上海中心城区商业中心空间特征研究
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摘要
利用手机信令数据识别游憩-居住功能联系,进一步在上海中心城区内识别出了24个城市级商业中心。依据各中心单位面积对游憩活动的吸引力判断等级,依据游憩者来源地分析各中心腹地并划分势力范围,依据与现状商业中心的距离和居住人口密度确定了商业中心布局优化方向。得出以下结论:(1)无论从空间分布和等级分布来看,现状商业中心都呈向心集聚特征;(2)中心的腹地和势力范围的空间分布受地铁、黄浦江等影响;(3)居民至游憩-居住功能相混合的中心平均出行距离较短;(4)商业中心规划需要重点关注居民游憩出行平均距离较大且居住密度较高的地区。上述结论有助于认识上海中心城区的空间结构,为商业中心规划布局提供决策支持。
The purpose of this study is to explore the spatial characteristics of commercial centers in the Shanghai central city by using recreation-housing relationship data obtained from mobile phone signals. We identify 24 urban commercial centers in the Shanghai central city, estimate the grade of each center according to the attraction per unit area, and analysizes the hinterland and influence areas of each center according to the residence place of recreationists. We determine the direction of spatial distribution optimization for commercial centers based on the distance to existing commercial centers and the residential density.Our main conclusions are as follows: firstly, commercial centers are centripetal agglomeration both on the aspect of spatial and grading distribution. Secondly, the distribution of hinterland and influence areas of each center is affected by subway and the Huangpu river. Thirdly mean trip distance to the centers with mixed residential and recreational function is usually shorter. Finally, areas with high recreational trip distance and high residential density must be highlighted in the commercial center plan. These conclusions will help to understand the spatial structure of the Shanghai central city and provide decision basis for commercial center development.
The purpose of this study is to explore the spatial characteristics of commercial centers in the Shanghai central city by using recreation-housing relationship data obtained from mobile phone signals. We identify 24 urban commercial centers in the Shanghai central city, estimate the grade of each center according to the attraction per unit area, and analysizes the hinterland and influence areas of each center according to the residence place of recreationists. We determine the direction of spatial distribution optimization for commercial centers based on the distance to existing commercial centers and the residential density.Our main conclusions are as follows: firstly, commercial centers are centripetal agglomeration both on the aspect of spatial and grading distribution. Secondly, the distribution of hinterland and influence areas of each center is affected by subway and the Huangpu river. Thirdly mean trip distance to the centers with mixed residential and recreational function is usually shorter. Finally, areas with high recreational trip distance and high residential density must be highlighted in the commercial center plan. These conclusions will help to understand the spatial structure of the Shanghai central city and provide decision basis for commercial center development.
引文
[1]丁亮,钮心毅,宋小冬.上海中心城就业中心体系测度--基于手机信令数据的研究[J].地理学报,2016,71(3):484-499.(DING Liang,NIU Xinyi,SONG Xiaodong.Measuring the employment center system in Shanghai central city:a study using mobile phone signaling data[J].Acta Geographica Sinica,2016,71(3):484-499.)
[2]谷一桢,郑思齐,曹洋.北京市就业中心的识别:实证方法及应用[J].城市发展研究,2009(9):118-124.(GU Yizhen,ZHENGSiqi,CAO Yang.The identification of employment centers in Beijing[J].Urban Studies,2009(9):118-124.)
[3]罗震东,朱查松.解读多中心:形态、功能与治理[J].国际城市规划,2008(1):85-88.(LUO Zhendong,ZHU Chasong.Understanding polycentricity by configuration,function and governance[J].Urban Planning International,2008(1):85-88.)
[4]钮心毅,丁亮,宋小冬.基于手机数据识别上海中心城区的城市空间结构[J].城市规划学刊,2014(6):61-67.(NIU Xinyi,DINGLiang,SONG Xiaodong.Understanding urban spatial structure of Shanghai central city based on mobile phone data[J].Urban Planning Forum,2014(6):61-67.)
[5]宁越敏,黄胜利.上海市区商业中心的等级体系及其变迁特征[J].地域研究与开发,2005,24(2):15-19.(NING Yuemin,HUANG Shengli.The hierarchical system and its changing characteristic of the retail centers in Shanghai city[J].Areal Research and Development,2005,24(2):15-19.)
[6]孙斌栋,涂婷,石巍,等.特大城市多中心空间结构的交通绩效检验--上海案例研究[J].城市规划学刊,2013(2):63-69.(SUN Bindong,TU Ting,SHI Wei,et al.Test on the performance of polycentric spatial structure as measure of congestion reduction in megacities:the case study of Shanghai[J].Urban Planning Forum,2013(2):63-69.)
[7]孙斌栋,魏旭红.上海都市区就业--人口空间结构演化特征[J].地理学报,2014(6):747-758.(SUN Bindong,WEI Xuhong.Spatial distribution and structure evolution of employment and population in Shanghai Metropolitan Area[J].Acta Geographica Sinica,2014(6):747-758.)
[8]孙铁山,王兰兰,李国平.北京都市区人口--就业分布与空间结构演化[J].地理学报,2012(6):829-840.(SUN Tieshan,WANG Lanlan,LI Guoping.Distributions of population and employment and evolution of spatial structures in the Beijing Metropolitan Area[J].Acta Geographica Sinica,2012(6):829-840.)
[9]王德,张晋庆.上海市消费者出行特征与商业空间结构分析[J].城市规划,2001(10):6-14.(WANG De,Zhangjinqing.The analysis of consumer trip characteristics and spatial structure of commercial facilities in Shanghai[J].City Planning Review,2001(10):6-14.)
[10]王德,段文婷,马林志.大型商业中心开发的空间影响分析--以上海五角场地区为例[J].城市规划学刊,2013(2):79-86.(WANG De,DUAN Wenting,MA Linzhi.Spatial impact analysis on the development of large business center--case study of Wujiaochang area in Shanghai[J].Urban Planning Forum,2013(2):79-86.)
[11]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.(WANGDe,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[J].Urban Planning Forum,2015(3):50-60.)
[12]仵宗卿,戴学珍.北京市商业中心的空间结构研究[J].城市规划,2001,25(10):15-19.(WU Zongqing,Dai Xuezhen.The study on the spatial structure of Beijing's commercial centers[J].City Planning Review,2001,25(10):15-19.)
[13]徐放.北京市的商业服务地理[J].经济地理,1984(1):40-46.(XU Fang.Business services geography of Beijing[J].Economic Geography,1984(1):40-46.)
[14]许宁,尹凌,胡金星.从大规模短期规则采样的手机定位数据中识别居民职住地[J].武汉大学学报(信息科学版),2014,39(6):750-756.(XU Ning,YIN Ling,HU Jinxing.Identifying home-work locations from shortterm,large-scale,and regularly sampled mobile phone tracking data[J].Geomatics and Information Science of Wuhan University,2014,39(6):750-756.)
[15]周素红,郝新华,柳林.多中心化下的城市商业中心空间吸引衰减率验证--深圳市浮动车GPS时空数据挖掘[J].地理学报,2014,69(12):1810-1820.(ZHOUSuhong,HAO Xinhua,LIU Lin.Validation of spatial decay law caused by urban commercial center's mutual attraction in polycentric city:spatio-temporal data mining of floating cars'GPSdata in Shenzhen[J].Acta Geographica Sinica,2014,69(12):1810-1820.)
[16]AHAS R,SILM S,J?RV O,et al.Using mobile positioning data to model locations meaningful to users of mobile phones[J].Journal of Urban Technology,2010,17(1):3-27.
[17]BURGER M,MEIJERS E.Form follows function?linking morphological and functional polycentricism[J].Urban Studies,2012(5):1127-1149.
[18]BECKER R A,CACERES R,HANSON K,et al.A tale of one city:using cellular network data for urban planning[J].IEEE Pervasive Computing,2011,10(4):18-26.
[19]DAVIES R L.Marketing geography:with special reference to retailing[M].London:Methuen,1976.
[20]GREEN N.Functional polycentricity:a formal definition in terms of social network analysis[J].Urban Studies,2007,44(11):2077-2103.
[21]LIU L,BIDERMAN A,RATTI C.Urban mobility landscape:real time monitoring of urban mobility patterns[C].//Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management,2009:1-16.
[22]MCMILLEN D P,MCDONALD J F.Population density in suburban Chicago:a bid-rent approach[J].Urban Studies,1998,35(7):1119-1130.
[23]SUN Y,FAN H,LI M,et al.Identifying city center using human travel flows generated from location-based social networking data[J].Environment and Planning B:Planning and Design,2016,43(3):480-498.
[24]VASANEN A.Functional polycentricity:examining metropolitan spatial structure through the connectivity of urban sub-centers[J].Urban Studies,2012,49(16):3627-3644.
[25]ZHONG C,ARISONA S M,HUANG X,et al.Detecting the dynamics of urban structure through spatial network analysis[J].International Journal of Geographical Information Science,2014,28(11):2178-2199.
(1)手机信令数据的介绍详见参考文献1。
(2)根据参考文献1的方法识别工作地和居住地,将在中心城工作的就业者居住密度由高到低累加,计算累加到98%就业人数的密度等值线围合的面积占街道面积比值,超过30%的街道划入中心城区。
(3)以反距离法表达空间关系,取800m距离阈值。
(4)将21个工作日中每天第一个进站(出站)站点有13日及以上相同的站点识别为代表居住地(工作地)的站点,将休息日9点-21点之间非居住地和工作地的出站站点识别为代表游憩地的站点。从157万活跃用户(21个工作日至少出现过13次)中识别出了9个休息日59万人、142万人次的居住地和游憩地。
(5)由手机信令数据识别到的居住人口分布扩样计算。
(6)中心数量越多,值最大的中心所占比值也会越低。由于商业中心是24个,若严格按照位序-规模排序每个栅格中的各中心游憩人次居住密度,密度值最高的中心应占所有中心密度值的0.27。小于0.27说明这一中心主导作用不显著,该栅格是多个中心的争夺区。
(7)金桥地区的金桥国际广场和久金购物中心分别于2015年年底和2016年年初陆续开业。在本研究采集数据时尚未开业或形成规模,故缺少商业中心地区仅表示2015年11月的情况。
[2]谷一桢,郑思齐,曹洋.北京市就业中心的识别:实证方法及应用[J].城市发展研究,2009(9):118-124.(GU Yizhen,ZHENGSiqi,CAO Yang.The identification of employment centers in Beijing[J].Urban Studies,2009(9):118-124.)
[3]罗震东,朱查松.解读多中心:形态、功能与治理[J].国际城市规划,2008(1):85-88.(LUO Zhendong,ZHU Chasong.Understanding polycentricity by configuration,function and governance[J].Urban Planning International,2008(1):85-88.)
[4]钮心毅,丁亮,宋小冬.基于手机数据识别上海中心城区的城市空间结构[J].城市规划学刊,2014(6):61-67.(NIU Xinyi,DINGLiang,SONG Xiaodong.Understanding urban spatial structure of Shanghai central city based on mobile phone data[J].Urban Planning Forum,2014(6):61-67.)
[5]宁越敏,黄胜利.上海市区商业中心的等级体系及其变迁特征[J].地域研究与开发,2005,24(2):15-19.(NING Yuemin,HUANG Shengli.The hierarchical system and its changing characteristic of the retail centers in Shanghai city[J].Areal Research and Development,2005,24(2):15-19.)
[6]孙斌栋,涂婷,石巍,等.特大城市多中心空间结构的交通绩效检验--上海案例研究[J].城市规划学刊,2013(2):63-69.(SUN Bindong,TU Ting,SHI Wei,et al.Test on the performance of polycentric spatial structure as measure of congestion reduction in megacities:the case study of Shanghai[J].Urban Planning Forum,2013(2):63-69.)
[7]孙斌栋,魏旭红.上海都市区就业--人口空间结构演化特征[J].地理学报,2014(6):747-758.(SUN Bindong,WEI Xuhong.Spatial distribution and structure evolution of employment and population in Shanghai Metropolitan Area[J].Acta Geographica Sinica,2014(6):747-758.)
[8]孙铁山,王兰兰,李国平.北京都市区人口--就业分布与空间结构演化[J].地理学报,2012(6):829-840.(SUN Tieshan,WANG Lanlan,LI Guoping.Distributions of population and employment and evolution of spatial structures in the Beijing Metropolitan Area[J].Acta Geographica Sinica,2012(6):829-840.)
[9]王德,张晋庆.上海市消费者出行特征与商业空间结构分析[J].城市规划,2001(10):6-14.(WANG De,Zhangjinqing.The analysis of consumer trip characteristics and spatial structure of commercial facilities in Shanghai[J].City Planning Review,2001(10):6-14.)
[10]王德,段文婷,马林志.大型商业中心开发的空间影响分析--以上海五角场地区为例[J].城市规划学刊,2013(2):79-86.(WANG De,DUAN Wenting,MA Linzhi.Spatial impact analysis on the development of large business center--case study of Wujiaochang area in Shanghai[J].Urban Planning Forum,2013(2):79-86.)
[11]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.(WANGDe,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[J].Urban Planning Forum,2015(3):50-60.)
[12]仵宗卿,戴学珍.北京市商业中心的空间结构研究[J].城市规划,2001,25(10):15-19.(WU Zongqing,Dai Xuezhen.The study on the spatial structure of Beijing's commercial centers[J].City Planning Review,2001,25(10):15-19.)
[13]徐放.北京市的商业服务地理[J].经济地理,1984(1):40-46.(XU Fang.Business services geography of Beijing[J].Economic Geography,1984(1):40-46.)
[14]许宁,尹凌,胡金星.从大规模短期规则采样的手机定位数据中识别居民职住地[J].武汉大学学报(信息科学版),2014,39(6):750-756.(XU Ning,YIN Ling,HU Jinxing.Identifying home-work locations from shortterm,large-scale,and regularly sampled mobile phone tracking data[J].Geomatics and Information Science of Wuhan University,2014,39(6):750-756.)
[15]周素红,郝新华,柳林.多中心化下的城市商业中心空间吸引衰减率验证--深圳市浮动车GPS时空数据挖掘[J].地理学报,2014,69(12):1810-1820.(ZHOUSuhong,HAO Xinhua,LIU Lin.Validation of spatial decay law caused by urban commercial center's mutual attraction in polycentric city:spatio-temporal data mining of floating cars'GPSdata in Shenzhen[J].Acta Geographica Sinica,2014,69(12):1810-1820.)
[16]AHAS R,SILM S,J?RV O,et al.Using mobile positioning data to model locations meaningful to users of mobile phones[J].Journal of Urban Technology,2010,17(1):3-27.
[17]BURGER M,MEIJERS E.Form follows function?linking morphological and functional polycentricism[J].Urban Studies,2012(5):1127-1149.
[18]BECKER R A,CACERES R,HANSON K,et al.A tale of one city:using cellular network data for urban planning[J].IEEE Pervasive Computing,2011,10(4):18-26.
[19]DAVIES R L.Marketing geography:with special reference to retailing[M].London:Methuen,1976.
[20]GREEN N.Functional polycentricity:a formal definition in terms of social network analysis[J].Urban Studies,2007,44(11):2077-2103.
[21]LIU L,BIDERMAN A,RATTI C.Urban mobility landscape:real time monitoring of urban mobility patterns[C].//Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management,2009:1-16.
[22]MCMILLEN D P,MCDONALD J F.Population density in suburban Chicago:a bid-rent approach[J].Urban Studies,1998,35(7):1119-1130.
[23]SUN Y,FAN H,LI M,et al.Identifying city center using human travel flows generated from location-based social networking data[J].Environment and Planning B:Planning and Design,2016,43(3):480-498.
[24]VASANEN A.Functional polycentricity:examining metropolitan spatial structure through the connectivity of urban sub-centers[J].Urban Studies,2012,49(16):3627-3644.
[25]ZHONG C,ARISONA S M,HUANG X,et al.Detecting the dynamics of urban structure through spatial network analysis[J].International Journal of Geographical Information Science,2014,28(11):2178-2199.
(1)手机信令数据的介绍详见参考文献1。
(2)根据参考文献1的方法识别工作地和居住地,将在中心城工作的就业者居住密度由高到低累加,计算累加到98%就业人数的密度等值线围合的面积占街道面积比值,超过30%的街道划入中心城区。
(3)以反距离法表达空间关系,取800m距离阈值。
(4)将21个工作日中每天第一个进站(出站)站点有13日及以上相同的站点识别为代表居住地(工作地)的站点,将休息日9点-21点之间非居住地和工作地的出站站点识别为代表游憩地的站点。从157万活跃用户(21个工作日至少出现过13次)中识别出了9个休息日59万人、142万人次的居住地和游憩地。
(5)由手机信令数据识别到的居住人口分布扩样计算。
(6)中心数量越多,值最大的中心所占比值也会越低。由于商业中心是24个,若严格按照位序-规模排序每个栅格中的各中心游憩人次居住密度,密度值最高的中心应占所有中心密度值的0.27。小于0.27说明这一中心主导作用不显著,该栅格是多个中心的争夺区。
(7)金桥地区的金桥国际广场和久金购物中心分别于2015年年底和2016年年初陆续开业。在本研究采集数据时尚未开业或形成规模,故缺少商业中心地区仅表示2015年11月的情况。