Positioning localities from spatial assertions based on Voronoi neighboring  

作　　者：GONG YongXi 1,2,3,LI GuiCai 1,LIU Yu 2 & YANG Jian 1 1 Shenzhen Key Lab of Recycling Economy,Peking University Shenzhen Graduate School,Shenzhen 518055,China 2Institute of Remote Sensing and Geographical Information Systems,Peking University,Beijing 100871,China 3College of Urban and Environmental Science,Peking University,Beijing 100871,China 

出　　处：《Science China(Technological Sciences)》2010年第S1期143-149,共7页中国科学（技术科学英文版）

基　　金：supported by the National Hi-Tech Research and Development Program of China(Grant No.2007AA120502);the National Natural Science Foundation of China(Grant Nos.40701134,40771171,40830747,40928001);the National Key Technology R&D Program(GrantNos.2006BAJ14B04,2008BAJ11B04);the National Science Foundation for Post-doctoral Scientists of China(Grant No.20090460125)

摘　　要：With the rapid development of Internet,much spatial information contained in non-structured or semi-structured documents is available on the World Wide Web.In such documents,localities are always textually described using spatial relationships and named places,instead of numerical coordinates.Hence,extracting positional information from locality descriptions is an important task.In this paper,we bridge two aspects of locality descriptions,namely generating locality descriptions and positioning localities,and provide a method to compute probability density according to the selection probability of a reference object to describe the position of the target object.Refinement operation on uncertainty field is used to deal with locality description involving multiple reference objects.Three metrics are introduced to measure the results of positioning localities.We choose the mixed selection probability function based on Euclidean distance and Voronoi stolen-area to compute probability density function.Finally,we use three cases to demonstrate the proposed methods.With the rapid development of Internet,much spatial information contained in non-structured or semi-structured documents is available on the World Wide Web.In such documents,localities are always textually described using spatial relationships and named places,instead of numerical coordinates.Hence,extracting positional information from locality descriptions is an important task.In this paper,we bridge two aspects of locality descriptions,namely generating locality descriptions and positioning localities,and provide a method to compute probability density according to the selection probability of a reference object to describe the position of the target object.Refinement operation on uncertainty field is used to deal with locality description involving multiple reference objects.Three metrics are introduced to measure the results of positioning localities.We choose the mixed selection probability function based on Euclidean distance and Voronoi stolen-area to compute probability density function.Finally,we use three cases to demonstrate the proposed methods.

关 键 词：GEOGRAPHIC information science POSITIONING LOCALITY VORONOI neighboring PROBABILITY density function 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]