• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1307 (2020)
Fangmiao CHEN1、1, Huiping HUANG1、1、2、2、*, and Kun JIA3、3
Author Affiliations
  • 1. 中国科学院空天信息创新研究院,北京 100094
  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2. 中国科学院大学资源与环境学院,北京 100049
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. 北京师范大学地理科学学部,北京 100875
  • 3Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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    DOI: 10.12082/dqxxkx.2020.190524 Cite this Article
    Fangmiao CHEN, Huiping HUANG, Kun JIA. Study on the Administration and Construction of Urban Agglomeration with Spatiotemporal Big Data: A Progress Review[J]. Journal of Geo-information Science, 2020, 22(6): 1307 Copy Citation Text show less
    References

    [1] [M]. The polycentric metropolis: Learning from mega-city regions in Europe, 91-125(2006).

    [2] . Thirty years of exploration on innovation clusters(2013).

    [3] Regions, spatial strategies and sustainable development[M]. London: Rout-ledge, 200-212(2004).

    [4] The E-Society[M]. New York: Nova Science Publishers(2011).

    [5] Telecommunications and the city: Electronic spaces, urban places[M]. London: Rout-ledge(1996).

    [6] Demand for information and communication technology-based services and regional economic development[J]. Papers in Regional Science, 82, 27-50(2003).

    [7] Big data and human geography: Opportunities, challenges and risks[J]. Dialogues in Human Geography, 3, 274-279(2013).

    [8] Featured graphic, mapping the geoweb: A geography of Twitter[J]. Environment and Planning A, 44, 100-102(2013).

    [9] Citizens as sensors: Web 2.0 and the volunteering of geographic information[J]. GeoFocus, 8-10(2007).

    [10] Web mapping 2.0: The neogeography of the geoweb[J]. Geography Compass, 2, 2011-2039(2008).

    [11] ePlanning: A snapshot of the literature on using the world wide web in urban planning[J]. Journal of Planning Literature, 17, 227-245(2002).

    [12] Location based services: New challenges for planning and public administration[J]. Futures, 37, 547-561(2005).

    [13] 龙瀛, 崔承印, 茅明睿, 等. 大数据时代的精细化城市模拟: 方法、数据、案例和框架[J]. 人文地理, 2014,29(3):7-13. [ LongY, Cui CY, Mao MR, et al. Fine-scale urban modeling and its opportunities in the“big data”era: Methods, data and empirical studies[J]. Human Geography, 2014,29(3):7-13. ] [ Long Y, Cui C Y, Mao M R, et al. Fine-scale urban modeling and its opportunities in the“big data”era: Methods, data and empirical studies[J]. Human Geography, 2014,29(3):7-13. ]

    [14] et alInternet of things architecture: recent advances, taxonomy, requirements, and open challenges[J]. IEEE Wireless Communications, 24, 10-16(2017).

    [15] et alThe role of big data analytic in internet of things[J]. Computer Networks, 459-471(2017).

    [16] et alThe role of big data in smart city[J]. International of Journal of Information Management, 36, 748-758(2016).

    [17] et alBig data: From beginning to future[J]. International of Journal of Information Management, 36, 1231-1247(2016).

    [18] et alBig data and big cities: The promises and limitations of improved measures of urban life[J]. Economic Inquiry, 56, 114-137(2018).

    [19] Data-driven geography[J]. GeoJournal, 80, 449-461(2015).

    [20] [M]. The new science of cities(2013).

    [21] 黄慧萍, 李强子. 大数据时代土地利用优化的机遇、数据源及潜在应用[J]. 中国土地科学, 2017,31(7):74-82. [ Huang HP, Li QZ. Opportunities, data sources, and potential applications of land use optimization in the big data era[J]. China Land Sciences, 2017,31(7):74-82. ] [ Huang H P, Li Q Z. Opportunities, data sources, and potential applications of land use optimization in the big data era[J]. China Land Sciences, 2017,31(7):74-82. ]

    [22] 陈曈, 朱志慧. 大数据技术的发展情况综述[J].福建电脑,2017(3):1-4. [ ChenT, Zhu ZH. Overview of the development of big data technology[J]. Fujian Computer, 2017(3):1-4. ] [ Chen T, Zhu Z H. Overview of the development of big data technology[J]. Fujian Computer, 2017(3):1-4. ]

    [23] 崔振. 云计算技术下海量数据挖掘的实现机制[J]. 微型电脑应用, 2019,35(4):129-131. [ CuiZ. Realization mechanism of massive data mining under cloud computing technology[J]. Microcomputer Applications, 2019,35(4):129-131. ] [ Cui Z. Realization mechanism of massive data mining under cloud computing technology[J]. Microcomputer Applications, 2019,35(4):129-131. ]

    [24] A survey of temporal data mining[J]. Sadhana, 31, 173-198(2006).

    [25] A review on time series data mining[J]. Engineering applications of artificial intelligence, 24, 164-181(2011).

    [26] Spatial data mining and geographic knowledge discovery: An introduction[J]. Computers, Environment and Urban Systems, 33, 403-408(2009).

    [27] 吉根林, 赵斌. 面向大数据的时空数据挖掘综述[J]. 南京大学报(自然科学版), 2014,37(1):1-7. [ Ji GL, ZhaoB. A survey of spatiotemporal data mining for big data[J]. Journal of Nanjing University(NaturalScience Edition), 2014,37(1):1-7. ] [ Ji G L, Zhao B. A survey of spatiotemporal data mining for big data[J]. Journal of Nanjing University(NaturalScience Edition), 2014,37(1):1-7. ]

    [28] et alRemote sensing of the urban heat island effect across bio mes in the continental USA[J]. Remote Sensing of Environment, 114, 504-513(2010).

    [29] Land use land cover changes in detection of water quality: A study based on remote sensing and multivariate statistics[J]. Journal of Environmental and Public Health, 2017, 1-12(2017).

    [30] GIS and a remote sensing based approach for urban flood-plain mapping for the Tapi catchment, India[C]. Hydroinformatics in Hydrology, Hydrogeology & Water Resources. India: IAHS Publ(2009).

    [31] et alSpatiotemporal anomaly selection through visual analysis of geolocated twitter messages[C]. 2012 IEEE Pacific Visualization Symposium(2012).

    [32] Discovering regions of different functions in a city using human mobility and POIs[C]. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 186-194(2012).

    [33] et alOvercoming the key challenges to establishing vehicular communication: Is SDN the answer?[J]. IEEE Communications Magazine, 55, 128-134(2017).

    [34] Accuracy assessment of land use/land cover classification using remote sensing and GIS[J]. International Journal of Geosciences, 8, 611-622(2017).

    [35] et alThe big data of violent events: Algorithms for association analysis using spatio-temporal storytelling[J]. GeoInformation, 20, 879-921(2016).

    [36] et alTourists' digital footprint in cities: comparing big data sources[J]. Tourism Management, 66, 13-25(2018).

    [37] From Twitter to detector: real-time traffic incident detection using social media data[J]. Transportation Research Part C, 67, 321-342(2016).

    [38] et alThe geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place[J]. PloS one, 8, e64417(2013).

    [39] et alRecognizing city identity via attribute analysis of geo-tagged images[C]. European Conference on Computer Vision, 519-534(2014).

    [40] et alModelling the scaling properties of human mobility[J]. Nature Physics, 6, 818-823(2010).

    [41] . Big data mining and application(2017).

    [42] Uneven growth of urban clusters in megaregions and its policy implications for new urbanization in China[J]. Land Use Policy, 66, 72-29(2017).

    [43] The form study on urban spatial expansion and development in Pearl River Delta economy area based on the survey by remote sensing. Guangzhou: Guangzhou Institute of Geochemistry, Chinese Academy of Science(2004).

    [44] Research and analysis on Urumqi urban agglomeration land use dynamic changes. Urumqi: Xinjiang: Xinjiang University(2015).

    [45] Remote sensing monitoring and forecasting the urbanization of Hubaoe urban agglomerations. Huhhot: Inner Mongolia: Inner Mongolia Normal University(2010).

    [46] Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime images[J]. Annals of the Association of American Geographers, 92, 225-240(2002).

    [47] 孟祥玉. 基于多源数据京津冀城市群边界识别研究[D].北京:中国地质大学(北京), 2017. [ Meng XY. Research on boundary recognition of Beijing-Tianjin-Hebei urban agglomeration based on multivariate data[D]. Beijing: China University of Geosciences, 2017. ] [ Meng X Y. Research on boundary recognition of Beijing-Tianjin-Hebei urban agglomeration based on multivariate dataD]. Beijing: China University of Geosciences, 2017. ]

    [48] Spatial cluster analysis of urban landscape pattern using stable nighttime Light satellite images. Shanghai: East China Normal University(2013).

    [49] 昌亭, 吴绍华. 长三角城市群地域扩张的时空特征—基于“近十年来DSMP/OLS夜间灯光数据”的实证分析[J].现代城市研究,2014(7):67-73. [ ChangT, Wu SH. Spatial-temporal characteristics of Yangtze River Delta urban agglomeration's geographic expansion in recent decades: an empirical study based on DMSP/OLS light data[J]. Modern Urban Research, 2014(7):67-73. ] [ Chang T, Wu S H. Spatial-temporal characteristics of Yangtze River Delta urban agglomeration's geographic expansion in recent decades: an empirical study based on DMSP/OLS light data[J]. Modern Urban Research, 2014(7):67-73. ]

    [50] 何春阳, 李景刚, 陈婧. 基于夜间灯光数据的环渤海地区城市化空间模式和过程研究[J]. 地理学报(英文版), 2005,60(3):32-38. [ He CY, Li JG, ChenJ. The urbanization process of Bohai rim in the 1990s by using DMSP/OLS data[J]. Journal of Geographical Sciences, 2005,60(3):32-38. ] [ He C Y, Li J G, Chen J. The urbanization process of Bohai rim in the 1990s by using DMSP/OLS data[J]. Journal of Geographical Sciences, 2005,60(3):32-38. ]

    [51] Analysis of road network using remote sensing and GIS data nainital district(uttarakhand)[J]. International Journal For Innovative Research in Multidisciplinary Field, 3, 122-126(2017).

    [52] Spatial linkage and urban expansion: An urban agglomeration perspective[J]. Progress in Geography, 35, 1177-1185(2016).

    [53] 李健, 谷正气, 张勇. 高分遥感影像的城市群路网监测原型系统开发[J]. 地理空间信息, 2015,13(2):51-54. [ LiJ, Gu ZQ, ZhangY. Prototyping system development of the road network in city-clusters based high resolution remote sensing Images[J]. Geospatial Information, 2015,13(2):51-54. ] [ Li J, Gu Z Q, Zhang Y. Prototyping system development of the road network in city-clusters based high resolution remote sensing Images[J]. Geospatial Information, 2015,13(2):51-54. ]

    [54] The urban agglomeration road network evaluation research and system implementation based on HRRS and GIS. Zhuzhou: Hunan University of Technology(2015).

    [55] 龙瀛, 张宇, 崔承印. 利用公交卡刷卡数据分析北京职住关系和通勤交通出行[J]. 地理学报, 2012,67(10):1339-1352. [ LongY, ZhangY, Cui CY. Identifying commuting pattern of beijing using bus smart card data[J]. Acta Geographica Sinica, 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. ]

    [56] 天津市城市规划设计研究院数字规划技术研究中心. 城市厚数据建设手册V1.2[R]. 天津:天津市规划设计研究院, 2016. [ Digital Planning Research Center, Tianjin Uban Plannign & Design Institute. Urban big data construction V1.2[R]. Tianjin: Tianjin Uban Plannign & Design Institute, 2016. ] [ Digital Planning Research Center, Tianjin Uban Plannign & Design Institute. Urban big data construction V1.2[R]. Tianjin: Tianjin Uban Plannign & Design Institute, 2016. ]

    [57] Research of urban network of Yangtze River Delta region based on railway network. Hangzhou: Zhejiang Normal University(2015).

    [58] 傅毅明, 赵彦云. 基于公路交通流的城市群关联网络研究——以京津冀城市群为例[J]. 河北大学学报(哲学社会科学版), 2016,41(4):91-100. [ Fu MY, Zhao YY. Research on the association network of urban agglomeration based on highway traffic flow taking Beijing-Tianjin-Hebei urban agglomeration as an example[J]. Journal of Hebei University(Philosophy and Social Science), 2016,41(4):91-100. ] [ Fu M Y, Zhao Y Y. Research on the association network of urban agglomeration based on highway traffic flow taking Beijing-Tianjin-Hebei urban agglomeration as an example[J]. Journal of Hebei University(Philosophy and Social Science), 2016,41(4):91-100. ]

    [59] 王海江, 苗长虹, 李欣欣. 流视角下中国铁路交通联系空间模拟与格局解析[J]. 经济地理, 2019,39(1):29-36. [ Wang HJ, MiaoCH, Li XX. Spatial simulation and pattern analysis of China's railway transport contacts from the perspective of flows[J]. Economic Geography, 2019,39(1):29-36. ] [ Wang H J,Miao CH, Li X X.Spatial simulation and pattern analysis of China's railway transport contacts from the perspective of flows[J]. Economic Geography, 2019,39(1):29-36. ]

    [60] 陈斌. 珠三角城市群交通协调发展战略及空间规划实践[C]. 2017年中国城市交通规划年会论文集, 2017. [ ChenB. Traffic coordinated development strategy and spatial planning practice in Pearl River Delta urban agglomeration[C]. Urban traffic planning of China, 2017. ] [ Chen B. Traffic coordinated development strategy and spatial planning practice in Pearl River Delta urban agglomeration[C]. Urban traffic planning of China, 2017. ]

    [61] 董志国, 刘红杏, 吴冠中, 等. 基于手机大数据的城市群空间特征研究——以珠三角为例[J].交通与运输,2017(5):32-34. [ Dong ZG, Liu HX, Wu GZ, et al. Research on spacial characteristics of urban agglomeration based on mobile phone big data:a study in Pearl River Delta[J]. Traffic & Transportation, 2017(5):32-34. ] [ Dong Z G, Liu H X, Wu G Z, et al. Research on spacial characteristics of urban agglomeration based on mobile phone big data:a study in Pearl River Delta[J]. Traffic & Transportation, 2017(5):32-34. ]

    [62] 周永杰, 刘洁贞, 朱锦丰, 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 YJ, Liu JZ, Zhu JF, et al. Research on the spatial characteristics of Pearl River Delta urban agglomeration based on mobile signaling data[J]. Planners, 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. ]

    [63] 刘望保, 石恩明. 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 WB, Shi EM. Spatial pattern of population daily flow among cities based on ICT: A case study of "Baidu Migration"[J]. Acta Geographica Sinica, 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]. Acta Geographica Sinica, 2016,71(10):1667-1679. ]

    [64] 叶强, 张俪璇, 彭鹏, 等. 基于百度迁徙数据的长江中游城市群网络特征研究[J]. 经济地理, 2017,37(8):53-59. [ YeQ, Zhang LX, PengP, et al. The network characteristics of urban agglomerations in the middle reaches of the Yangtze River based on baidu migration data[J]. Economic Geography, 2017,37(8):53-59. ] [ Ye Q, Zhang L X, Peng P, et al. The network characteristics of urban agglomerations in the middle reaches of the Yangtze River based on baidu migration data[J]. Economic Geography, 2017,37(8):53-59. ]

    [65] A study on space-time characteristics and scope delimitation of residents' activities in Beijing-Tianjin-Hebei urban agglomeration based on the check-in data of Sina micro-blog. Wuhan: Wuhan University(2017).

    [66] 甄峰, 王波, 陈映雪. 基于网络社会空间的中国城市网络特征——以新浪微博为例[J]. 地理学报, 2012,67(8):1031-1043. [ ZhenF, WangB, Chen YX. China's city network characteristics based on social network space: An empirical analysis of Sina micro-blog[J]. Scientia Geographica Sinica, 2012,67(8):1031-1043. ] [ Zhen F, Wang B, Chen Y X. China's city network characteristics based on social network space: An empirical analysis of Sina micro-blog[J]. Scientia Geographica Sinica, 2012,67(8):1031-1043. ]

    [67] 蒋大亮, 孙烨, 任航, 等. 基于百度指数的长江中游城市群城市网络特征研究[J]. 长江流域资源与环境, 2015,24(10):1654-1664. [ Jiang DL, SunY, RenH, et al. Analyses on the city network characteristics of middle Yangtze urban agglomeration based on Baidu index[J]. Resources and Environment in the Yangtze Basin, 2015,24(10):1654-1664. ] [ Jiang D L, Sun Y, Ren H, et al. Analyses on the city network characteristics of middle Yangtze urban agglomeration based on Baidu index[J]. Resources and Environment in the Yangtze Basin, 2015,24(10):1654-1664. ]

    [68] 张宏乔. 流空间视角下的城市网络特征分析——以中原城市群为例[J].资源开发与市场,2016(10):1219-1222. [ Zhang HQ. Research on city network of Zhongyuan urban agglomeration based on space of flows[J]. Resource Development&Market, 2016(10):1219-1222. ] [ Zhang H Q. Research on city network of Zhongyuan urban agglomeration based on space of flows[J]. Resource Development&Market, 2016(10):1219-1222. ]

    [69] 卢佳. 基于腾讯位置大数据的四大城市群内部空间联系格局特征研究[C]. 2017中国城市规划年会论文集, 2017. [ LuJ. Research on spacial association analysis of four urban agglomerations in China based on Tencent location data[C]. China Urban Planning Annual Symposium2017,2017. ] [ Lu J. Research on spacial association analysis of four urban agglomerations in China based on Tencent location data[C]. China Urban Planning Annual Symposium 2017, 2017. ]

    [70] 王贤文, 王虹茵, 李青纯. 基于地理位置大数据的京津冀城市群短期人口流动研究[J]. 大连理工大学学报(社会科学版), 2017,38(2):105-113. [ Wang XW, Wang HY, Li QC. Location based big data analysis of the short-term population flow of Beijing, Tianjin and Hebei urban agglomeration[J]. Journal of Dalian University of Technology(social Sciences), 2017,38(2):105-113. ] [ Wang X W, Wang H Y, Li Q C. Location based big data analysis of the short-term population flow of Beijing, Tianjin and Hebei urban agglomeration[J]. Journal of Dalian University of Technology(social Sciences), 2017,38(2):105-113. ]

    [71] 何志超, 郭青海, 杨一夫, 等. 基于POI 数据的厦漳泉同城化进展评估[J].规划师,2018(4):33-37. [ He ZC, Guo QH, Yang YF, et al. An evaluation of Xiamen-Zhangzhou-Quanzhou integrate development based on POI[J]. Planners, 2018(4):33-37. ] [ He Z C, Guo Q H, Yang Y F, et al. An evaluation of Xiamen-Zhangzhou-Quanzhou integrate development based on POI[J]. Planners, 2018(4):33-37. ]

    [72] 巫细波, 赖长强. 基于POI大数据的城市群功能空间结构特征研究——以粤港澳大湾区为例[J].城市观察,2019(3):44-55. [ Wu XB, Lai CQ. Study on spatial structure characteristics of urban agglomeration based on POI big data: Taking Guangdong-Hong Kong-Macao Greater Bay Area as an example[J]. Urban Insight, 2019(3):44-55. ] [ Wu X B, Lai C Q. Study on spatial structure characteristics of urban agglomeration based on POI big data: Taking Guangdong-Hong Kong-Macao Greater Bay Area as an example[J]. Urban Insight, 2019(3):44-55. ]

    [73] Big data mining and application based on logistics website. Shanghai: East China Normal University(2018).

    [74] 黄金川, 徐君, 黄艳.基于大数据的城市群空间联系网络研究——以京津冀协同区域为例[J].科技经济导刊, 2018, 26(3): 1, 2. [ Huang JC, XuJ, HuangY. Research on the spatial association network of urban agglomeration: A study in Beijing-Tianjin-Herbei region[J]. Technology and Economic Guide, 2018, 26(3): 1, 2. ] [ Huang J C, Xu J, Huang Y. Research on the spatial association network of urban agglomeration: A study in Beijing-Tianjin-Herbei region[J]. Technology and Economic Guide, 2018,26(3):1,2. ]

    [75] 李涛, 周锐, 苏海龙, 等. 长三角区域经济一体化水平的测度:以关系型大数据为基础[C]. 2015年中国城市规划年会, 2015. [ LiT, ZhouR, Su HL, et al. Evaluation of the Level of economic integration in Yangtze River Delta region: based on related big data[C]. China Urban Planning Annual Symposium2015,2015. ] [ Li T, Zhou R, Su H L, et al. Evaluation of the Level of economic integration in Yangtze River Delta region: based on related big data[C]. China Urban Planning Annual Symposium 2015, 2015. ]

    [76] Research on the cooperative effect between industrial cluster and urban agglomeration development. Shanghai: Shanghai Academy of Social Sciences(2016).

    [77] 王丽. 基于新增产业用地视角的京津冀地区经济特征分析[J].国土资源情报,2018(2):41-45. [ WangL. Analysis of the economic characteristics of Beijing-Tianjin-Hebei region based on the perspective of newly added industrial land use[J]. Land and Resources Information, 2018(2):41-45. ] [ Wang L. Analysis of the economic characteristics of Beijing-Tianjin-Hebei region based on the perspective of newly added industrial land use[J]. Land and Resources Information, 2018(2):41-45. ]

    [78] 沈静, 向澄, 柳意云. 广东省污染密集型产业转移机制—基于2000-2009年面板数据模型的实证[J]. 地理研究, 2012,31(2):357-368. [ ShenJ, XiangC, Liu YY. The mechanism of pollution-intensive industry relocation in Guangdong Province,2000-2009[J]. Geographical Research, 2012,31(2):357-368. ] [ Shen J, Xiang C, Liu Y Y. The mechanism of pollution-intensive industry relocation in Guangdong Province,2000-2009[J]. Geographical Research, 2012,31(2):357-368. ]

    [79] 张丽屏, 张翔. 创新产业聚集的影响因素分析—基于深圳、佛山企业大数据的空间分析[C].第十七届中国科技年会, 2015: 1-6. [ Zhang LP, ZhangX. The analysis of interfering factors of innovation industry clusters: Based on the spatial analysis of the industrial big data in Shenzhen and Foshan[C]. The 17 th China science and technology annual symposium , 2015: 1-6. ] [ Zhang L P, Zhang X. The analysis of interfering factors of innovation industry clusters: Based on the spatial analysis of the industrial big data in Shenzhen and Foshan[C]. The 17th China science and technology annual symposium, 2015:1-6. ]

    [80] 刘勍, 毛克彪, 马莹, 等. 基于农业大数据可视化方法的中国生猪空间流通模式[J]. 地理科学, 2017,37(1):118-124. [ LiuQ, Mao KB, MaY, et al. Pig's circulation pattern based on agricultural big data visualization method in China[J]. Scientia Geographica Sinica, 2017,37(1):118-124. ] [ Liu Q, Mao K B, Ma Y, et al. Pig's circulation pattern based on agricultural big data visualization method in China[J]. Scientia Geographica Sinica, 2017,37(1):118-124. ]

    [81] 陈阳, 朱郁郁. 基于企业大数据的长三角城市体系演化研究[C]. 2016中国城市规划年会,中国城市规划年会, 2016. [ ChenY, Zhu YY. Research on the urban system evolution of Yangtze River region based on enterprise big data[C]. China Urban Planning Annual Symposium2016,2016. ] [ Chen Y, Zhu Y Y. Research on the urban system evolution of Yangtze River region based on enterprise big data[C]. China Urban Planning Annual Symposium 2016, 2016. ]

    [82] Research on the co-development strategy of modern services in Beijing-Tianjin-Hebei region based on spatio-temporal big data. Wuhan: Central China Normal University(2017).

    [83] 周春艳, 厉青, 王中挺, 等. 2005-2014年京津冀对流层NO2柱浓度时空变化及影响因素[J]. 遥感学报, 2014,20(3):468-480. [ Zhou CY, LiQ, Wang ZT, et al. Spatio-temporal trend and changing factors of tropospheric NO2 column density in Beijing-Tianjin-Hebei region from 2005 to 2014[J]. Journal of Remote Sensing, 2014,20(3):468-480. ] 2柱浓度时空变化及影响因素[J].遥感学报,2014,20(3):468-480. [ Zhou C Y, Li Q, Wang Z T, et al.Spatio-temporal trend and changing factors of tropospheric NO2 column density in Beijing-Tianjin-Hebei region from 2005 to 2014[J]. Journal of Remote Sensing, 2014,20(3):468-480. ]

    [84] et alInvestigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology: A case study of Yangtze River Delta, China[J]. Urban Climate, 24, 485-502(2018).

    [85] 邓孺孺, 曾令初, 解学通, 等. 基于多源数据的城市群区水污染遥感反演——以珠江三角洲为例[J].中国科技成果,2013(14):28-30. [ Deng RR, Zeng LC, Xie XT, et al. The analysis of water pollution based on multi-data trough remote sensing: a study in Pearl River Delta[J]. China Science and Technology Achievements, 2013(14):28-30. ] [ Deng R R, Zeng L C, Xie X T, et al. The analysis of water pollution based on multi-data trough remote sensing: a study in Pearl River Delta[J]. China Science and Technology Achievements, 2013(14):28-30. ]

    [86] Urban stream deserts: Mapping a legacy of urbanization in the United States[J]. Applied Geography, 67, 129-139(2016).

    [87] 林金煌, 陈文惠, 祁新华, 等. 闽三角城市群生态系统格局演变及其驱动机制[J]. 生态学杂志, 2018,37(1):203-210. [ Lin JH, Chen WH, Qi XH, et al. Evolution pattern of ecosystem and its driving mechanism in urban agglomeration in Fujian Delta[J]. Journal of Ecology, 2018,37(1):203-210. ] [ Lin J H, Chen W H, Qi X H, et al. Evolution pattern of ecosystem and its driving mechanism in urban agglomeration in Fujian Delta[J]. Journal of Ecology, 2018,37(1):203-210. ]

    [88] 喻送霞, 杨波, 宾津佑, 等. 长株潭城市群土地资源承载力评价[J]. 中南林业科技大学学报(社会科学版), 2019,13(1):37-44. [ Yu SX, YangB, BinJY, et al.Evaluation of land resources carrying capacity of Chang-Zhu-Xiang agglomeration[J]. Journal of Central South University of Forestry & Technology (Social Sciences), 2019,13(1):37-44. ] [ Yu S X,Yang B, BinJ Y, et al.Evaluation of land resources carrying capacity of Chang-Zhu-Xiang agglomeration[J].Journal of Central South University of Forestry & Technology (Social Sciences), 2019,13(1):37-44. ]

    [89] 陈广东, 王继华, 田君慧. 遥感技术在中原城市群地质灾害危险性区划中的应用[J].低碳世界,2016(5):118-119. [ Chen GD, Wang JH, Tian JH. Application of remote sensing to the division of geography hazard risk in Central Henan urban agglomeration[J]. Low Carbon World, 2016(5):118-119. ] [ Chen G D, Wang J H, Tian J H. Application of remote sensing to the division of geography hazard risk in Central Henan urban agglomeration[J]. Low Carbon World, 2016(5):118-119. ]

    [90] The analysis of spatial conflict measurement in ChangZhutan Urban agglomeration based on ecological security. Changsha: Hunan Normal University(2011).

    [91] et alEstimating residential energy consumption in metropolitan areas: A microsimulation approach[J]. Energy, 155, 162-173(2018).

    [92] et alEmerging megaregions: A new spatial scale to explore urban sustainability[J]. Land Use Policy, 34, 353-366(2013).

    [93] How residential compactness and attractiveness can be shaped by environmental amenities in an industrial city?[J]. Sustainable Cities and Society, 41, 363-377(2018).

    [94] Externalities of auto traffic congestion growth: evidence from the residential property values in the US Great Lakes megaregion[J]. Journal of Transport Geography, 70, 131-140(2018).

    [95] et alU.S. metropolitan house price dynamics[J]. Journal of Urban Economics, 105, 54-69(2018).

    [96] et alEvaluation of energy-related household carbon footprints in metropolitan areas of Japan[J]. Ecological Modelling, 377, 16-25(2018).

    [97] 张丹丹, 李曼, 傅征博, 等. 城市群地质环境演化空间信息智能服务框架[J].测绘通报,2018(4):131-135. [ Zhang DD, LiM, Fu ZB, et al. Spatial information intelligent service framework for geological environment evolution of urban agglomeration[J]. Bulletin of Surveying and Mapping, 2018(4):131-135. ] [ Zhang D D, Li M, Fu Z B, et al. Spatial information intelligent service framework for geological environment evolution of urban agglomeration[J]. Bulletin of Surveying and Mapping, 2018(4):131-135. ]

    [98] 王国峰, 杜震洪, 何思明, 等. 基于时空大数据的交通灾害评估预警及服务关键技术[R]. 成都山地灾害与环境研究所, 2015. [ Wang GF, Du ZH, He SM, et al. The key technology of evaluation, risk- alert and service of traffic disaster based on spatio- temporal big data[R]. Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences,Chengdu, 2015. ] [ Wang G F, Du Z H, He S M, et al. The key technology of evaluation, risk- alert and service of traffic disaster based on spatio- temporal big data[R]. Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences, Chengdu, 2015. ]

    [99] et alMapping Urban Land Use by Using Landsat images and open social data[J]. Remote Sensing, 8, 151-169(2016).

    [100] Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study[J]. Applied Geography, 1-17(2018).

    [101] Cumulative attraction and spatial dependence in a destination choice model for beach recreation[J]. Tourism Management, 66, 318-328(2018).

    [102] et alEarthquake: Twitter as a distributed sensor system[J]. Transactions in GIS, 17, 124-147(2013).

    [103] et alMicroblogging during two natural hazards events: what twitter may contribute to situational awareness[C]. Proceedings of the SIGCHI conference on human factors in computing systems, ACM, 1079-1088(2010).

    [104] et alOMG earthquake! Can Twitter improve earthquake response?[J]. Seismological Research Letters, 81, 246-251(2010).

    [105] Earthquake shakes Twitter users: Real-time event detection by social sensors[C]. Proceedings of the 19th international conference on World wide web. ACM, 851-860(2010).

    [106] The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic[J]. PloS one, 6, e19467(2011).

    [107] Event detection using Twitter: A spatio-temporal approach[J]. PLoS One, 9, e97807(2014).

    [108] The word's technological capacity to store, communicate, and computer information[J]. Science, 332, 60-65(2011).

    Fangmiao CHEN, Huiping HUANG, Kun JIA. Study on the Administration and Construction of Urban Agglomeration with Spatiotemporal Big Data: A Progress Review[J]. Journal of Geo-information Science, 2020, 22(6): 1307
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