道路密度分区及其在道路选取质量评价中的应用  

Road Density Partition and Its Application in Evaluation of Road Selection

作　　者：田晶[1,2,3] 熊富全 程雪萍 王睿[1] 方华强[1] 

机构地区：[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]武汉大学地理信息系统教育部重点实验室,湖北武汉430079 [3]数字制图与国土信息应用工程国家测绘地理信息局重点实验室,湖北武汉430079

出　　处：《武汉大学学报（信息科学版）》2016年第9期1225-1231,共7页Geomatics and Information Science of Wuhan University

基　　金：国家基础科学人才培养基金武汉大学地理科学理科基地创新项目(J1103409)~~

摘　　要：道路密度信息广泛应用于景观分析、路网规划、城市边界提取与道路网综合等领域。提出了一种道路密度分区方法,该方法首先生成道路交叉点和端点的Voronoi图,然后运用G*i提取局部的Voronoi单元面积的高值聚集区与低值聚集区,最后将95%置信度下的高值区域和低值区域对应的相邻Voronoi单元合并。对Google地图上14级、13级和12级香港的道路网进行了实证研究,结果表明,该方法生成的密度分区大致反映了道路网的疏密,并较好地反映了选取前后各区域道路密度的对比规律。将本文方法与网格密度法进行对比,结果表明本文方法优于网格密度法。Road density is a useful index widely applied in the analysis of ecological effects,road network planning,delimitation of urban areas and road network generalization.Extraction of dense and sparse areas of the road network is a key issue in the filed of automated map generalization.This paper proposes a method for road density partition.The method creates the Voronoi diagram of road intersections and endpoints,and then uses G＊ito identify statistically significant spatial clusters of high values and low values for the area of a Voronoi cell.Finally,the method aggregates neighboring Voronoi cells from the statistically significant spatial clusters of high values and low values at a 95%confidence level.The road network of Hong Kong at the 14,13 and 12levels of Google Map are used as experimental data for evaluation of road network selection.Experimental results showed that the road density partitions using the proposed method generally reflected the density of the road network,while the road density contrasted well before and after road selection.Our method is superior to the grid density approach.

关 键 词：道路选取 道路密度 分区 VORONOI图 空间自相关 

分 类 号：P283.1[天文地球—地图制图学与地理信息工程] P208[天文地球—测绘科学与技术]