基于居民行为周期特征的城市空间研究
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摘要
伴随着中国经济社会进入"新常态"的发展阶段,对城市存量空间的研究提出了更加精细化的要求,基于居民行为活动的周期规律对城市空间进行研究,进而提升城市空间的品质日益重要。随着信息通信技术的快速发展,使许多大数据的获取成为可能,并由于其低成本、即时、大样本等优势,在城市空间研究方面具有巨大的价值。以上海市中心城区为例,利用手机信令数据,探究居民活动的空间周期变化特征,并基于空间的周期特征曲线,采用相似性传播聚类算法进行空间分类。研究表明,居民活动有平日一日周期和平日加周末二日周期,与人的作息规律相符合。市核心区、城市副中心及主要就业中心,昼夜波动和平日周末活动强度的差异都较为明显。空间分类结果显示,城市活动空间的组织既体现出个体充分的空间能动性,也反映出对土地使用类型以及设施建设、投入程度的耦合性。上海市内环内核心区混合多样的用地模式使得活动区内居民活动内容丰富,周期特征功能区边界模糊。研究成果可为未来的城市空间规划提供指导,为城市空间结构、功能布置、设施布局等优化提供决策支撑和科学依据。
As the development of economy and society enters into the "new normal" stage in China,urban planning is also gradually transformed from the traditional incremental planning to inventory planning.It is important to explore the urban spatial dynamic functional characteristics,and to optimize the use of urban activity space based on people's needs,which would enhance the quality of urban space.Advancements of information,communication,and location-aware technologies have made collections of various passively generated datasets possible.These datasets provide new opportunities to understand spatial dynamic characteristics at a low cost and large scale.This study explored the classification of urban space and spatial dynamic characteristics based on a large mobile phone location dataset from Shanghai Municipality,China.The results suggest that the geographical differences of spatial dynamic patterns in Shanghai are evident.The diurnal activity curve is consistent with the patterns of human activity.There were significant differences in intensity of day-to-day activity fluctuations and weekday activities between downtown,sub-centers,and major employment centers.Affinity propagation clustering was introduced to identify the characteristics of urban spatial structure and identify the characteristics of urban space structure of liquidity and viscosity.Several distinct patterns were extracted,and the spatial distributions of the derived clusters highlight distinct human mobility patterns in different areas of the city.We then discuss the socioeconomic and demographic characteristics of the regions covered by different cluster types to gain insights of human mobility patterns in the context of urban functional regions.The findings could offer useful information for policy and decision making.
As the development of economy and society enters into the "new normal" stage in China,urban planning is also gradually transformed from the traditional incremental planning to inventory planning.It is important to explore the urban spatial dynamic functional characteristics,and to optimize the use of urban activity space based on people's needs,which would enhance the quality of urban space.Advancements of information,communication,and location-aware technologies have made collections of various passively generated datasets possible.These datasets provide new opportunities to understand spatial dynamic characteristics at a low cost and large scale.This study explored the classification of urban space and spatial dynamic characteristics based on a large mobile phone location dataset from Shanghai Municipality,China.The results suggest that the geographical differences of spatial dynamic patterns in Shanghai are evident.The diurnal activity curve is consistent with the patterns of human activity.There were significant differences in intensity of day-to-day activity fluctuations and weekday activities between downtown,sub-centers,and major employment centers.Affinity propagation clustering was introduced to identify the characteristics of urban spatial structure and identify the characteristics of urban space structure of liquidity and viscosity.Several distinct patterns were extracted,and the spatial distributions of the derived clusters highlight distinct human mobility patterns in different areas of the city.We then discuss the socioeconomic and demographic characteristics of the regions covered by different cluster types to gain insights of human mobility patterns in the context of urban functional regions.The findings could offer useful information for policy and decision making.
引文
柴彦威,申悦,马修军,等.2013.北京居民活动与出行行为时空数据采集与管理[J].地理研究,32(3):441-451[Chai Y W,Shen Y,Ma X J,et al.2013.The collection and management of space-time data of individual behavior based on location-based technologies:A case study of activity-travel survey in Beijing[J].Geographical Research32(3):441-451.]
柴彦威,沈洁.2008.基于活动分析法的人类空间行为研究[J].地理科学,28(5):594-600.[Chai Y W,Shen J.2008Acitivity-based approach to human spatial behavior research[J].Scientia Geographica Sinica,28(5):594-600.]
柴彦威,塔娜.2013.中国时空间行为研究进展[J].地理科学进展,32(9):1362-1373.[Chai Y W,Ta N.2013.Progress in space-time behavior research in China[J].Progress in Geography,32(9):1362-1373.]
龚玺,裴韬,孙嘉,等.2011.时空轨迹聚类方法研究进展[J]地理科学进展,30(5):522-534.[Gong X,Pei T,Sun J,e al.2011.Review of the research progresses in trajectory clustering methods[J].Progress in Geography,30(5):522-534.]
郭璨,甄峰,朱寿佳.2014.智能手机定位数据应用于城市研究的进展与展望[J].人文地理,29(6):18-23.[Guo CZhen F,Zhu S J.2014.Progress and prospect of the application of smart phone LBS data in urban researches[J].Human Geography,29(6):18-23.]
钮心毅,丁亮,宋小冬.2014.基于手机数据识别上海中心城的城市空间结构[J].城市规划学刊,(6):61-67.[Niu X YDing L,Song X D.2014.Understanding urban spatia structure of Shanghai central city based on mobile phone data[J].Urban Planning Forum,(6):61-67.]
秦萧,甄峰,熊丽芳,等.2013.大数据时代城市时空间行为研究方法[J].地理科学进展,32(9):1352-1361.[Qin XZhen F,Xiong L F,et al.2013.Methods in urban tempora and spatial behavior research in the Big Data Era[J].Progress in Geography,32(9):1352-1361.]
宋辞,裴韬.2012.基于特征的时间序列聚类方法研究进展[J].地理科学进展,31(10):1307-1317.[Song C,Pei T2012.Research progress in time series clustering methods based on characteristics[J].Progress in Geography,31(10)1307-1317.]
王德,王灿,谢栋灿,等.2015.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,(3):51-61.[Wang D,Wang C,Xie D C,et al.2015.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,(3):51-61.]
王德,钟炜菁,谢栋灿,等.2015.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[J].城市规划学刊,(5):82-90.[Wang D,Zhong W J,Xie D C,et al.2015.The application of cell phone signaling data in the assessment of urban built environment:A case study of Baoshan district in Shanghai[J].Urban Planning Forum,(5):82-90.]
吴康敏,张虹鸥,王洋,等.2016.广州市多类型商业中心识别与空间模式[J].地理科学进展,35(8):963-974.[Wu KM,Zhang H O,Wang Y,et al.2016.Identify of the multiple types of commercial center in Guangzhou and its spatial pattern[J].Progress in Geography,35(8):963-974.]
谢永俊,彭霞,黄舟,等.2017.基于微博数据的北京市热点区域意象感知[J].地理科学进展,36(9):1099-1110.[Xie Y J,Peng X,Huang Z,et al.2017.Image perception of Beijing's regional hotspots based on microblog data[J].Progress in Geography,36(9):1099-1110.]
杨微石,郭旦怀,逯燕玲,等.2017.基于大数据的文化遗产认知分析方法:以北京旧城中轴线为例[J].地理科学进展,36(9):1111-1118.[Yang W S,Guo D H,Lu Y L,et al.2017.Analyzing perception of cultural heritage sites based on big data:A case study of Beijing Central Axis[J].Progress in Geography,36(9):1111-1118.]
杨振山,龙瀛,Douay N.2015.大数据对人文:经济地理学研究的促进与局限[J].地理科学进展,34(4):410-417.[Yang Z S,Long Y,Douay N.2015.Opportunities and limitations of big data applications to human and economic geography:The state of the art[J].Progress in Geography,34(4):410-417.]
易峥,李继珍,冷炳荣,等.2017.基于微博语义分析的重庆主城区风貌感知评价[J].地理科学进展,36(9):1058-1066.[Yi Z,Li J Z,Leng B R,et al.2017.Perception and evaluation of cityscape characteristics using semantic analysis on microblog in the main urban area of Chongqing Municipality[J].Progress in Geography,36(9):1058-1066.]
尹芹,孟斌,张丽英.2016.基于客流特征的北京地铁站点类型识别[J].地理科学进展,35(1):126-134.[Yin Q,Meng B,Zhang L Y.2016.Classification of subway stations in Beijing based on passenger flow characteristics[J].Progress in Geography,35(1):126-134.]
张文忠,李业锦.2006.北京城市居民消费区位偏好与决策行为分析:以西城区和海淀中心地区为例[J].地理学报61(10):1037-1045.[Zhang W Z,Li Y J.2006.Study on residents'shopping location preference and shopping location choice:A case on Xicheng and Haidian districts of Beijing[J].Acta Geographica Sinica.61(10):1037-1045.]
甄峰,席广亮,秦萧.2015.基于地理视角的智慧城市规划与建设的理论思考[J].地理科学进展,34(4):402-409[Zhen F,Xi G L,Qin X.2015.Smart city planning and construction based on geographic perspectives:Some theoretical thinking[J].Progress in Geography,34(4):402-409.]
钟炜菁,王德,谢栋灿,等.2017.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,36(5):972-984.[Zhong W J,Wang D,Xie D C,e al.2017.Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J].Geographical Research,36(5):972-984.]
朱玮,王德.2008.南京东路消费者的空间选择行为与回游轨迹[J].城市规划,(3):33-40.[Zhu W,Wang D.2008Space choice behavior and multi-stop tracks of consumers in east Nanjing road[J].City Planning Review.(3):33-40.]
Bodenhofer U,Kothmeier A,Hochreiter S.2011.APCluster An R package for affinity propagation clustering[J].Bioinformatics,27(17):2463-2464.
Calabrese F,Colonna M,Lovisolo P,et al.2011.Real-time urban monitoring using cell phones:A case study in Rome[J].IEEE Transactions on Intelligent Transportation Systems,12(1):141-151.
Calabrese F,Reades J,Ratti C.2010.Eigenplaces:Segmenting space through digital signatures[J].IEEE Pervasive Computing,9(1):78-84.
Clark C.1951.Urban population densities[J].Journal of the Royal Statistical Society:Series A,114(4):490-496.
Frey B J,Dueck D.2007.Clustering by passing messages between data points[J].Science,315:972-976.
Gollege R G.1997.Spatial behavoir:A geographical perspective[M].New York:Guilford Press.
Hagerstrand T.1970.What about people in regional science?[J].Papers in Regional Science,24(1):7-24.
Iovan C,Olteanu-Raimond A M,CouronnéT,et al.2013.Moving and calling:Mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies[M]//Vandenbroucke D,Bucher B,Crompvoets J.Geographic Information Science at the Heart of Europe.Berlin,Germany:Springer:247-265.
Liu Y,Liu X,Gao S,et al.2015.Social sensing:A new approach to understanding our socioeconomic environments[J].Annals of the Association of American Geographers,105(3):512-530.
Pei T,Sobolevsky S,Ratti C,et al.2014.A new insight into land use classification based on aggregated mobile phone data[J].International Journal of Geographical Information Science,28(9):1988-2007.
Reades J,Calabrese F,Sevtsuk A,et al.2007.Cellular census:Explorations in urban data collection[J].IEEE Pervasive Computing,6(3):30-38.
Rubio A,Sanchez A,Frias-Martinez E.2013.Adaptive nonparametric identification of dense areas using cell phone records for urban analysis[J].Engineering Applications of Artificial Intelligence,26(1):551-563.
Sevtsuk A,Ratti C.Explorations into urban mobility patterns using aggregate mobile network data[R].MIT Portugal Program,2008.
Vajakas T,Vajakas J,Lillemets R.2015.Trajectory reconstruction from mobile positioning data using cell-to-cell travel time information[J].International Journal of Geographical Information Science,29(11):1941-1954.
Yang X,Fang Z,Xu Y,et al.2016.Understanding spatiotemporal patterns of human convergence and divergence using mobile phone location data[J].ISPRS International Journal of Geo-Information,5(10):177.doi:10.3390/ijgi5100177.
(1)根据《上海市统计年鉴(2015)》,上海全市年末常住人口为2425.68万人。
(2)根据《上海统计年鉴2014》,上海2013年移动电话普及率为132.5%。
(3)土地利用图(中)来源于上海市土地利用现状图(2014年),其他笔者自绘。
(4)现实中,基站的蜂窝服务范围并不是理想中边界清晰、易于划分的六边形,而是存在重叠,如果在一定区域内基站信号发生剧烈变化,就可能导致手机在不同基站之间频繁切换,即乒乓切换现象。
(5)在核密度分析进行可视化时,考虑显示效果,采用100 m×100 m的栅格单元。
(6)由于本次数据的自动更新周期普遍为两小时,以两小时为时间单元,可避免因自动更新带来的数据误差。
(7)本文中的空间核密度图,如无特别说明,都采用分位数的等级划分标准进行等级划分。
(8)研究以2个小时为时间单元,对一天中的12个时间单元都分别计算了平日记录量和周末记录量的比值进行比较。结果显示,早上10-12点和下午14-16点时段的空间分布较为显示,都呈现出就业中心比值高特征。因此研究选择了以下午时段为代表在文中进行具体阐述。
(9)由于土地利用现状(2014年)数据采用的是国家《城市用地分类与规划建设用地标准GBJ137-90》用地分类标准,因此本研究中也使用这一分类标准。
柴彦威,沈洁.2008.基于活动分析法的人类空间行为研究[J].地理科学,28(5):594-600.[Chai Y W,Shen J.2008Acitivity-based approach to human spatial behavior research[J].Scientia Geographica Sinica,28(5):594-600.]
柴彦威,塔娜.2013.中国时空间行为研究进展[J].地理科学进展,32(9):1362-1373.[Chai Y W,Ta N.2013.Progress in space-time behavior research in China[J].Progress in Geography,32(9):1362-1373.]
龚玺,裴韬,孙嘉,等.2011.时空轨迹聚类方法研究进展[J]地理科学进展,30(5):522-534.[Gong X,Pei T,Sun J,e al.2011.Review of the research progresses in trajectory clustering methods[J].Progress in Geography,30(5):522-534.]
郭璨,甄峰,朱寿佳.2014.智能手机定位数据应用于城市研究的进展与展望[J].人文地理,29(6):18-23.[Guo CZhen F,Zhu S J.2014.Progress and prospect of the application of smart phone LBS data in urban researches[J].Human Geography,29(6):18-23.]
钮心毅,丁亮,宋小冬.2014.基于手机数据识别上海中心城的城市空间结构[J].城市规划学刊,(6):61-67.[Niu X YDing L,Song X D.2014.Understanding urban spatia structure of Shanghai central city based on mobile phone data[J].Urban Planning Forum,(6):61-67.]
秦萧,甄峰,熊丽芳,等.2013.大数据时代城市时空间行为研究方法[J].地理科学进展,32(9):1352-1361.[Qin XZhen F,Xiong L F,et al.2013.Methods in urban tempora and spatial behavior research in the Big Data Era[J].Progress in Geography,32(9):1352-1361.]
宋辞,裴韬.2012.基于特征的时间序列聚类方法研究进展[J].地理科学进展,31(10):1307-1317.[Song C,Pei T2012.Research progress in time series clustering methods based on characteristics[J].Progress in Geography,31(10)1307-1317.]
王德,王灿,谢栋灿,等.2015.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,(3):51-61.[Wang D,Wang C,Xie D C,et al.2015.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,(3):51-61.]
王德,钟炜菁,谢栋灿,等.2015.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[J].城市规划学刊,(5):82-90.[Wang D,Zhong W J,Xie D C,et al.2015.The application of cell phone signaling data in the assessment of urban built environment:A case study of Baoshan district in Shanghai[J].Urban Planning Forum,(5):82-90.]
吴康敏,张虹鸥,王洋,等.2016.广州市多类型商业中心识别与空间模式[J].地理科学进展,35(8):963-974.[Wu KM,Zhang H O,Wang Y,et al.2016.Identify of the multiple types of commercial center in Guangzhou and its spatial pattern[J].Progress in Geography,35(8):963-974.]
谢永俊,彭霞,黄舟,等.2017.基于微博数据的北京市热点区域意象感知[J].地理科学进展,36(9):1099-1110.[Xie Y J,Peng X,Huang Z,et al.2017.Image perception of Beijing's regional hotspots based on microblog data[J].Progress in Geography,36(9):1099-1110.]
杨微石,郭旦怀,逯燕玲,等.2017.基于大数据的文化遗产认知分析方法:以北京旧城中轴线为例[J].地理科学进展,36(9):1111-1118.[Yang W S,Guo D H,Lu Y L,et al.2017.Analyzing perception of cultural heritage sites based on big data:A case study of Beijing Central Axis[J].Progress in Geography,36(9):1111-1118.]
杨振山,龙瀛,Douay N.2015.大数据对人文:经济地理学研究的促进与局限[J].地理科学进展,34(4):410-417.[Yang Z S,Long Y,Douay N.2015.Opportunities and limitations of big data applications to human and economic geography:The state of the art[J].Progress in Geography,34(4):410-417.]
易峥,李继珍,冷炳荣,等.2017.基于微博语义分析的重庆主城区风貌感知评价[J].地理科学进展,36(9):1058-1066.[Yi Z,Li J Z,Leng B R,et al.2017.Perception and evaluation of cityscape characteristics using semantic analysis on microblog in the main urban area of Chongqing Municipality[J].Progress in Geography,36(9):1058-1066.]
尹芹,孟斌,张丽英.2016.基于客流特征的北京地铁站点类型识别[J].地理科学进展,35(1):126-134.[Yin Q,Meng B,Zhang L Y.2016.Classification of subway stations in Beijing based on passenger flow characteristics[J].Progress in Geography,35(1):126-134.]
张文忠,李业锦.2006.北京城市居民消费区位偏好与决策行为分析:以西城区和海淀中心地区为例[J].地理学报61(10):1037-1045.[Zhang W Z,Li Y J.2006.Study on residents'shopping location preference and shopping location choice:A case on Xicheng and Haidian districts of Beijing[J].Acta Geographica Sinica.61(10):1037-1045.]
甄峰,席广亮,秦萧.2015.基于地理视角的智慧城市规划与建设的理论思考[J].地理科学进展,34(4):402-409[Zhen F,Xi G L,Qin X.2015.Smart city planning and construction based on geographic perspectives:Some theoretical thinking[J].Progress in Geography,34(4):402-409.]
钟炜菁,王德,谢栋灿,等.2017.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,36(5):972-984.[Zhong W J,Wang D,Xie D C,e al.2017.Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J].Geographical Research,36(5):972-984.]
朱玮,王德.2008.南京东路消费者的空间选择行为与回游轨迹[J].城市规划,(3):33-40.[Zhu W,Wang D.2008Space choice behavior and multi-stop tracks of consumers in east Nanjing road[J].City Planning Review.(3):33-40.]
Bodenhofer U,Kothmeier A,Hochreiter S.2011.APCluster An R package for affinity propagation clustering[J].Bioinformatics,27(17):2463-2464.
Calabrese F,Colonna M,Lovisolo P,et al.2011.Real-time urban monitoring using cell phones:A case study in Rome[J].IEEE Transactions on Intelligent Transportation Systems,12(1):141-151.
Calabrese F,Reades J,Ratti C.2010.Eigenplaces:Segmenting space through digital signatures[J].IEEE Pervasive Computing,9(1):78-84.
Clark C.1951.Urban population densities[J].Journal of the Royal Statistical Society:Series A,114(4):490-496.
Frey B J,Dueck D.2007.Clustering by passing messages between data points[J].Science,315:972-976.
Gollege R G.1997.Spatial behavoir:A geographical perspective[M].New York:Guilford Press.
Hagerstrand T.1970.What about people in regional science?[J].Papers in Regional Science,24(1):7-24.
Iovan C,Olteanu-Raimond A M,CouronnéT,et al.2013.Moving and calling:Mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies[M]//Vandenbroucke D,Bucher B,Crompvoets J.Geographic Information Science at the Heart of Europe.Berlin,Germany:Springer:247-265.
Liu Y,Liu X,Gao S,et al.2015.Social sensing:A new approach to understanding our socioeconomic environments[J].Annals of the Association of American Geographers,105(3):512-530.
Pei T,Sobolevsky S,Ratti C,et al.2014.A new insight into land use classification based on aggregated mobile phone data[J].International Journal of Geographical Information Science,28(9):1988-2007.
Reades J,Calabrese F,Sevtsuk A,et al.2007.Cellular census:Explorations in urban data collection[J].IEEE Pervasive Computing,6(3):30-38.
Rubio A,Sanchez A,Frias-Martinez E.2013.Adaptive nonparametric identification of dense areas using cell phone records for urban analysis[J].Engineering Applications of Artificial Intelligence,26(1):551-563.
Sevtsuk A,Ratti C.Explorations into urban mobility patterns using aggregate mobile network data[R].MIT Portugal Program,2008.
Vajakas T,Vajakas J,Lillemets R.2015.Trajectory reconstruction from mobile positioning data using cell-to-cell travel time information[J].International Journal of Geographical Information Science,29(11):1941-1954.
Yang X,Fang Z,Xu Y,et al.2016.Understanding spatiotemporal patterns of human convergence and divergence using mobile phone location data[J].ISPRS International Journal of Geo-Information,5(10):177.doi:10.3390/ijgi5100177.
(1)根据《上海市统计年鉴(2015)》,上海全市年末常住人口为2425.68万人。
(2)根据《上海统计年鉴2014》,上海2013年移动电话普及率为132.5%。
(3)土地利用图(中)来源于上海市土地利用现状图(2014年),其他笔者自绘。
(4)现实中,基站的蜂窝服务范围并不是理想中边界清晰、易于划分的六边形,而是存在重叠,如果在一定区域内基站信号发生剧烈变化,就可能导致手机在不同基站之间频繁切换,即乒乓切换现象。
(5)在核密度分析进行可视化时,考虑显示效果,采用100 m×100 m的栅格单元。
(6)由于本次数据的自动更新周期普遍为两小时,以两小时为时间单元,可避免因自动更新带来的数据误差。
(7)本文中的空间核密度图,如无特别说明,都采用分位数的等级划分标准进行等级划分。
(8)研究以2个小时为时间单元,对一天中的12个时间单元都分别计算了平日记录量和周末记录量的比值进行比较。结果显示,早上10-12点和下午14-16点时段的空间分布较为显示,都呈现出就业中心比值高特征。因此研究选择了以下午时段为代表在文中进行具体阐述。
(9)由于土地利用现状(2014年)数据采用的是国家《城市用地分类与规划建设用地标准GBJ137-90》用地分类标准,因此本研究中也使用这一分类标准。