基于K-means算法的突发事件地理信息空间化研究
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摘要
针对目前众源突发事件地理信息提取中空间化方法的不足,本文提出了基于众源应急事件的地理信息空间化方法研究。采用K-means算法来解决地图服务无法空间化的地理信息问题。地理信息空间化有效地解决了众源应急地理信息提取中地址空间化的不足,实现了最优的地址空间化的识别与存储,同时配合地理信息认知模型可以进一步细化分析地名的空间关系,这为下一步应急地理信息的可视化提供了数据质量的保证。试验结果表明,本文的应急地理信息空间化方法不仅可以对中文地址实现地理信息空间化,而且该方法还适用于以非中文为母语国家的众源应急地理信息空间化,同时还为其他领域的关联识别提供了方法上的参考。该研究可以为应急灾害救援部门提供更高精度的空间地理信息数据,将获取到的众源应急信息准确地显示在地图上。
This paper analyzes the shortcomings of the common spatialization way in the crowdsourcing emergency geographical information extraction.It puts forward the consideration of geographical info interspace based on the sourcing contingency events. Kmeans algorithm is used to solve the problem that the map service cannot extract the multi-sourcing affairs' spatial material.The spatial circumstance combines the advantages of attribute data and geography.This method carries out the optimal hazard trouble identification and storage. It makes the surveying and mapping better serve the society and the nation more closely related to people. With the geospatial cognitive model,this essay can discuss the spatial relations of toponomy in more specific aspects.This provides a guarantee of data quality for the next step in the visualization of the outburst geographic accident. Trial runs results show that the space of breakthrough geoscience's mishap not only can draw the Chinese placename data,but also identify non-Chinese country. The measure also offers a reference to other relevant areas of research.The study can supply more accurate spatial geonomy's malfunction needs for flying into disaster relief departments.They get to the source of the breaking out record displayed on the plat accurately.
This paper analyzes the shortcomings of the common spatialization way in the crowdsourcing emergency geographical information extraction.It puts forward the consideration of geographical info interspace based on the sourcing contingency events. Kmeans algorithm is used to solve the problem that the map service cannot extract the multi-sourcing affairs' spatial material.The spatial circumstance combines the advantages of attribute data and geography.This method carries out the optimal hazard trouble identification and storage. It makes the surveying and mapping better serve the society and the nation more closely related to people. With the geospatial cognitive model,this essay can discuss the spatial relations of toponomy in more specific aspects.This provides a guarantee of data quality for the next step in the visualization of the outburst geographic accident. Trial runs results show that the space of breakthrough geoscience's mishap not only can draw the Chinese placename data,but also identify non-Chinese country. The measure also offers a reference to other relevant areas of research.The study can supply more accurate spatial geonomy's malfunction needs for flying into disaster relief departments.They get to the source of the breaking out record displayed on the plat accurately.
引文
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[3]BU F,XIE Q.Research on Emergency Evacuation Traffic Trip Generation Forecasting Based on Logistic Regression[C]∥IEEE International Conference on Emergency Management and Management Sciences.[S.l.]:IEEE,2010:504-507.
[4]PARK K H,MIZUMOTO T.Address Recognition and Generation of Switching Control Signal for All-optical Routing[J].Optical Engineering,1999,38(38):1848-1852.
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[9]ZHANG C,LI Y.Research on Data Storage System of Geographical Spatial Information Based on Grid[C]∥International Conference on Intelligent Systems Research and Mechatronics Engineering.[S.l.]:ISRME,2015.
[10]STEPHAN S.Reasoning on Spatial Relations between Entity Classes[C]∥International Conference on Geographic Information Science.[S.l.]:Springer-Verlag,2008:234-248.
[11]O’SULLIVAN D,UNWIN D J.Geographic Information Analysis and Spatial Data[M].[S.l.]:Johnwiley&Sons Inc.,2010:1-32.
[12]BAHL B,SHARMA V,RAJPAL N.Topo-net Spatial Entity Relationship Model for Geographic Information System Applications[J].International Journal of Database Management Systems,2011,3(4).
[13]ZHOU S,CHEN S,WAN B.Spatial Topology Rule Checking Algorithm of Linear Entity Based on Quadtree[J].Journal of Geographic Information System,2009,1(1):1-4.
[14]BABU G P,MURTY M N.A Near-optimal Initial Seed Value Selection in K-means Algorithm Using a Genetic Algorithm[J].Pattern Recognition,1993,14(10):763-769.
[15]SHI N,LIU X,GUAN Y.Research on K-means Clustering Algorithm:an Improved K-means Clustering Algorithm[C]∥Proceedings of the Third International Symposium on Intelligent Information Technology and Security Informatics.Washington DC:IEEE,2010:63-67.
[16]ZHENG D,WANG Q P.Selection Algorithm for K-means Initial Clustering Center[J].Journal of Computer Applications,2012,32(8):2186-2188.
[17]MOJENA R.Ward’s Clustering Algorithm[M].[S.l.]:John Wiley&Sons,Inc,2004:73-78.
[18]SUNDHEIM B M.Information Extraction System Evaluation[C]∥Proceedings of the Workshop on Human Language Technology.Princeton:Stroudsburg,1993:27-44.
[19]KONYS A.An Approach for Ontology-based Information Extraction System Selection and Evaluation[J].Przeglad Elektrotechniczny,2015,1(11):207-211.
[20]ZENG G,ZHANG C,BO X,et al.CRFs-based Chinese Named Entity Recognition with Improved Tag Set[C]∥Wri World Congress on Computer Science and Information Engineering.[S.l.]:IEEE Computer Society,2009:519-522.