Received date: 2021-08-06
Revised date: 2021-10-31
Online published: 2022-01-29
Supported by
the Strategic Priority Research Program of the Chinese Academy of Sciences "Process-oriented global marine remote sensing monitoring system"(XDA19060103);The National Natural Science Foundation of China "Process-oriented spatiotemporal clustering model for mining marine abnormal variations"(41671401)
There exists a sort of dynamic geographic phenomena with a property from production through development to dissipation in a real world， and the integrated Earth observation technology and the crowd sources technology promote the capabilities of obtaining these dynamics. The traditional spatiotemporal methods take a point， a line， a polygon or a voxel as an analyzing unit， and a scale of data acquisition as the analyzing scale， which splits a continuity of temporal evolutions， and limits their dynamic analysis. This paper abstracts the property from production through development to dissipation into a geographical process， takes it as a scale of analysis， and proposes a novel approach of geographical dynamic analysis. Firstly， a process semantics with a hierarchical abstraction of "geographical process-evolution sequence- instantaneous state" is proposed. And a graph-based model with a "node-edge" is used to represent and store geographical objects and their evolution behaviors. Secondly， a geographical process is used as a unit to design a process-oriented object extracting method with an "extracting of instantaneous state-tracking of evolution sequence- reconstructing of geographical process"， and on the basis of in-degree and out-degree of a graph， four evolution behaviors are identified. Then， series of new concepts about spatiotemporal mining are redefined on the basis of a scale of process， and some process-oriented mining methods are designed， e.g. spatiotemporal clustering， association rule mining. Finally， a real dataset of monthly Sea Surface Temperature Anomaly （SSTA） during the period of January. 1950 to December. 2019 is to explore their evolving structures in Pacific Ocean， and the association patterns between the evolving structures of SSTA and the types of ENSO are addressed. Results demonstrate the effectiveness and the advantages of process-oriented dynamic analyzing method.
Cunjin XUE , Fenzhen SU , Yawen HE . Process：A New View of Geographical Spatiotemporal Dynamic Analysis[J]. Advances in Earth Science, 2022 , 37(1) : 65 -79 . DOI: 10.11867/j.issn.1001-8166.2021.108
| 1 | YUAN M， MARK D M， EGENHOFER M J， et al. Extensions to geographic representation： a research agenda for geographic information science［M］. Boca Raton： CRC Press， 2004： 129-156. |
| 2 | YU M， YANG C， JIN B. A framework for natural phenomena movement tracking-using 4D dust simulation as an example［J］. Computers & Geosciences， 2018， 121： 53-66. |
| 3 | XUE C， WU C， LIU J， et al. A novel process-oriented graph storage for dynamic geographic phenomena［J］. International Journal of Geo-Information， 2019， 8： 100. |
| 4 | ZHOU C， SU F， PEI T， et al. COVID-19： challenges to GIS with big data［J］. Geography and Sustainability， 2020， 1（1）： 77-87. |
| 5 | GONG Jianya. Object-oriented spatio-temporal data model in GIS［J］. Acta Geodaetica et Cartographica Sinica， 1997， 26（4）： 10-19. |
| 5 | 龚健雅. GIS中面向对象时空数据模型［J］.测绘学报，1997，26（4）：10-19. |
| 6 | KJENSTAD K. On the integration of object-based models and field-based models in GIS［J］. International Journal of Geographical Information Science， 2006， 20（5）： 491-509. |
| 7 | DODGE S， ROBERT W B， SEAN C A， et al. Analysis of movement data［J］. International Journal of Geographical Information Science， 2016， 30（5）： 825-834. |
| 8 | DODGE S. A data science framework for movement［J］. Geographical Analysis， 2019. DOI： 10.m/gean.12212. |
| 9 | YU M， BAMBACUS M， CERVONE G， et al. Spatiotemporal event detection： a review［J］. International Journal of Digital Earth， 2020， 1： 1-27. |
| 10 | WU Qunyong， SUN Mei， CUI Lei. Overview of research on spatio-temporal data model［J］. Advances in Earth Science， 2016， 31（10）： 1 001-1 011. |
| 10 | 邬群勇，孙梅，崔磊.时空数据模型研究综述［J］.地球科学进展，2016，31（10）：1 001-1 011. |
| 11 | YANG J， GONG P， FU R， et al. The role of satellite remote sensing in climate change studies［J］. Nature Climate Change， 2013， 3（11）： 875-883. |
| 12 | FERREIRA K R， CAMARA G， MIGUEL A， et al. An algebra for spatiotemporal data： from observations to events［J］. Transactions in GIS， 2014， 18（2）： 253-269. |
| 13 | FERREIRA L N， VEGA-OLIVEROS D A， COTACALLPA M， et al. Spatiotemporal data analysis with chronological networks［J］. Nature Communications， 2020， 11（1）： 4036. |
| 14 | CHENG Changxiu， SHI Peijun， SONG Changqing， et al. Geographic big data provides new opportunities for geographic complexity research［J］. Acta Geographica Sinica， 2018， 73（8）： 1 397-1 406. |
| 14 | 程昌秀，史培军，宋长青，等.地理大数据为地理复杂性研究提供新机遇［J］.地理学报，2018， 73（8）： 1 397-1 406. |
| 15 | XUE C， DONG Q， XIE J. Marine spatio-temporal process semantics and its applications-taking the El Ni?o southern oscilation process and chinese rainfall anomaly as an example［J］. Acta Oceanologica Sinica， 2012，31（2）： 16-24. |
| 16 | YI J， DU Y， LIANG F， et al. A representation framework for studying spatiotemporal changes and interactions of dynamic geographic phenomena［J］. International Journal of Geographical Information Science， 2014， 28（5）： 1 010-1 027. |
| 17 | SU Fenzhen， ZHOU Chenghu. Framework foundation and prototype construction of process geographic information system［J］. Geographical Research， 2006， 3（3）： 477-484. |
| 17 | 苏奋振，周成虎.过程地理信息系统框架基础与原型构建［J］.地理研究，2006，3（3）：477-484. |
| 18 | ZHU R， ERIC G， WONG M. Object-oriented tracking of the dynamic behavior of urban heat islands［J］. International Journal of Geographical Information Science， 2017， 31（2）： 405-424. |
| 19 | Gong J， Ge J， Chen Z. Real-time GIS data model and sensor web service platform for environmental data management［J］. International Journal of Health Geographics， 2015， 14： 2. |
| 20 | ZHU Jie， ZHANG Hongjun. Modeling of the temporal and spatial process of battlefield geographical environment oriented to simulation events［J］. Geomatics and Information Science of Wuhan University， 2020， 45（9）： 1 367-1 377. |
| 20 | 朱杰，张宏军.面向仿真事件的战场地理环境时空过程建模［J］.武汉大学学报：信息科学版，2020，45（9）：1 367-1 377. |
| 21 | MENG Lingkui， ZHAO Chunyu， LIN Zhiyong， et al. Research and implementation of spatio-temporal data model based on time-varying sequence of geographical events［J］. Geomatics and Information Science of Wuhan University， 2003， 28（2）： 202-207. |
| 21 | 孟令奎，赵春宇，林志勇，等.基于地理事件时变序列的时空数据模型研究与实现［J］. 武汉大学学报：信息科学版，2003，28（2）：202-207. |
| 22 | WORBOYS M F. Event-oriented approaches to geographic phenomena［J］. International Journal of Geographical Information Science， 2005， 19（1）： 1-28. |
| 23 | MCHLNTOSH J， YUAN M. Assessing similarity of geographic processes and events［J］. Transaction in GIS，2005， 9（2）： 223-245. |
| 24 | PEUQUET D J， DUAN N. An Event-based Spatio-Temporal Data Model （ESTDM） for temporal analysis of geographical data ［J］. International Journal of Geographical Information Systems， 1995， 9（1）： 7-24. |
| 25 | LIN Guangfa， FENG Xuezhi， WANG Lei， et al. Event-oriented object-oriented spatio-temporal data model［J］. Acta Geodaetica et Cartographica Sinica， 2002（1）： 71-76 |
| 25 | 林广发，冯学智，王雷，等.以事件为核心的面向对象时空数据模型［J］.测绘学报，2002， 31（1）：71-76. |
| 26 | DU Yunyan， YI Jiawei， XUE Cunjin， et al. Geographic event modeling and analysis supported by multi-source geographic big data［J］. Acta Geographica Sinica， 2021，76（11）：2 853- 2 866. |
| 26 | 杜云艳，易嘉伟，薛存金，等.多源地理大数据支撑下的地理事件建模与分析［J］.地理学报， 2021，76（11）：2 853- 2 866. |
| 27 | REITSMA F， ALBRECHT J. Implementing a new data model for simulating processes［J］. International Journal of Geographical Information Science， 2005， 19（10）： 1 073-1 090. |
| 28 | XUE Cunjin， ZHOU Chenghu， SU Fenzhen， et al. Research on process-oriented spatio-temporal data model［J］. Acta Geodaetica et Cartographica Sinica， 2010， 39（1）： 95-101. |
| 28 | 薛存金，周成虎，苏奋振，等.面向过程的时空数据模型研究［J］.测绘学报，2010，39（1）： 95-101. |
| 29 | YUAN M. Representing complex geographic phenomena in GIS［J］. Cartography & Geographic Information Science， 2001， 28（2）： 83-96. |
| 30 | YUAN M， MCINTOSH J. GIS representation for visualizing and mining geographic dynamics［J］. Transaction in GIS， 2003， 2（3）： 3-10. |
| 31 | SIABATO W， CLARAMUNT C， ILARRI S， et al. A survey of modelling trends in temporal GIS［J］. ACM Computing Surveys， 2018， 51（2）： 1-41. |
| 32 | RAHIMI S， MOORE A B， WHIGHAM P A. Beyond objects in space-time： towards a movement analysis framework with 'How' and 'Why' elements［J］. International Journal of Geo-Information， 2021， 10（3）： 190. |
| 33 | MONDO G D， RODRIGUEZ M A， CLARAMUNT C， et al. Modelling consistency of spatio-temporal graphs［J］. Data & Knowledge Engineering， 2013， 84（1）： 59-80. |
| 34 | POKORNY J. Graph Databases： their power and limitations［C］// Springer International Publishing. Germany： Springer International Publishing， 2015. |
| 35 | ROBINSIN I， WEBBER J， EIFREM E. Graph Database［M］. 2nd， edition. Sebastopol， CA， USA： O'Reilly Media， 2015. |
| 36 | Thibaud RéMY，GéRALDINE del Mondo，THIERRY Garlan，et al. A spatio-temporalgraph model for marine dune dynamics analysis and representation［J］. Transactions in GIS，2013，17（5）：742-762. |
| 37 | YI J， DU Y， WANG D， et al. Tracking the evolution processes and behaviors of mesoscale eddies in the South China Sea： a global nearest neighbor filter approach［J］. Acta Oceanologica Sinica， 2017， 36（11）： 27-37. |
| 38 | WANG H， DU Y， YI J， et al. Mining evolution patterns from complex trajectory structures—a case study of mesoscale eddies in the South China Sea［J］. International Journal of Geo-Information， 2020， 9（7）： 441. |
| 39 | BALAGUER A， RUIZ L A， HERMOSILLA T， et al. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification［J］. Computers & Geosciences， 2010， 36（2）： 231-240. |
| 40 | XUE C， DONG Q， QIN L. A cluster-based method for marine sensitive object extraction and representation［J］. Journal of Ocean University of China， 2015， 14（4）： 612-620. |
| 41 | LIU J， XUE C， HE Y， et al. Dual-constraint spatiotemporal clustering approach for exploring marine anomaly patterns using remote sensing products［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2018， 11（11）： 3 963-3 976. |
| 42 | LI L， XU Y， XUE C， et al. A process-oriented approach to identify evolutions of sea surface temperature anomalies with a time-series of a raster dataset［J］. International Journal of Geo-Information， 2021， 10： 500. |
| 43 | XUE C， LIU J， YANG G， et al. A process-oriented method for tracking process-objects with a time-series of raster datasets［J］. Applied Sciences， 2019， 9（12）： 2 468. |
| 44 | DIXON M， WIENER G. TITAN： Thunderstorm Identification， Tracking， Analysis， and Nowcasting—a radar-based methodology［J］. Journal of Atmospheric and Oceanic Technology， 1993， 10（6）： 785-797. |
| 45 | FIOLLEAU T， RéMY R. An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite［J］. IEEE Transactions on Geoscience & Remote Sensing， 2013， 51（7）： 4 302-4 315. |
| 46 | LIU W， LI X， RAHN D A. Storm event representation and analysis based on a directed spatiotemporal graph model［J］. International Journal of Geographical Information Science， 2016，30（5）： 948-969. |
| 47 | MU?OZ C， WANG L， WILLEMS P. Enhanced object-based tracking algorithm for convective rainstorms and cells［J］. Atmospheric Research， 2018， 201： 144-158. |
| 48 | WANG H， DU Y， YI J， et al. A new method for measuring topological structure similarity between complex trajectories［J］. IEEE Transactions on Knowledge and Data Engineering， 2019， 31： 1 836-1 848. |
| 49 | YU M， YANG C. A 3D multi-threshold， region-growing algorithm for identifying dust storm features from model simulations［J］. International Journal of Geographical Information Science， 2017， 31（5）： 939-961. |
| 50 | KHIALI L， IENCO D， TEISSEIRE M. Object-oriented satellite image time series analysis using a graph-based representation［J］. Ecological Informatics， 2018， 43： 52-64. |
| 51 | KHIALI L， NDIATH M， ALLEAUME S， et al. Detection of spatio-temporal evolutions on multi-annual satellite image time series： a clustering-based approach［J］. International Journal of Applied Earth Observation and Geoinformation， 2019， 74： 103-119. |
| 52 | HUSSAIN M， CHEN D， CHENG A， et al. Change detection from remotely sensed images： from pixel-based to object-based approaches［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2013， 80： 91-106. |
| 53 | XU T， MA T， ZHOU C， et al. Characterizing spatio-temporal dynamics of urbanization in China using time series of DMSP/OLS night light data［J］. Remote Sensing， 2014， 6（8）： 7 708-7 731. |
| 54 | LIU J， XUE C， QING D， et al. A process-oriented spatiotemporal clustering method for complex trajectories of dynamic geographic phenomena［J］. IEEE Access， 2019， 7： 155 951-155 964. |
| 55 | XUE C， SONG W， QIN L， et al. A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images［J］. International Journal of Applied Earth Observations & Geoinformation， 2015， 38： 105-114. |
| 56 | HE Z， DENG M， CAI J， et al. Mining spatiotemporal association patterns from complex geographic phenomena［J］. International Journal of Geographical Information Science， 2020， 34（6）： 1 162-1 187. |
| 57 | DODGE S， SONG G， MARTIN T， et al. Progress in computational movement analysis-towards movement data science［J］. International Journal of Geographical Information Science， 2020， 34（12）： 2 395-2 400. |
| 58 | PEI Tao， LIU Yaxi， GUO Sihui， et al. The essence of geographic big data mining［J］. Acta Geographica Sinica， 2019， 74（3）： 586-598. |
| 58 | 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. |
| 59 | MAKROGIANNIS S， ECONOMOU G， FOTOPOULOS S. A region dissimilarity relation that combines feature-space and spatial information for color image segmentation［J］. IEEE Transaction on Systems， Man， and Cybernetics， 2005， 35（1）： 44-53. |
| 60 | ESAIAS W E， IVERSON R L， TURPLE K. Ocean province classification using ocean colour data： observing biological signatures of variations in physical dynamics［J］. Global Change Biology， 2000， 6： 39-55. |
| 61 | LI Deren， ZHANG Liangpei， XIA Guisong. Remote sensing big data automatic analysis and data mining［J］. Acta Geodaetica et Cartographica Sinica， 2014， 43（12）： 1 211-1 216. |
| 61 | 李德仁，张良培，夏桂松.遥感大数据自动分析与数据挖掘［J］.测绘学报， 2014，43（12）：1 211-1 216. |
| 62 | DENG Min， LIU Qiliang， WANG Jiaqiu， et al. A universal method of spatio-temporal cluster analysis［J］. Scientia Sinica Informationis， 2012， 42 （1）： 111-124. |
| 62 | 邓敏，刘启亮，王佳璆，等.时空聚类分析的普适性方法［J］.中国科学：信息科学，2012，42 （1）：111-124. |
| 63 | JI Genlin， ZHAO Bin. Overview of big data-oriented spatio-temporal mining［J］. Journal of Nanjing Normal University（Natural Science Edition）， 2014， 37（1）： 1-7. |
| 63 | 吉根林， 赵斌. 面向大数据的时空挖掘综述［J］. 南京师大学报：自然科学版，2014，37（1）：1-7. |
| 64 | ESTER M， KRIEGEL H P， JIIRG S， et al. A density based algorithm for discovering clusters in large spatial databases［C］// Proceedings of international conference on knowledge discovery & data mining. Germany： Institute for Computer Science of University of Munich， 1996. |
| 65 | PEI T， ZHOU C， ZHU A， et al. Windowed nearest neighbor method for mining spatio-temporal clusters in the presence of noise［J］. International Journal of Geographical Information Science， 2010， 24（6）： 925-948. |
| 66 | MCGUIRE M P， JANEIA V P， GANGOPADHVAY A. Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets［J］. Data Mining and Knowledge Discovery， 2014， 28（4）： 961-1 003. |
| 67 | LIU Q， DENG M， BI J， et al. A novel method for discovering spatio-temporal clusters of different sizes， shapes， and densities in the presence of noise［J］. International Journal of Digital Earth， 2014， 7（2）： 138-157. |
| 68 | LEE J， LEE Y. A knowledge discovery framework for spatiotemporal data mining［J］. International Journal of Information Processing Systems， 2006， 2（2）： 124-129. |
| 69 | BERTOLOTTO M， DIMARTINO S， FERRUCCI F， et al. Towards a framework for mining and analysing spatio-temporal datasets［J］. International Journal of Geographical Information Science， 2007， 21（8）： 895-906. |
| 70 | KUMAR V. Discovery of patterns in global Earth science data using data mining［J］. Lecture Notes in Computer Science， 2010， 2： 6118. |
| 71 | RAO K V， GOVARDHAN A， RAO K V. An object-oriented modeling and implementation of spatio-temporal knowledge discovery system［J］. International Journal of Computer Science & Information Technology， 2011， 3（2）： 61-76. |
| 72 | AGRAWAL R， SRIKANT R. Fast algorithms for mining association rules［C］// Proceeding of the 20th international conference on very large databases. Santiago de Chile： Morgan Kaufmann， 1994. |
| 73 | HAN J， PEI J， YIN Y. Mining frequent patterns without candidate generation［C］//Proceedings of the 2000 ACM SIGMOD international conference on management of data. Dallas， Texas， USA： ACM， 2000. |
| 74 | KE Y P， CHEN G J， Ng W. An information-theoretic approach to quantitative association rule mining［J］. Knowledge Information Systeml， 2008， 16（2）： 213-244. |
| 75 | XUE C， SONG W， QIN L， et al. A mutual-information-based mining method for marine abnormal association rules［J］. Computers & Geosciences， 2015， 76： 121-129. |
| 76 | MCPHADEN M J， ZEBIAK S E， GLANTZ M H. ENSO as an integrating concept in Earth science［J］. Science， 2006， 314 （5 806）： 1 740-1 745. |
| 77 | SONG W， DONG Q， XUE C. A classified El Ni?o index using AVHRR remote-sensing SST data［J］. International Journal of Remote Sensing， 2016， 37（2）： 403-417. |
| 78 | WANG M， GUAN Z， JIN D. Two new sea surface temperature anomalies indices for capturing the eastern and central equatorial Pacific type El Ni?o-Southern Oscillation events during boreal summer［J］. International Journal of Climatology， 2018， 38： 4 066-4 076. |
| 79 | ZHANG Z， REN B， ZHENG J. A unified complex index to characterize two types of ENSO simultaneously［J］. Scientific Reports， 2019， 9（1）： 8373. |
| 80 | LEE T， MCPHADEN M J. Increasing intensity of El Ni?o in the central-equatorial Pacific［J］. Geophysical Research Letters， 2010， 37（14）. DOI 10.1024/20106L044007. |
| 81 | ISHII M， SHOUJI A， SUGIMOTO S， et al. Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe collection［J］. International Journal of Climatology， 2005， 25（7）： 865-879. |
| 82 | WOLTER K， TIMLIN M S. El Ni?o/Southern Oscillation behavior since 1871 as diagnosed in an extended multivariate ENSO index （MEI.ext） ［J］. International Journal of Climatology， 2011， 31（7）： 1 074-1 087. |
| 83 | KAO H Y， YU J. Contrasting eastern-Paci?c and central-Paci?c types of ENSO［J］. Journal of Climate， 2009， 22 （3）： 615-632. |
| 84 | XIANG B， WANG B， LI T. A new paradigm for the predominance of standing central pacific warming after the late 1990s［J］. Climate Dynamics， 2013， 41（2）： 327-340. |
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