面向微地图的地标提取方法及个性化寻路应用

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。

Landmark Extraction Method and Personalized Wayfinding Application for We-Map

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 user-generated content, timely update, and dissemination of landmark data, providing reference for rapid we-map drawing, and improving wayfinding efficiency.