时空大数据在城市群建设与管理中的应用研究进展

doi:10.12082/dqxxkx.2020.190524

Abstract

Abstract:

With the development of the new type of urbanization, urban agglomeration plays a key role in modern social-economical development. To date, big data has been considered as a technological breakthrough and applied in many fields in recent years. Spatiotemporal big data mining and data fusion analysis can improve the efficiency of the administration and construction of cities/urban agglomeration in the new era of smart city. In this study, we aimed to review the types, acquisition methods, and analysis techniques of spatiotemporal big data. We investigated researches on urban agglomeration using spatiotemporal big data in order to identify the application of big data. We collected conference and journal articles as well as academic dissertations published in big data and data mining areas between 2004 and 2019. In total, wesummarized ten types of big data which were classified into traditionaltype and newtype categories, five big data acquisition methods including downloading, crawling, purchasing, and data processing, as well as seven most common big data analysis techniques. Five application fields on administration and construction of city/urban agglomeration using big data were concluded through literature review, including demarcation and spatial development monitoring, traffic network monitoring, association and function analysis, industrial coordination analysis, and environment assessment. Moreover, we summarized the bottlenecks of future big data applications, including: (1) difficulties in data management; (2) low-level data sharing; (3) high complexity of data analysis; (4) limitations of research ideas and application fields. In response to the above issues, some suggestions are listed: (1) government should strengthen policy support for more extensive information sharing and efficient information security assurance to create a favorable environment for the development of big data application; (2) constructing adaptable modern network infrastructure to create an all-in-one system which integrates network coordination, simulation, calculation, and administration; (3) building a big data management standard for urban agglomeration to solve the problems triggered by its characteristic of variability and multiformity; (4) promoting the establishment of big data platform to enforce an integrated information sharing mode from regional to national level; (5) adopting the international advanced technologies and methods with some new ideas from the studies on smart cities to build a technical system that is suitable in China. This study finally put forward that the construction of network infrastructure and spatiotemporal big data resources sharing platform could make new patterns of integrated analysis of big data possible, leading to highly effective supervision and strategical development of the city/urban agglomerations in the future.

Key words: urban agglomeration administration and construction, spatiotemporal big data, types of big data, data mining, big data technology and application, integrated analysis, research progress

CHEN Fangmiao, HUANG Huiping, JIA Kun. 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): 1307-1319.DOI:10.12082/dqxxkx.2020.190524

Figures/Tables 5

Tab. 1

Types of big data in the administration of urban agglomeration"

类型
传统数据	基础地理数据	地貌、水系、植被等基础地理信息数据;居住地、交通境界、特殊地物地名等要素信息
	台站观测数据	中国科学院、水利部、农业农村部、生态环境部、自然资源部、国家林业局等部门等建立野外观测台站数据,如对农田、森林、草地、荒漠、沼泽、水体等生态系统以及生态站碳氮水通量等观测数据
	社会经济统计数据	年鉴、普查中的城市人口和劳动力统计资料、城市经济发展主要指标统计资料、城市社会发展主要统计资料、城市环境与基础设施资料等
新型数据	遥感数据产品	多源遥感数据、遥感产品如土地覆盖、土地利用数据,各类遥感指数如植被指数、水体指数、建筑指数、不透水面指数、裸土指数、亮温及湿度指数等
	个体时空定位数据	手机信令数据、其他具有全球定位功能的个人终端数据
	APP兴趣与消费数据	网购支付（淘宝、京东、亚马逊、当当等）;生活消费类主题网站（携程、大众点评、58同城、赶集网、链家网等）数据
	电子地图数据	电子地图提供商提供的POI信息、用户使用电子地图时获取到的有关用户位置等信息,如百度、高德、谷歌、搜狗、凯立德、天翼等地图服务产品
	社交应用网络数据	微信、微博、QQ空间、人人网、Twitter、facebook、Flikr等社交网络数据
	智能交通数据	智能交通设备和系统,如智能公交、电子警察、交通信号控制、卡口、交通视频监控、出租车信息服务管理、城市客运枢纽信息化、GPS与警用系统、交通信息采集与发布和交通指挥类平台等
	物联网传感数据	服务于城市治安、交通、生态环境质量管理等的城市传感器网络数据。城市管理方面包括货物流跟踪、环境监测、气象监测、城市路灯控制、城市安防监控、车辆监控调度等。生态方面包括温度、湿度、光照度、空气质量、噪音等物理数据,含氧量、二氧化碳等化学数据,细菌数和植被等生物数据

Tab. 1

Tab. 2

Tab. 3

Methods of spatiotemporal big data mining for urban agglomeration"

挖掘方法	内容	城市/城市群建设中的应用示例
遥感反演	对不同空间分辨率、时间分辨率、波段传感器的数据进行分析获取更多地表参量信息	可用于城市热岛效应分析和生态环境监测;水质综合评价和生态承载力分析;城市群生态质量和生态安全评价^[28,29,30]
时空聚类	指基于空间和时间相似度把具有相似行为的时空对象划分到同一组中,使组间差别尽量大,而组内差别尽量小	可进行精准功能区分类,如城市功能区、生态区等;识别异常区域,如生态功能退化区、受自然灾害影响区等^[31,32]
分类	指基于训练样本数据确定未知样本数据类别的过程	可用于土地覆盖/利用遥感分类以及特定土地覆盖类型的提取、网络爬取数据的分类等^[33,34]
关联分析	也称关联规则挖掘,指从大量数据中挖掘关联性、相关性,从而进一步提取不同事物间关系出现的规律和模式	可通过开展产业、用地、人口、功能之间的关联分析,提取相应的规则和知识,从而为土地调控、用地协同和功能疏解提供决策支持^[35,36,37]
机器学习	通过多层非线性变换对高复杂度数据建模的算法的合集,能够处理图像、声音、文本等多种数据	可用于在语言识别、图像分类及目标识别、人脸识别、视频分类和行为识别。在城市群规划中用于面向空间优化利用大数据的挖掘与知识发现技术^[23,38-39]
数据可视化算法	指运用计算机图形学和图像处理技术,将数据转换为图形或图像在屏幕上显示出来,并进行交互处理的方法	可将遥感和地理信息系统提供的抽象化数据可视化,用于城市群时空发展变化的监测^[40]
切片分析	指在给定的数据立方体的一个维度上进行的选择操作	可从时间、空间、功能区类别等不同维度对城市群区域土地优化利用大数据等常见时空大数据进行切片分析^[41]

Tab. 3

Fig. 1

Fig. 2

References 108

[1]	Hall P, Pain K. The polycentric metropolis: Learning from mega-city regions in Europe[M]. London: Routledge Press, 2006: 91-125.
[2]	王缉慈. 创新集群三十年探索之旅[M]. 北京: 科学出版社, 2013.
	[ Wang J C. Thirty years of exploration on innovation clusters[M]. Beijing: Science Press, 2013. ]
[3]	Counsell D, Haughton G. Regions, spatial strategies and sustainable development[M]. London: Rout-ledge, 2004: 200-212.
[4]	Loo B P Y. The E-Society[M]. New York: Nova Science Publishers, 2011.
[5]	Graham S, Marvin S. Telecommunications and the city: Electronic spaces, urban places[M]. London: Rout-ledge, 1996.
[6]	De Castro E A, Jensen-Butler C. Demand for information and communication technology-based services and regional economic development[J]. Papers in Regional Science, 2003,82(1):27-50. doi: 10.1007/s101100200115
[7]	Kitchin R. Big data and human geography: Opportunities, challenges and risks[J]. Dialogues in Human Geography, 2013,3(3):274-279. doi: 10.1177/2043820613513390 pmid: 29472982
[8]	Graham M, Stephes M, Hale S. Featured graphic, mapping the geoweb: A geography of Twitter[J]. Environment and Planning A, 2013,44:100-102.
[9]	Goodchild M F. Citizens as sensors: Web 2.0 and the volunteering of geographic information[J]. GeoFocus, 2007(7):8-10.
[10]	Haklay M, Singleton A, Parker C. Web mapping 2.0: The neogeography of the geoweb[J]. Geography Compass, 2008,2(6):2011-2039. doi: 10.1111/geco.2008.2.issue-6
[11]	Budthimedhee K, Li J H, George R V. ePlanning: A snapshot of the literature on using the world wide web in urban planning[J]. Journal of Planning Literature, 2002,17(2):227-245. doi: 10.1177/088541202762475964
[12]	Ahas R, Mark U. Location based services: New challenges for planning and public administration[J]. Futures, 2005,37(6):547-561. doi: 10.1016/j.futures.2004.10.012
[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.
	[ 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]	Miluzzo E, Lane N D, Eisenman S B, et al. Internet of things architecture: recent advances, taxonomy, requirements, and open challenges[J]. IEEE Wireless Communications, 2017,24(3):10-16.
[15]	Ahmed E, Yaqoob I, Hashem I A T, et al. The role of big data analytic in internet of things[J]. Computer Networks, 2017(129):459-471.
[16]	Hashem I A T, Chang V, Anuar N B, et al. The role of big data in smart city[J]. International of Journal of Information Management, 2016,36(5):748-758. doi: 10.1016/j.ijinfomgt.2016.05.002
[17]	Yaqoob I, Hashem I A T, Gani A, et al. Big data: From beginning to future[J]. International of Journal of Information Management, 2016,36(6):1231-1247. doi: 10.1016/j.ijinfomgt.2016.07.009
[18]	Glaeser E L, Kominers S D, Luca M, et al. Big data and big cities: The promises and limitations of improved measures of urban life[J]. Economic Inquiry, 2018,56(1):114-137.
[19]	Miller H J, Goodchild M F. Data-driven geography[J]. GeoJournal, 2015,80(4):449-461.
[20]	Batty M. The new science of cities[M]. Cambridge: MIT Press, 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 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.
	[ 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.
	[ Cui Z. Realization mechanism of massive data mining under cloud computing technology[J]. Microcomputer Applications, 2019,35(4):129-131. ]
[24]	Laxman S S, Sastry P S. A survey of temporal data mining[J]. Sadhana, 2006,31(2):173-198.
[25]	Fu T C. A review on time series data mining[J]. Engineering applications of artificial intelligence, 2011,24(1):164-181.
[26]	Mennis J, Guo D. Spatial data mining and geographic knowledge discovery: An introduction[J]. Computers, Environment and Urban Systems, 2009,33(6):403-408.
[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 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]	Marc L I, Zhang P, Robert E, et al. Remote sensing of the urban heat island effect across bio mes in the continental USA[J]. Remote Sensing of Environment, 2010,114(3):504-513.
[29]	Kean H A. 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, 2017,2017:1-12.
[30]	Singh A K, Sharma A K. 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]	Thom D, Bosch H, Koch S, et al. Spatiotemporal anomaly selection through visual analysis of geolocated twitter messages [C]. 2012 IEEE Pacific Visualization Symposium, 2012.
[32]	Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs [C]. Proceedings of the 18^th ACM SIGKDD international conference on Knowledge discovery and data mining , 2012: 186-194.
[33]	Yaqoob I, Ahmad I, Ahmed E, et al. Overcoming the key challenges to establishing vehicular communication: Is SDN the answer?[J]. IEEE Communications Magazine, 2017,55(7):128-134.
[34]	Rwanga S S, Ndambuki J M. Accuracy assessment of land use/land cover classification using remote sensing and GIS[J]. International Journal of Geosciences, 2017,8(4):611-622.
[35]	Dos Santos R F, Boedihardjo A, Shah S, et al. The big data of violent events: Algorithms for association analysis using spatio-temporal storytelling[J]. GeoInformation, 2016,20(4):879-921.
[36]	Salas-Olmedo M H, Moya-Gomez B, García-Palomare J C, et al. Tourists' digital footprint in cities: comparing big data sources[J]. Tourism Management, 2018,66:13-25.
[37]	Gu Y M, Qian Z, Chen F. From Twitter to detector: real-time traffic incident detection using social media data[J]. Transportation Research Part C, 2016,67:321-342.
[38]	Mitchell L, Frank M R, Harris K D, et al. The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place[J]. PloS one, 2013,8(5):e64417. doi: 10.1371/journal.pone.0064417 pmid: 23734200
[39]	Zhou B, Liu L, Oliva A, et al. Recognizing city identity via attribute analysis of geo-tagged images[C]. European Conference on Computer Vision. Springer International Publishing, 2014: 519-534.
[40]	Song C, Koren T, Wang P, et al. Modelling the scaling properties of human mobility[J]. Nature Physics, 2010,6(10):818-823.
[41]	王振武. 大数据挖掘与应用[M]. 北京: 清华大学出版社, 2017.
	[ Wang Z W. Big data mining and application[M]. Beijing: Qinghua University Press, 2017. ]
[42]	Tan M H. Uneven growth of urban clusters in megaregions and its policy implications for new urbanization in China[J]. Land Use Policy, 2017,66:72-29.
[43]	顾立松. 基于遥感调查的珠江三角洲经济区城镇空间发展形态研究[D]. 广州:中国科学院广州地球化学研究, 2004.
	[ Gu L S. The form study on urban spatial expansion and development in Pearl River Delta economy area based on the survey by remote sensing[D]. Guangzhou: Guangzhou Institute of Geochemistry, Chinese Academy of Science, 2004. ]
[44]	美克拉衣·艾克拜尔. 乌鲁木齐城市群的土地利用动态变化分析研究[D]. 乌鲁木齐:新疆大学, 2015.
	[ Meclair E. Research and analysis on Urumqi urban agglomeration land use dynamic changes[D]. Urumqi: Xinjiang: Xinjiang University, 2015. ]
[45]	黄涛. 呼包鄂城市群城市化遥感监测及其模拟预测[D]. 呼和浩特:内蒙古师范大学, 2010.
	[ Huang T. Remote sensing monitoring and forecasting the urbanization of Hubaoe urban agglomerations[D]. Huhhot: Inner Mongolia: Inner Mongolia Normal University, 2010. ]
[46]	Lo C P. Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime images[J]. Annals of the Association of American Geographers, 2002,92(2):225-240.
[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 X Y. Research on boundary recognition of Beijing-Tianjin-Hebei urban agglomeration based on multivariate data[D]. Beijing: China University of Geosciences, 2017. ]
[48]	舒松. 基于稳定夜间灯光遥感数据的城市群空间模式识别方法研究[D]. 上海:华东师范大学, 2013.
	[ Shu S. Spatial cluster analysis of urban landscape pattern using stable nighttime Light satellite images[D]. 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.
	[ 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 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]	Kumar S, Kumar A, Hooda R S. Analysis of road network using remote sensing and GIS data nainital district(uttarakhand)[J]. International Journal For Innovative Research in Multidisciplinary Field, 2017,3(7):122-126.
[52]	Limin J, Xin T, Liu X P. Spatial linkage and urban expansion: An urban agglomeration perspective[J]. Progress in Geography, 2016,35(10):1177-1185.
[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.
	[ 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]	张姝雅. 基于HRRS-GIS的城市群路网评价技术研究及系统实现[D]. 株洲:湖南工业大学, 2015.
	[ Zhang S Y. The urban agglomeration road network evaluation research and system implementation based on HRRS and GIS[D]. 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.
	[ 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. ]
[57]	谭石柳. 基于铁路交通的长三角城市网络研究[D]. 杭州:浙江师范大学, 2015.
	[ Tan S L. Research of urban network of Yangtze River Delta region based on railway network[D]. 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 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 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.
	[ 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 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 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 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.
	[ 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]	韩华瑞. 基于新浪微博签到的京津冀城市群居民活动时空特征及范围划界初探[D]. 武汉:武汉大学, 2017.
	[ Han H R. 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[D]. 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.
	[ 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 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 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.
	[ 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 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 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 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]	王魁. 基于物流网站的大数据挖掘系统开发及其应用[D]. 上海:华东师范大学, 2018.
	[ Wang K. Big data mining and application based on logistics website[D]. 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 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.
	[ 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]	吴亚菲. 产业集群与城市群发展的协同效应研究[D]. 上海:上海社会科学院, 2016.
	[ Wu Y f. Research on the cooperative effect between industrial cluster and urban agglomeration development[D]. 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.
	[ 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.
	[ 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 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 17 ^th 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.
	[ 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.
	[ 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]	苗红. 基于时空数据分析的京津冀现代服务业协同发展策略研究[D]. 武汉:华中师范大学, 2017.
	[ Miao H. Research on the co-development strategy of modern services in Beijing-Tianjin-Hebei region based on spatio-temporal big data[D]. 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 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]	Cai M, Ren C, Xu Y, et al. Investigating 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, 2018,24:485-502.
[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 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]	Jacob A N, Carvalhaes T. Urban stream deserts: Mapping a legacy of urbanization in the United States[J]. Applied Geography, 2016,67:129-139.
[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 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 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 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]	彭佳捷. 基于生态安全的长株潭城市群空间冲突测度研究[D]. 长沙:湖南师范大学, 2011.
	[ Peng J J. The analysis of spatial conflict measurement in ChangZhutan Urban agglomeration based on ecological security[D]. Changsha: Hunan Normal University, 2011. ]
[91]	Zhang W W, Robinson C, Guhathakurta S, et al. Estimating residential energy consumption in metropolitan areas: A microsimulation approach[J]. Energy, 2018,155:162-173.
[92]	Marull J, Galletto V, Domene E, et al. Emerging megaregions: A new spatial scale to explore urban sustainability[J]. Land Use Policy, 2013,34:353-366.
[93]	Mondal B, Nath Das D. How residential compactness and attractiveness can be shaped by environmental amenities in an industrial city?[J]. Sustainable Cities and Society, 2018,41:363-377.
[94]	Jin J, Rafferty P. Externalities of auto traffic congestion growth: evidence from the residential property values in the US Great Lakes megaregion[J]. Journal of Transport Geography, 2018,70:131-140.
[95]	Oikarinen E, Steven C, Hoeslid B M, et al. U.S. metropolitan house price dynamics[J]. Journal of Urban Economics, 2018,105:54-69.
[96]	Yin L, Liang D, Yoshikunia Y, et al. Evaluation of energy-related household carbon footprints in metropolitan areas of Japan[J]. Ecological Modelling, 2018,377:16-25. doi: 10.1016/j.ecolmodel.2018.03.008
[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 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 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]	Hu T Y, et al. Mapping Urban Land Use by Using Landsat images and open social data[J]. Remote Sensing, 2016,8(2):151-169.
[100]	Levin N, Ali S, Crandall D. Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study[J]. Applied Geography, 2018(94):1-17.
[101]	Matthews Y, Scarpa R, Marsh D. Cumulative attraction and spatial dependence in a destination choice model for beach recreation[J]. Tourism Management, 2018,66:318-328.
[102]	Crooks A, Croitoru A, Stefanidis A, et al. Earthquake: Twitter as a distributed sensor system[J]. Transactions in GIS, 2013,17(1):124-147.
[103]	Vieweg S, Hughes A L, Starbird K, et al. Microblogging 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, 2010: 1079-1088.
[104]	Earle P, Guy M, Buckmaster R, et al. OMG earthquake! Can Twitter improve earthquake response?[J]. Seismological Research Letters, 2010,81(2):246-251.
[105]	Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: Real-time event detection by social sensors [C]. Proceedings of the 19^th international conference on World wide web. ACM , 2010: 851-860.
[106]	Signorini A, Segre A M, Polgreen P M. 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, 2011,6(5):e19467. doi: 10.1371/journal.pone.0019467 pmid: 21573238
[107]	Cheng T, Wicks T. Event detection using Twitter: A spatio-temporal approach[J]. PLoS One, 2014,9(6):e97807. doi: 10.1371/journal.pone.0097807 pmid: 24893168
[108]	Hilbert M, Priscilla. L. The word's technological capacity to store, communicate, and computer information[J]. Science, 2011,332(6025):60-65. doi: 10.1126/science.1200970 pmid: 21310967

数据来源	获取方法	获取数据举例	特点
公开数据库	网络下载与收集	政府公开的社会经济统计数据、常用的数据公开网站和部分共享遥感数据下载平台	数据多可免费,传统统计数据类型较多
部分网站平台	网络爬取、API接	网站的财经数据、公司年报、新媒体数据等有价值的信息,如谷歌地图、Facebook等	可获得大量有价值的数据,数据属性明确,部分需要付费
专业数据交易平台	购买	基于交易平台的政务、社会、社交、教育、消费、交通、能源、金融、健康等领域的数据资源	需要付费,可根据需求购买
派生数据	自行加工生产	如在已经下载的遥感数据基础上分析获取新的信息与知识	根据需要进行数据处理,灵活性强
其他		如通过合作等方式共享数据

Study on the Administration and Construction of Urban Agglomeration with Spatiotemporal Big Data: A Progress Review

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