移动定位大数据支持建成环境规划设计的途径和方法
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摘要
移动定位大数据包括手机信令数据、移动互联网LBS数据等类型,是表示个体活动时空特征的轨迹数据。在移动定位大数据中依据轨迹点的时空特征规律能识别出个体活动的居住、工作、游憩等特征点,计算上述活动的空间分布特征,进而获得居住、工作、游憩等功能之间联系的流向和流量特征,获得传统数据无法反映的城市功能空间分布以及城市功能联系的时空特征。由此,移动定位大数据支持建成环境规划设计有密度出发和联系出发两种应用途径。联系出发的途径能支持区域城市关联、职住空间关系、城市中心体系、设施服务水平等方向;密度出发的应用途径能支持城市人口规模、街道活力分析等方向。移动定位大数据测算居民活动特征还只是一种推测,在应用方法上要避免"黑箱"方式,对移动定位大数据测算结果一定要结合传统数据进行校核,提高可靠性。
With the development of information and communication technologies, especially mobile communications and mobile Internet, portable mobile devices have generated a variety of mobile positioning big data. The mobile positioning big data includes mobile phone signaling data, mobile Internet LBS data and other similar data, which is a kind of trajectory data indicating the spatial and temporal characteristics of individual activities. From the temporal and spatial characteristics of the trajectory points based on the mobile positioning big data, the feature points such as residence, work, and recreation of individual activities can be identified,so that the spatial distribution characteristics of these above activities can be calculated. In addition, the flow direction and the flow volume characteristics between the functions of residence, work, recreation can be obtained, which can present the spatial distribution of urban functions and the spatial and temporal characteristics of urban functional links that cannot be reflected by traditional data. This is the application basis of mobile positioning big data in planning and design of built environment. Mobile positioning big data can also be used for regional urban agglomeration and urban system analysis. For example, flow of people between cities can be measured using mobile positioning big data. In this application scenario, mobile positioning big data is equivalent to passenger data of full-transportation mode between cities which can support regional planning.Therefore, there are two application approaches for mobile positioning big data to support built environment planning and design, density-based and link-based approaches. The linkbased application is to focus on the link between specific activities such as "residence", "work"and "recreation" of the same user. The link-based approaches can support regional urban association, jobs-housing spatial relationship, urban public center system, urban facilities service level, etc. The density-based approach is to focus on the density spatial distribution characteristics of specific functions such as residence, work and recreation. Since mobile positioning big data has relatively high resolution spatial positioning accuracy, the density of urban functions such as residence, employment, and recreation is no longer restricted by spatial statistical units, and can be applied to different scale in planning and design. The density-based approach can support the research direction of urban population and street vitality analysis.Applications of mobile positioning big data in planning and design should pay attention to the application method. Resident activity characteristics measured by mobile positioning big data is only a speculation, so the characteristics of data must be fully understood, these characteristics include the positioning principle, the time continuity of positioning, the spatial resolution of positioning. The data product provider's calculation rules and algorithms from raw data to data products must be transparent and must be clearly communicated to the application.For the designers, it is necessary to understand the data characteristics and data processing rules clearly, and avoid the "black box" type of data application. When determining the data application scenario, the time continuity and spatial resolution of the data must be considered. The mobile positioning big data with good time continuity is more suitable for the link-based application, and the mobile positioning big data with high spatial precision is more suitable for the density-based application in fine spatial scale. As far as the current various kinds of data, there is no such mobile positioning big data that can meet both two requirements of spatial high resolution and better time continuity.The mobile positioning big data application in supporting planning and design must be combined with traditional data to improve the reliability of data analysis. The traditional census data has an irreplaceable advantage, and the sample survey data that follows strict rules is also a good data source. Existing technologies are not yet able to directly support planning predictions using mobile positioning big data. Mobile positioning big data support planning predictions requires a period of data accumulation and requires better technical support.
With the development of information and communication technologies, especially mobile communications and mobile Internet, portable mobile devices have generated a variety of mobile positioning big data. The mobile positioning big data includes mobile phone signaling data, mobile Internet LBS data and other similar data, which is a kind of trajectory data indicating the spatial and temporal characteristics of individual activities. From the temporal and spatial characteristics of the trajectory points based on the mobile positioning big data, the feature points such as residence, work, and recreation of individual activities can be identified,so that the spatial distribution characteristics of these above activities can be calculated. In addition, the flow direction and the flow volume characteristics between the functions of residence, work, recreation can be obtained, which can present the spatial distribution of urban functions and the spatial and temporal characteristics of urban functional links that cannot be reflected by traditional data. This is the application basis of mobile positioning big data in planning and design of built environment. Mobile positioning big data can also be used for regional urban agglomeration and urban system analysis. For example, flow of people between cities can be measured using mobile positioning big data. In this application scenario, mobile positioning big data is equivalent to passenger data of full-transportation mode between cities which can support regional planning.Therefore, there are two application approaches for mobile positioning big data to support built environment planning and design, density-based and link-based approaches. The linkbased application is to focus on the link between specific activities such as "residence", "work"and "recreation" of the same user. The link-based approaches can support regional urban association, jobs-housing spatial relationship, urban public center system, urban facilities service level, etc. The density-based approach is to focus on the density spatial distribution characteristics of specific functions such as residence, work and recreation. Since mobile positioning big data has relatively high resolution spatial positioning accuracy, the density of urban functions such as residence, employment, and recreation is no longer restricted by spatial statistical units, and can be applied to different scale in planning and design. The density-based approach can support the research direction of urban population and street vitality analysis.Applications of mobile positioning big data in planning and design should pay attention to the application method. Resident activity characteristics measured by mobile positioning big data is only a speculation, so the characteristics of data must be fully understood, these characteristics include the positioning principle, the time continuity of positioning, the spatial resolution of positioning. The data product provider's calculation rules and algorithms from raw data to data products must be transparent and must be clearly communicated to the application.For the designers, it is necessary to understand the data characteristics and data processing rules clearly, and avoid the "black box" type of data application. When determining the data application scenario, the time continuity and spatial resolution of the data must be considered. The mobile positioning big data with good time continuity is more suitable for the link-based application, and the mobile positioning big data with high spatial precision is more suitable for the density-based application in fine spatial scale. As far as the current various kinds of data, there is no such mobile positioning big data that can meet both two requirements of spatial high resolution and better time continuity.The mobile positioning big data application in supporting planning and design must be combined with traditional data to improve the reliability of data analysis. The traditional census data has an irreplaceable advantage, and the sample survey data that follows strict rules is also a good data source. Existing technologies are not yet able to directly support planning predictions using mobile positioning big data. Mobile positioning big data support planning predictions requires a period of data accumulation and requires better technical support.
引文
[1]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[J].上海城市规划,2015(2):39-43.NIU X Y, DING L. Analyzing jobhousing spatial relationship in Shanghai using mobile phone data:Some conclusions and discussions[J]. Shanghai Urban Planning Review, 2015(2):39-43.
[2]许园园,塔娜,李响.基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J].人文地理,2017, 32(3):93-101.XU Y Y,TA N,LI X. Analysis of commuting behavior and employment center using subway smart card data[J].Human Geography, 2017, 32(3):93-101.
[3]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[J].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]. Scientia Geographica Sinica,2012, 67(10):1339-1352.
[4]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[J].城市规划,2015,39(9):100-106.DING L,NIU X Y,SONG X D.Identifying the commuting area of Shanghai central city using mobile phone data[J]. City Planning Review,2015,39(9):100-106.
[5]张天然.基于手机信令数据的上海市域职住空间分析[J].城市交通,2016(1):15-23.ZHANG T R. Jobs-housing spatial distribution analysis in shanghai metropolitan area based on cellular signaling data[J]. Urban Transport of China, 2016(1):15-23.
[6]李星月,陈濛.大数据背景下同城化量化分析方法及温岭市实践[J].规划师,2016,32(2):83-88.LI X Y, CHEN M. Quantitative study of urban integration with big data and wenling's practice[J]. Planners, 2016,32(2):83-88.
[7]钮心毅,丁亮,宋小冬.基于职住空间关系分析上海郊区新城发展状况[J].城市规划,2017, 41(8):47-53.NIU X Y,DING L,SONG X D.Analyzing suburban new town development in shanghai from the perspective of jobs-housing spatial relationship[J]. City Planning Review,2017, 41(8):47-53.
[8]田金玲,王德,谢栋灿,等.上海市典型就业区的通勤特征分析与模式总结——张江、金桥和陆家嘴的案例比较[J].地理研究,2017, 36(1):134-148.TIAN J L, WANG D, XIE D C, et al.Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data:a case study in Zhangjiang, Jinqiao and Lujiazui[J]. Geographical Research,2017, 36(1):134-148.
[9]钮心毅,王立,刘嘉伟,等.基于跨城功能联系的上海都市圈空间结构研究[J].城市规划学刊,2018(5):80-87.NIU X Y, WANG Y, LIU J W, et al.Spatial structure of shanghai metropolitan coordination area from perspective of inter-city functional links[J]. UrbanPlanning Forum, 2018(5):80-87.
[10]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[J].城市规划学刊,2015(3):50-60.WANG D, WANG C, XIE D C, 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[J]. Urban Planning Forum,2015(3):50-60.
[11]丁亮,钮心毅,宋小冬.上海中心城区商业中心空间特征研究[J].城市规划学刊,2017(1):63-70.DING L,NIU X Y,SONG X D. A study on spatial characteristics of commercial centers in the Shanghai Central City[J]. Urban Planning Forum,2017(1):63-70.
[12]龙奋杰,石朗,彭智育,等.基于手机信令数据的城市公园服务评价[J].城市问题,2018(6):88-92.LONG F J, SHI L, PENG Z Y, et al.Urban park service evaluation based on mobile phone signaling data[J]. Urban Problems,2018(6):88-92.
[13]方家,刘颂,王德,等.基于手机信令数据的上海城市公园供需服务分析[J].风景园林,2017(11):35-40.FANG J, LIU S, WANG D, et al.Supply-demand service analysis of park in shanghai by mobile phone signaling data[J]. Landscape Architecture,2017(11):35-40.
[14]钮心毅,朱娟,施澄.手机信令数据支持城市总体规划实施评估的技术框架[J].城市建筑,2017(27):16-20.NIU X Y, ZHU J, SHI C. A technical framework for urban master plan implementation evaluation using mobile phone signaling data[J]. Urbanism and Architecture, 2017(27):16-20.
[15]钮心毅,王立,刘嘉伟.基于手机信令数据的深圳与周边城市的空间关联分析[J].城市建筑,2018(5):34-38.NIU X Y,WANG Y,LIU J W. Analysis of spatial relevance between Shenzhen and its neighboring cities based on mobile phone signaling data[J].Urbanism and Architecture, 2018(5):34-38.
[16]钮心毅,王立,丁亮.利用手机信令数据测度城镇体系的等级结构[J].规划师,2017, 33(1):50-56.NIU X Y, WANG Y, DING L.Measuring urban system hierarchy with cellphone signaling[J]. Planners, 2017,33(1):50-56.
[17]刘望保,石恩名.基于ICT的中国城市间人口日常流动空间格局——以百度迁徙为例[J].地理学报,2016, 71(10):1667-1679.LIU W B, SHI E M. Spatial pattern of population daily flow among cities based on ICT:A case study of"Baidu Migration"[J]. Scientia Geographica Sinica,2016, 71(10):1667-1679.
[18]周永杰,刘洁贞,朱锦锋,等·基于手机信令数据的珠三角城市群空间特征研究[J].规划师,2018(1):113-119.ZHOU Y J, LIU J Z, ZHU J F, et al.Research on the spatial characteristics of Pearl River Delta Urban Agglomeration based on mobile signaling data[J].Planners, 2018(1):113-119.
[19]施澄,陈晨,钮心毅.面向“实际服务人口”的特大城市空间规划响应——以杭州市为例[J].城市规划学刊,2018(4):41-48.SHI C, CHEN C, NIU X Y. Planning megacities for the actual service population:the case of Hangzhou[J].Urban Planning Forum, 2018(4):41-48.
[20]钟炜菁,王德,谢栋灿,等.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.ZHONG W J, WANG D, XIE D C, et al.Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J]. GeographicalResearch, 2017, 36(5):972-984.
[21]钮心毅,吴莞姝,李萌.基于LBS定位数据的建成环境对街道活力影响及其时空特征研究[J].国际城市规划,2019(1):29-37.NIU X Y, WU W S, LI M. In uence of built environment on street vitality and its spatiotemporal characteristics based on LBS positioning data[J]. Urban Planning International, 2019(1):29-37.
[22]杨喜平,方志祥.移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J].地理科学进展,2018, 37(7):880-889YANG X P, FANG Z X. Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018,37(7):880-889.
[2]许园园,塔娜,李响.基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J].人文地理,2017, 32(3):93-101.XU Y Y,TA N,LI X. Analysis of commuting behavior and employment center using subway smart card data[J].Human Geography, 2017, 32(3):93-101.
[3]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[J].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]. Scientia Geographica Sinica,2012, 67(10):1339-1352.
[4]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[J].城市规划,2015,39(9):100-106.DING L,NIU X Y,SONG X D.Identifying the commuting area of Shanghai central city using mobile phone data[J]. City Planning Review,2015,39(9):100-106.
[5]张天然.基于手机信令数据的上海市域职住空间分析[J].城市交通,2016(1):15-23.ZHANG T R. Jobs-housing spatial distribution analysis in shanghai metropolitan area based on cellular signaling data[J]. Urban Transport of China, 2016(1):15-23.
[6]李星月,陈濛.大数据背景下同城化量化分析方法及温岭市实践[J].规划师,2016,32(2):83-88.LI X Y, CHEN M. Quantitative study of urban integration with big data and wenling's practice[J]. Planners, 2016,32(2):83-88.
[7]钮心毅,丁亮,宋小冬.基于职住空间关系分析上海郊区新城发展状况[J].城市规划,2017, 41(8):47-53.NIU X Y,DING L,SONG X D.Analyzing suburban new town development in shanghai from the perspective of jobs-housing spatial relationship[J]. City Planning Review,2017, 41(8):47-53.
[8]田金玲,王德,谢栋灿,等.上海市典型就业区的通勤特征分析与模式总结——张江、金桥和陆家嘴的案例比较[J].地理研究,2017, 36(1):134-148.TIAN J L, WANG D, XIE D C, et al.Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data:a case study in Zhangjiang, Jinqiao and Lujiazui[J]. Geographical Research,2017, 36(1):134-148.
[9]钮心毅,王立,刘嘉伟,等.基于跨城功能联系的上海都市圈空间结构研究[J].城市规划学刊,2018(5):80-87.NIU X Y, WANG Y, LIU J W, et al.Spatial structure of shanghai metropolitan coordination area from perspective of inter-city functional links[J]. UrbanPlanning Forum, 2018(5):80-87.
[10]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[J].城市规划学刊,2015(3):50-60.WANG D, WANG C, XIE D C, 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[J]. Urban Planning Forum,2015(3):50-60.
[11]丁亮,钮心毅,宋小冬.上海中心城区商业中心空间特征研究[J].城市规划学刊,2017(1):63-70.DING L,NIU X Y,SONG X D. A study on spatial characteristics of commercial centers in the Shanghai Central City[J]. Urban Planning Forum,2017(1):63-70.
[12]龙奋杰,石朗,彭智育,等.基于手机信令数据的城市公园服务评价[J].城市问题,2018(6):88-92.LONG F J, SHI L, PENG Z Y, et al.Urban park service evaluation based on mobile phone signaling data[J]. Urban Problems,2018(6):88-92.
[13]方家,刘颂,王德,等.基于手机信令数据的上海城市公园供需服务分析[J].风景园林,2017(11):35-40.FANG J, LIU S, WANG D, et al.Supply-demand service analysis of park in shanghai by mobile phone signaling data[J]. Landscape Architecture,2017(11):35-40.
[14]钮心毅,朱娟,施澄.手机信令数据支持城市总体规划实施评估的技术框架[J].城市建筑,2017(27):16-20.NIU X Y, ZHU J, SHI C. A technical framework for urban master plan implementation evaluation using mobile phone signaling data[J]. Urbanism and Architecture, 2017(27):16-20.
[15]钮心毅,王立,刘嘉伟.基于手机信令数据的深圳与周边城市的空间关联分析[J].城市建筑,2018(5):34-38.NIU X Y,WANG Y,LIU J W. Analysis of spatial relevance between Shenzhen and its neighboring cities based on mobile phone signaling data[J].Urbanism and Architecture, 2018(5):34-38.
[16]钮心毅,王立,丁亮.利用手机信令数据测度城镇体系的等级结构[J].规划师,2017, 33(1):50-56.NIU X Y, WANG Y, DING L.Measuring urban system hierarchy with cellphone signaling[J]. Planners, 2017,33(1):50-56.
[17]刘望保,石恩名.基于ICT的中国城市间人口日常流动空间格局——以百度迁徙为例[J].地理学报,2016, 71(10):1667-1679.LIU W B, SHI E M. Spatial pattern of population daily flow among cities based on ICT:A case study of"Baidu Migration"[J]. Scientia Geographica Sinica,2016, 71(10):1667-1679.
[18]周永杰,刘洁贞,朱锦锋,等·基于手机信令数据的珠三角城市群空间特征研究[J].规划师,2018(1):113-119.ZHOU Y J, LIU J Z, ZHU J F, et al.Research on the spatial characteristics of Pearl River Delta Urban Agglomeration based on mobile signaling data[J].Planners, 2018(1):113-119.
[19]施澄,陈晨,钮心毅.面向“实际服务人口”的特大城市空间规划响应——以杭州市为例[J].城市规划学刊,2018(4):41-48.SHI C, CHEN C, NIU X Y. Planning megacities for the actual service population:the case of Hangzhou[J].Urban Planning Forum, 2018(4):41-48.
[20]钟炜菁,王德,谢栋灿,等.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.ZHONG W J, WANG D, XIE D C, et al.Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J]. GeographicalResearch, 2017, 36(5):972-984.
[21]钮心毅,吴莞姝,李萌.基于LBS定位数据的建成环境对街道活力影响及其时空特征研究[J].国际城市规划,2019(1):29-37.NIU X Y, WU W S, LI M. In uence of built environment on street vitality and its spatiotemporal characteristics based on LBS positioning data[J]. Urban Planning International, 2019(1):29-37.
[22]杨喜平,方志祥.移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J].地理科学进展,2018, 37(7):880-889YANG X P, FANG Z X. Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018,37(7):880-889.