大数据时代细胞仿生结构的地理空间认知方法Geospatial cognition method of biomimetic cell structure under the era of big data
陆妍玲;刘采玮;李景文;姜建武;夏勇超;
摘要(Abstract):
针对多属性、多维、多源异构的时空大数据特征,急需一种全空间的时空大数据模型进行地理空间认知。基于地理空间认知过程,提出了地理空间细胞模型,将地理空间抽象成无数个地理细胞组成,邻近的地理细胞围绕核心细胞聚集为地理簇,相似属性的地理簇聚集为地理块,实现了时空大数据的高效聚类,解决了时空大数据的组织管理问题,达到了时空大数据在地理空间中微观与宏观的统一认知。通过仿生生物遗传学里的细胞、组织、器官、系统管理地理空间细胞的聚类过程,形成对地理空间自下而上的多尺度表达。
关键词(KeyWords): 细胞仿生;地理空间认知;时空大数据;时空聚类
基金项目(Foundation): 国家自然科学基金项目(41461085);; 广西空间信息与测绘重点实验室主任基金项目(15-140-07-14,16-380-25-17)
作者(Authors): 陆妍玲;刘采玮;李景文;姜建武;夏勇超;
DOI: 10.16251/j.cnki.1009-2307.2020.09.026
参考文献(References):
- [1] 陆妍玲,李景文,叶苏娴,等.扩展流数据立方体的GIS时空大数据组织方法[J].测绘通报,2018(8):115-118.(LU Yanling,LI Jingwen,YE Suxian,et al.Big data organization method of GIS spatio-temporal expanded the streaming data cube[J].Bulletin of Surveying and Mapping,2018(8):115-118.)
- [2] 徐佑军,谭成国.时空数据的组织与应用研究[J].测绘通报,2017(2):98-101.(XU Youjun,TAN Chengguo.Study on the spatio-temporal data organization and application[J].Bulletin of Surveying and Mapping,2017(2):98-101.)
- [3] 李德仁,马军,邵振峰.论时空大数据及其应用[J].卫星应用,2015(9):7-11.(LI Deren,MA Jun,SHAO Zhenfeng.On spatio-temporal big data and its application[J].Satellite Application,2015(9):7-11.)
- [4] 王家耀,武芳,郭建忠,等.时空大数据面临的挑战与机遇[J].测绘科学,2017,42(7):1-7.(WANG Jiayao,WU Fang,GUO Jianzhong,et al.Challenges and opportunities of spatio-temporal big data[J].Science of Surveying and Mapping,2017,42(7):1-7.)
- [5] 王家耀.时空大数据时代的地图学[J].测绘学报,2017,46(10):1226-1237.(WANG Jiayao.Cartography in the age of spatio-temporal big data[J].Acta Geodaetica et Cartographica Sinica,2017,46(10):1226-1237.)
- [6] 华一新.全空间信息系统的核心问题和关键技术[J].测绘科学技术学报,2016,33(4):331-335.(HUA Yixin.The core problems and key technologies of pan-spatial information system[J].Joumal of Geomatics Science and Technology,2016,33(4):331-335.)
- [7] 裴韬,刘亚溪,郭思慧,等.地理大数据挖掘的本质[J].地理学报,2019,74(3):586-598.(PEI Tao,LIU Yaxi,GUO Sihui,et al.Principle of big geodata mining[J].Acta Geographica Sinica,2019,74(3):586-598.)
- [8] 华一新,周成虎.面向全空间信息系统的多粒度时空对象数据模型描述框架[J].地球信息科学学报,2017,19(9):1142-1149.(HUA Yixin,ZHOU Chenghu.Description frame of data model of multi-granularity spatio-temporal object for Pan-spatial Information System[J].Journal of Geo-information Science,2017,19(9):1142-1149.)
- [9] 秦萧,甄峰.大数据与小数据结合:信息时代城市研究方法探讨[J].地理科学,2017,37(3):321-330.(QIN Xiao,ZHEN Feng.Combination between big data and small data:new methods of urban studies in the information era[J].Scientia Geographica Sinica,2017,37(3):321-330.)
- [10] 江南,方成,陈敏颉.全空间信息系统认知与表达初探[J].地球信息科学学报,2017,19(9):1150-1157.(JIANG Nan,FANG Cheng,CHEN Minjie.Initial exploration of pan-spatial cognition and representation[J].Journal of Geo-information Science,2017,19(9):1150-1157.)
- [11] 王劲峰,葛咏,李连发,等.地理学时空数据分析方法[J].地理学报,2014,69(9):1326-1345.(WANG Jinfeng,GE Yong,LI Lianfa,et al.Spatiotemporal data analysis in geography[J].Acta Geographica Sinica,2014,69(9):1326-1345.)
- [12] 李小龙.支持动态数据管理与时空过程模拟的实时GIS数据模型研究[J].测绘学报,2017,46(3):402.(LI Xiaolong.Real-time GIS data model supporting dynamic data management and spatiotemporal process simulation[J].Acta Geodaetica et Cartographica Sinica,2017,46(3):402.)
- [13] 吴宾.基于对象的地理时空演变分析与知识发现[D].上海:华东师范大学,2018:80-86.(WU Bin.Object-based analysis and knowledge discovery by modeling spatio-temporal evolution of geographical phenomena[D].Shanghai:East China Normal University,2018:80-86.)
- [14]刘杭雨,于洪.一种多粒度增量属性的聚类方法[J].小型微型计算机系统,2019,40(3):618-622.(LIU Hangyu,YU Hong. Multi-granularity incremental attribute clustering method[J].Journal of Chinese Computer Systems,2019,40(3):618-622.)
- [15]顾昱骅.地理时空大数据高效聚类方法研究[D].浙江:浙江大学,2018:69-71.(GU Yuhua.The methods of high efficient clustering on spatiotemporal big data[D].Zhejiang:Zhejiang University,2018:69-71.)
- [16]DU Qingyun,DONG Zhi,HUANG Chudong,et al.Density-based clustering with geographical background constraints using a semantic expression model[J].ISPRS International Journal of Geo-Information,2016,5(5):72.
- [17]JING Weipeng,ZHAO Chuanyu,JIANG Chao. An improvement method of DBSCAN algorithm on cloud computing[J].Procedia Computer Science,2019,147:596-604.
- [18]MUMTAZ K,DURAISWAMY K. An analysis on density based clustering of multi dimensional spatial data[J].Indian Journal of Computer Science&Engineering,2010,1(1):8-12.
- [19]李志林,刘启亮,唐建波.尺度驱动的空间聚类理论[J].测绘学报,2017,46(10):1534-1548.(LI Zhilin,LIU Qiliang,TANG Jianbo.Towards a scale-driven theory for spatial clustering[J].Acta Geodaetica et Cartographica Sinica,2017,46(10):1534-1548.)
- [20]吴文浩,吴升.多时间尺度密度聚类算法的案事件分析应用[J].地球信息科学学报,2015,17(7):837-845.(WU Wenhao,WU Sheng.Application of densitybased clustering algorithm in crime cases analysis considering multiple time scale[J].Journal of Geoinformation Science,2015,17(7):837-845.)
- [21]伏家云,靖常峰,杜明义.空间密度聚类模式挖掘方法DBSCAN研究回顾与进展[J].测绘科学,2018,43(12):50-57.(FU Jiayun,JING Changfeng,DU Mingyi.Review and progress of DBSCAN research on density clustering pattern mining method[J].Science of Surveying and Mapping,2018,43(12):50-57.)
- [22]李文杰,闫世强,蒋莹,等.自适应确定DBSCAN算法参数的算法研究[J].计算机工程与应用,2019,55(5):1-7,148.(LI Wenjie,YAN Shiqiang,JIANG Ying,et al.Research on method of self-adaptive determination of DBSCAN algorithm parameters[J].Computer Engineering and Applications,2019,55(5):1-7.)