期刊文献+

基于RkNN的村镇基础设施选址分析系统 被引量:1

A Village Infrastructure Location Analysis System Based on RkNN
下载PDF
导出
摘要 设施选址问题是解决如何使各个设施能够为客户需求提供最好的服务,可以利用空间位置影响力查询空间技术实现.空间位置的影响力指其对周围空间对象的影响程度,现有的空间位置影响力度量方法只是计算所属区域中的空间对象的个数.但是,空间对象可对多个空间位置产生贡献.因此,提出了一种新的空间位置影响力的度量模型,根据空间对象与空间位置的距离确定影响力权重,使其更加符合实际应用情况;并提出了基于RkNN的选址算法,利用新度量模型计算选址方案中各设施的影响力,进而引入均衡系数评价选址方案的合理性,均衡系数越小,方案越合理.开发了村镇基础设施选址分析系统,本系统实际应用表明基于RkNN的选址算法使村镇基础设施的选址更加合理、有效,为新农村基础设施科学选址、合理建设提供了依据和技术支持. The location problem deals with on how to make facilities providing the best customer service,which can be achieved by the query for the influence of a spatial location.The influence of a spatial location s is a degree of the influence on objects around s.In the existing measures of the influence of spatial location,it is usually measured by the number of objects owned by s.However,an object can make contribution to multiple locations.So,a new measure for evaluating the influence of spatial location is proposed,and the influence weight is determined by the distance between the object and the location,which meets the actual applications.A location algorithm based on RkNN is proposed in this paper. The new measure is used to estimate the influence of the village infrastructure,in the end,balanced coefficient is used to evaluate the reasonability of the location. The smaller the balanced coefficient is,the more reasonability the program is.A village infrastructure location analysis system based on RkNN has been developed.Its actual application shows that the location algorithm based on RkNN makes the location algorithm more reasonable and available,and provides basis and technique support for the location and reasonable construction of the new village.
出处 《计算机研究与发展》 EI CSCD 北大核心 2013年第S1期426-430,共5页 Journal of Computer Research and Development
基金 国家自然科学基金项目(61070024)
关键词 选址 影响力 RkNN 管理系统 基础设施 location influence reverse k nearest neighbor management system infrastructure
  • 相关文献

参考文献5

二级参考文献86

  • 1刘永山,薄树奎,张强,郝忠孝.多对象的最近邻查询[J].计算机工程,2004,30(11):66-68. 被引量:8
  • 2Papadias D,Shen Q,Tao Y,et al.Group nearest neighbor queries[C] //Proc of the 20th Int Conf on Data Engineering.Los Alamitos,CA:IEEE Computer Society,2004:301-312.
  • 3Papadias D,Tao Y,Mouratidis K,et al.Aggregate nearest neighbor queries in spatial databases[J].ACM Trans on Database Systems,2005,30(2):529-576.
  • 4Li Hongga,Lu Hua,Huang Bo,et al.Two ellipse-based pruning methods for group nearest neighbor queries[C] //Proc of the 13th Annual ACM Int Workshop on Geographic Information Systems.New York:ACM,2005:192-199.
  • 5Luo Y,Chen H,Furuse K,et al.Efficient methods in finding aggregate nearest neighbor by projection-based filtering[C] //Proc of the Int Conf on Computational Science and Its Applications.Berlin:Springer,2007:821-833.
  • 6Sack J R,Urrutia J.Handbook on Computational Geometry[M].New York:Elsevier,2000:201-290.
  • 7Rao B,Minakakis L.Evolution of mobile location-based services.Association for Computing Machinery,2003,46(12):61-65.
  • 8Wu Wei,Chee Fei Yang,Chan Yong,Tan Kian-Lee.Continuous reverse k-Nearest-Neighbor monitoring//Proceedings of the 9th International Conference on Mobile Data Management.Beijing,2008:132-139.
  • 9Korn F,Muthukrishnan S.Influence sets based on reverse nearest neighbor queries//Special Interest Group on Management of Data.Dallas,Texas,USA,2000:201-212.
  • 10Yang C,Lin K-I.An index structure for efficient reverse nearest neighbor queries//Proceedings of the 17th International Conference on Data Engineering.Heidelberg,Germany,2001:485-492.

共引文献87

同被引文献28

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部