基于K-means算法的突发事件地理信息空间化研究

doi:10.13474/j.cnki.11-2246.2018.0222

测绘通报 ›› 2018, Vol. 0 ›› Issue (7): 112-115.doi: 10.13474/j.cnki.11-2246.2018.0222

基于K-means算法的突发事件地理信息空间化研究

杨波¹, 王继周², 毛曦², 马维军²

1. 辽宁工程技术大学, 辽宁阜新 123000;
2. 中国测绘科学研究院, 北京 100830

收稿日期:2017-09-11 出版日期:2018-07-25 发布日期:2018-08-02
作者简介:杨波(1992-),男,硕士生,研究方向为地理信息科学、数据挖掘。E-mail:michaelyangbo@outlook.com
基金资助:
基于众源数据的突发事件信息融合与可视化（7771615）

Research on Spatialization of Crowdsourcing Contingency Geographic Information Based on K-means Algorithm

YANG Bo¹, WANG Jizhou², MAO Xi², MA Weijun²

1. Liaoning Technical University, Fuxin 123000, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received:2017-09-11 Online:2018-07-25 Published:2018-08-02

摘要/Abstract

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。

关键词: 众源, 应急, K-means算法, 空间认知模型, 空间化

Abstract: 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.K-means 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.

Key words: crowdsourcing, emergency, K-means algorithm, spatial cognition model, spatialization

中图分类号:

P208

杨波, 王继周, 毛曦, 马维军. 基于K-means算法的突发事件地理信息空间化研究[J]. 测绘通报, 2018, 0(7): 112-115.

YANG Bo, WANG Jizhou, MAO Xi, MA Weijun. Research on Spatialization of Crowdsourcing Contingency Geographic Information Based on K-means Algorithm[J]. 测绘通报, 2018, 0(7): 112-115.

参考文献

[1] SUN X,ZHOU Y.Modeling Emergency Event and Emergency Response Decision Based on Event Evolvement[J].ICSSSM,2011:1-5.DOI:10.1109/ICSSSM,2011,5959332.
[2] HE M.Study on Emergency Management of Traffic Jam and Improvement of Emergency Mechanism During Ice Disaster in Hunan[C]//IEEE International Conference on Emergency Management and Management Sciences.[S.l.]:IEEE,2010:318-321.
[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.
[5] YOSHIOKA O,ARAI K,SUGAMURA N,et al.An Address Data Entry System with a Multimodal Interface Including Speech Recognition[J].Systems & Computers in Japan,2015,30(9):64-73.
[6] MURRAY A T,TONG D,GRUBESIC T H.Spatial Optimization:Expanding Emergency Services to Address Regional Growth and Development[J].Chapters,2012.
[7] HOUSTON D.Policies to Address Spatial Mismatch[J].International Encyclopedia of Housing and Home,2012:274-279.
[8] LIN J,CHEN C,WU X,et al.GeoKSGrid:A Geographical Knowledge Grid with Functions of Spatial Data Mining and Spatial Decision[C]//IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.[S.l.]:IEEE,2011:121-126.
[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.

基于K-means算法的突发事件地理信息空间化研究

Research on Spatialization of Crowdsourcing Contingency Geographic Information Based on K-means Algorithm

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	吴献文, 速云中, 孙照辉. 夜间灯光数据支持下的广州市人口空间化[J]. 测绘通报, 2021, 0(5): 59-62.
[2]	郭子翰, 陈斐, 刘晓丽, 戴昭鑫. 基于土地利用类型提高人口格网数据空间精度的方法[J]. 测绘通报, 2021, 0(12): 66-70.
[3]	田海波, 陈利军, 张宏伟, 杜晓. 全球4D数据产品研制[J]. 测绘通报, 2020, 0(7): 1-4,21.
[4]	张俊辉, 艾海滨, 王庆栋, 韩晓霞. 高分辨率光学卫星影像全球重点区域三维建模方法——以迪拜市为例[J]. 测绘通报, 2020, 0(7): 22-28.
[5]	朱杰, 王俊伟, 王中祥, 武昊, 张蓉晖. 无人直升机航空应急测绘系统设计与实现[J]. 测绘通报, 2020, 0(6): 87-92.
[6]	周钰, 王亮, 李西灿, 张玉, 栗斌. 基于生态足迹的格网化生态承载力评价——以衡水市为例[J]. 测绘通报, 2020, 0(6): 93-98.
[7]	杨燈, 王坚, 王敏敏, 张亚磊. 应急环境基站布设对定位精度的影响分析[J]. 测绘通报, 2020, 0(5): 90-94.
[8]	唐新明, 刘锟, 王界, 甘宇航, 楼临江. 卫星遥感应急监测信息服务框架设计与应用——以四川木里森林火灾为例[J]. 测绘通报, 2020, 0(12): 110-113,154.
[9]	郭晨, 许强, 董秀军, 巨袁臻, 宁浩. 无人机在重大地质灾害应急调查中的应用[J]. 测绘通报, 2020, 0(10): 6-11,73.
[10]	赵勇, 武昊, 王中祥, 朱杰. 国家应急测绘保障能力建设项目总体技术设计[J]. 测绘通报, 2019, 0(9): 121-127.
[11]	王中祥, 武昊, 朱杰, 王俊伟, 张蓉晖. 大载荷长航时无人机航空应急测绘系统设计与实现[J]. 测绘通报, 2019, 0(9): 128-134.
[12]	刘磊, 刘津, 翟永, 邢绪超. 国家应急测绘调度系统设计[J]. 测绘通报, 2019, 0(9): 135-138,146.
[13]	毕凯, 王中祥, 武昊, 朱杰, 张蓉晖. 国家应急测绘前线勘测系统设计与建设[J]. 测绘通报, 2019, 0(9): 139-146.
[14]	杨靓, 黄蔚, 查祝华, 张红平. 兼顾志愿者地理信息的国家应急测绘公众服务平台设计[J]. 测绘通报, 2019, 0(9): 147-151.
[15]	李东海. 三维激光扫描技术在1∶1万地质灾害详细调查中的应用——以河北邢台峡谷群国家地质公园为例[J]. 测绘通报, 2019, 0(7): 160-162.