期刊文献+

面向微地图的地标提取方法及个性化寻路应用 被引量:5

Landmark Extraction Method and Personalized Wayfinding Application for We-Map
原文传递
导出
摘要 地标在空间信息传输中具有重要作用,微地图为用户制作及传播地图内容提供平台。为提取符合人们空间认知的微地图地标,本文提出了一种由用户生成内容来提取地标的方法。①计算公众认知度、城市中心度、特征属性值3个指标;②利用熵值法确定各指标的权重,依据地标显著度差异分层获取地标,建立服务于微地图用户的地标集;③在用户制作及传播微地图的过程中,收集由用户生成的地标,丰富地标库,实现地标的二次传播,达到由用户生成内容提取地标的目的。实验选择兰州市安宁区的POI数据计算地标显著度,提取不同层次的地标,实验结果创建了服务于微地图用户的各层地标集,实例化利用地标连线完成寻路,绘制出满足用户不同需求的个性化路线。本研究应用于日常寻路,为微地图的快速绘制和将地标纳入导航系统提供参考,提高寻路效率。 Landmarks play an important role in spatial information transmission,especially for wayfinding navigation.Numerous studies have shown that the inclusion of landmarks in route tasks can effectively reduce steering errors.How to incorporate landmarks into navigation systems and break the barrier of using distance information as the indicator to guide users in wayfinding is currently a difficult problem to solve.And we-map provides a platform for users to produce and disseminate map content.Because we-map does not distinguish between mapmakers and map users,it lowers the threshold for mapping and enables users to have self-make maps.In the process of route mapping,we-map platform can provide a collection of landmarks for users to choose from and use them to complete their wayfinding,enabling the solution to the challenge of incorporating landmarks into navigation systems.In order to extract we-map landmarks accorded with people's spatial cognition,the method of extracting landmarks by user-generated content is proposed.First,there are three indicators(public awareness,city centrality,and individual characteristic value)that are calculated separately,and each of them is obtained by the entropy value method.Then,landmarks are extracted in a hierarchical manner in term of the difference in the significance of landmarks to establish a set of landmarks for serving the users of the we-map.Last,the user-generated landmarks are collected during the process of publishing,sharing,and disseminating the we-map to enrich the landmark library,aiming at realizing secondary dissemination about the extraction of landmarks from user-generated contents.The experiment selects POI data of An Ning District in Lanzhou City to calculate landmark salience,selects landmarks at different levels according to different scales,designs tasks for participants to describe routes and complete connections between landmarks,collects usage landmarks,forms user-generated content to disseminate landmark data,and draws personalized routes that meet different user needs.This study simulates the process of route-finding cartography using landmarks by we-map users to pave the ground for personalized navigation on the we-map platform.The experimental results show that the content generated by using users'shared service data effectively solves the problem of acquiring and timely updating landmark candidate sets,expresses the user's cognitive expressiveness to the greatest extent,and reduces the burden of wayfinding for pedestrians walking out.This study is applied to daily wayfinding,where we-map users participate in constructing and sharing service data,forming spontaneous dissemination of usergenerated content,timely update,and dissemination of landmark data,providing reference for rapid we-map drawing,and improving wayfinding efficiency.
作者 何阳 闫浩文 王卓 王小龙 HE Yang;YAN Haowen;WANG Zhuo;WANG Xiaolong(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China;School of Resource and Environmental Science,Wuhan University,Wuhan 430079,China)
出处 《地球信息科学学报》 CSCD 北大核心 2022年第5期827-836,共10页 Journal of Geo-information Science
基金 2021年度中央引导地方科技发展资金支持项目 国家自然科学基金项目(41930101) 甘肃省教育厅:优秀研究生“创新之星”项目(2021CXZX-590)。
关键词 地标 微地图 POI 空间认知 寻路 VORONOI图 地标显著度 空间密度聚类 landmark we-map POI spatial cognition wayfinding Voronoi diagram landmarks salience spatial density clustering
  • 相关文献

参考文献9

二级参考文献82

  • 1刘金义,刘爽.Voronoi图应用综述[J].工程图学学报,2004,25(2):125-132. 被引量:74
  • 2李丽萍,吴祥裕.宜居城市评价指标体系研究[J].中共济南市委党校学报,2007(1):16-21. 被引量:44
  • 3王芳,高晓路,许泽宁.基于街区尺度的城市商业区识别与分类及其空间分布格局——以北京为例[J].地理研究,2015,34(6):1125-1134. 被引量:68
  • 4王晓明,刘瑜,张晶.地理空间认知综述[J].地理与地理信息科学,2005,21(6):1-10. 被引量:51
  • 5Ankerst M, Breunig M M, Kriegel H P and Sander J. 1999. OPTICS: ordering points to identify the clustering structure.ACM SIGMOD Record, 28(2): 49-60 DOI: 10.1145/304181.304187.
  • 6Caduff D and Timpf S. 2008. On the assessment of landmark salience for human navigation. Cognitive Processing, 9(4): 249-267 DOI: 10.1007/s10339-007-0199-2.
  • 7Daniel M P and Denis M. 1998. Spatial descriptions as navigational aids: a cognitive analysis of route directions. Kognitionswissenschaft, 7(1): 45-52 DOI: 10.1007/s001970050050.
  • 8Dong P L. 2008. Generating and updating multiplicatively weighted Voronoi diagrams for point, line and polygon features in GIS. Computers and Geosciences, 34(4): 411-421 DOI: }0.1016/j.cageo.2007.04.005.
  • 9Elias B. 2003. Extracting landmarks with data mining methods. Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science. Vol. 2825 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.
  • 10ISO. 2004. Intelligent Transport Systems-Geographic Data Files (GDF)-Overall Data Specifications. ISO 14825.

共引文献229

同被引文献52

引证文献5

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