利用城市POI数据提取分层地标

摘要

For acquiring the hierarchical spatial knowledge to be applied in cognitive route directions, a method of extracting hierarchical landmarks from urban POI data according to their significances is proposed. After analyzing the factors influencing the significances of POI objects from public cognition, spatial distribution and individual characteristics, a significance measure model composed of three vectors which are public cognition degree, urban centrality degree and characteristic attribute value is constructed. Then, the processes of computing the vector values of POI objects are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively. An experiment is carried out to compute the significances of the POIs selected from the area of Wuchang region of Wuhan city, and the POIs with different significances are treated as landmarks in different levels at last. In this experiment, several levels of landmarks are extracted, and being used as seeds to compute weighted Voronoi diagrams in every level, to reflect the influence area of every landmark and associate the landmarks in the same level and between the sequential levels.%为了获取能够用于智能化路径引导的层次性空间知识,提出了一种依据显著度的差异从城市POI数据中提取出分层地标的方法.首先,通过从公众认知、空间分布和个体特征3个方面分析影响POI显著性的因素,构造了包括公众认知度、城市中心度和特征属性值3个指标向量的POI显著性度量模型;然后,分别讨论了利用问卷调查、多密度空间聚类和数据规格化的方法计算POI对象的各项显著性指标值的过程;最后,选择武汉市武昌地区的POI数据进行显著度计算,从中提取显著度较高的对象构成若干层地标,并以各层地标为种子生成加权的Voronoi图,用来反映各地标的空间影响范围并建立了同层和上下层地标之间蕴含的关系.