centers are Sanlitun, Guomao, Beijing Normal University and Beijing West Railway Station. The community centers can be divided into 9 groups, including political center, residential center, administration center and business-residential center. The representativeness for each types of centers are Xidan, Wudaokou,Wanliu, Qianmen, Zhongguancun, Tianmen square, Chaoyang District Government, Beijing West Railway Station and Yonganli Community. The residential clusters can be observed in some daily hours. Number of residential clusters for district centers are commonly higher than number for community centers, while number of residential clusters in workdays area higher than that in holidays. For district centers, there are 4 classes of common cluster pattern in workdays and 5 classes of common cluster pattern in holidays. For community centers, there are 5 classes of common cluster pattern in workdays and 3 classes of common cluster pattern in holidays, respectively.
With the development of urbanization, the problem of "big city disease" is becoming more and more prominent in recent years. Since the strategy of polycentric city has been proposed, polycentric characteristics of city has become one of the most important issue in revealing spatial structure of a city. Due to the rough resolution of traditional census data, most studies have not get an insight into the fine structure of city centers and lacked empirical researches. With the widely application of LBS services, most trajectory data of human activities have been recorded to provide a profile of daily urban dynamics in real time. This information has opened up a new way to analyze the mechanism of polycentric city. Based on the reason, this study proposes a new method to identify the multi-centers in Beijing within rings, and analyzes the spatial-temporal characteristics and interactions in each center, using sina micro blog check-in data and taxi GPS data with POI data. In our study, groups of centers with different social functions can be identified from the clusters of residents' activities. From these results, 4 city centers, 16 district centers and 45 community centers have been identified within 5 th rings in Beijing. The district centers can be divided into 4 groups, including cultural and recreational center, business center, education center and transportation center. And the representativeness for each types of centers are Sanlitun, Guomao, Beijing Normal University and Beijing West Railway Station. The community centers can be divided into 9 groups, including political center, residential center, administration center and business-residential center. The representativeness for each types of centers are Xidan, Wudaokou,Wanliu, Qianmen, Zhongguancun, Tianmen square, Chaoyang District Government, Beijing West Railway Station and Yonganli Community. The residential clusters can be observed in some daily hours. Number of residential clusters for district centers are commonly higher than number for community centers, while number of residential clusters in workdays area higher than that in holidays. For district centers, there are 4 classes of common cluster pattern in workdays and 5 classes of common cluster pattern in holidays. For community centers, there are 5 classes of common cluster pattern in workdays and 3 classes of common cluster pattern in holidays, respectively.
引文
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[23]Zhong C,Huang X F,Arisona S M,et al.Inferring building functions from a probabilistic model using public transportation data[J].Computers Environment and Urban Systems,2014,48:124-137.
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[25]Ratti C,Frenchman D,Pulselli R M,et al.Mobile landscapes:Using location data from cell phones for urban analysis[J].Environment and Planning B-Planning&Design,2006,33:727-748.
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[27]Pei T,Sobolevsky S,Ratti C,et al.A new insight into land use classification based on aggregated mobile phone data[J].International Journal of Geographical Information Science,2014,28:1988-2007.
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[31]Cranshaw J,Schwartz R,Hong J I,et al.The Livehoods Project:Utilizing social media to understand the dynamics of a city[C].Dublin:International AAAI Conference on Weblogs and Social Media,2012:58-65.
[32]Liu Y,Wang F H,Xiao Y,et al.Urban land uses and traffic'source-sink areas':Evidence from GPS-enabled taxi data in Shanghai[J].Landscape and Urban Planning,2012,106:73-87.
[33]Guo D S,Zhu X,Jin H,et al.Discovering spatial patterns in origin-destination mobility data[J].Transactions in GIS,2012,16:411-429.
[2]Anas A,I K.General equilibrium models of polycentric urban land use with endogenous congestion and job agglomeration[J].Journal of Urban Economics,1996,40:232-256.
[3]Anas A,Arnott R.Small K A.Urban spatial structure[J].Journal of Economic Literature,1996,36:1426-1464.
[4]韦亚平,赵民.都市区空间结构与绩效--多中心网络结构的解释与应用分析[J].城市规划,2006,30:9-16.[Wei Y P,Zhao M.Spatial structure and performance of metropolis:Interpretation and application of polycentric structure[J].City Planning Review,2006,30:9-16.]
[5]Fujita M,Ogawa H.Multiple equilibria and structural transition of non-monocentric urban configurations[J].Regional Science and Urban Economics,1982,12:161-196.
[6]吴文钰,马西亚.多中心城市人口模型及模拟:以上海为例[J].现代城市研究,2006(12):39-44.[Wu W Y,Ma X Y.Polycentric population density functions and modeling:The case of Shanghai[J].Modern Urban Research,2006(12):39-44.]
[7]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[J].地理研究,2013,32:711-719.[Qin X H,Wei YH,Chen W,et al.Population expansion and polycentric development of Nanjing city in a period of hyper-growth[J].Geographical Research,2013,32(4):711-719.]
[8]刘安国,杨开忠.克鲁格曼的多中心城市空间自组织模型评析[J].地理科学,2001,21(4):315-322.[Liu A G,Yang K Z.Comments on Krugman's Edge City MO/Del[J].Scientia Geographica Sinica,2001,21(4):315-322.]
[9]沈宏婷,张京祥,陈眉舞.中国大城市空间的“多中心”重组[J].城市问题,2005(4):25-30.[Shen H T,Zhang J X,Chen M W.On the multicentered restructure of metropolitan space in China[J].Urban Problems,2005(4):25-30]
[10]周春山.城市空间结构与形态[M].北京:科学出版社,2007.[Zhou C S.Urban spatial structure and morphology[M].Beijing:Science Publishing,2007.]
[11]Champion A G.A changing demographic regime and evolving polycentric urban regions:Consequences for the size,composition and distribution of city populations[J].Urban Studies,2001,38:657-677.
[12]McMillen D P.Polycentric urban structure:The case of Milwaukee[J].Economic Perspectives-Federal Reserve Bank of Chicago,2001,25:15-27.
[13]Meijers E J,Burger M J.Spatial structure and productivity in US metropolitan areas[J].Environment and Planning A,2010,42:1383-1402.
[14]冯健,周一星.中国城市内部空间结构研究进展与展望[J].地理科学进展,2003,22(3):304-315.[Feng J,Zhou YX.A review and prospect on urban internal spatial structure research in China[J].Progress in Geography,2003,22(3):304-315.]
[15]孙斌栋,石巍,宁越敏.上海市多中心城市结构的实证检验与战略思考[J].城市规划学刊,2010(1):58-63.[Sun BD,Shi W,Ning Y M.An empirical study on the polycentric urban structure of Shanghai and strategies in future[J].Urban Planning Forum,2010(1):58-63.]
[16]张纯,易成栋,宋彦.北京市职住空间关系特征及变化研究:基于第五和六次人口普查和2001和2008年经济普查数据的实证分析[J].城市规划,2016,40(10):59-64.[Zhang C,Yi C D,Song Y.Characteristics of job-housing spatial relationship and changes in Beijing:An empirical study based on data from the 5th,6thpopulation census and economy census in 2001 and 2008[J].City Planning Review,2016,40(10):59-64]
[17]Batty M.“Smart cities,big data”[J].Environment and Planning B:Planning and Design,2012,39:191-193.
[18]Chen C,Zhang D,Castro P S,et al.Real-time detection of anomalous taxi trajectories from GPS traces[C].Mobile and Ubiquitous Systems:Computing,Networking,and Services,Berlin:Springer,2012:63-74.
[19]Fang Z X,Shaw S L,Tu W,et al.Spatiotemporal analysis of critical transportation links based on time geographic concepts:A case study of critical bridges in Wuhan,China[J].Journal of Transport Geography,2012,23:44-59.
[20]Sun L,Zhang D Q,Chen C,et al.Real Time anomalous trajectory detection and analysis[J].Mobile Networks&Applications,2013,18:341-356.
[21]Zheng K,Zheng Y,Yuan N J,et.al.On Discovery of Gathering Patterns from Trajectories[C].IEEE 29thInternational Conference on Data Engineering(ICDE),2013:242-253.
[22]Roth C,Kang S M,Batty M,et al.Structure of urban movements:Polycentric activity and entangled hierarchical flows[J].Plos One,2011,6(1):1-9.
[23]Zhong C,Huang X F,Arisona S M,et al.Inferring building functions from a probabilistic model using public transportation data[J].Computers Environment and Urban Systems,2014,48:124-137.
[24]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行.地理学报,2012,67(10):1339-1352.[Long Y,Zhang Y,Cui C Y.Identifying commuting pattern of Beijing using bus smart card data[J].Acta Geographica Sinica,2012,67(10):1339-1352.]
[25]Ratti C,Frenchman D,Pulselli R M,et al.Mobile landscapes:Using location data from cell phones for urban analysis[J].Environment and Planning B-Planning&Design,2006,33:727-748.
[26]Calabrese F,Colonna M,Lovisolo P,et al.Real-time urban monitoring using cell phones:A case study in rome[J].IEEE Transactions on Intelligent Transportation Systems,2011,12:141-151.
[27]Pei T,Sobolevsky S,Ratti C,et al.A new insight into land use classification based on aggregated mobile phone data[J].International Journal of Geographical Information Science,2014,28:1988-2007.
[28]Birkin M,Malleson N.Microscopic simulations of complex metropolitan dynamics[C].Amsterdam:Complex City workshop,2011.
[29]Wakamiya S,Lee R,Sumiya K.Urban area characterization based on semantics of crowd activities in twitter[J].Geospatial Semantics,2011,6631:108-123.
[30]Hollensteinl L,Purves R S.Exploring place through usergenerated content:Using Flickr tags to describe city cores[J].Journal of Spatial Information Science,2010,1:21-48.
[31]Cranshaw J,Schwartz R,Hong J I,et al.The Livehoods Project:Utilizing social media to understand the dynamics of a city[C].Dublin:International AAAI Conference on Weblogs and Social Media,2012:58-65.
[32]Liu Y,Wang F H,Xiao Y,et al.Urban land uses and traffic'source-sink areas':Evidence from GPS-enabled taxi data in Shanghai[J].Landscape and Urban Planning,2012,106:73-87.
[33]Guo D S,Zhu X,Jin H,et al.Discovering spatial patterns in origin-destination mobility data[J].Transactions in GIS,2012,16:411-429.