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基于道路网络分析的Voronoi面域图构建算法 总被引：3，自引：3，他引：0
提出一种基于网络分析的Voronoi面域图和加权Voronoi面域图构建算法。鉴于道路网络在城市中心地、公共设施引力传导与功能覆盖上的重要作用,采用网络最短路径距离分析和最短路径时间分析构建的Voronoi面域图可以模拟出中心功能的辐射影响范围空间划分的实际情形,进而为空间分析和空间优化提供有力支持。算法过程主要包括:设施邻近道路结点检索和分界结点计算;基于网络最短路径分析Dijkstra算法和分界结点计算的网络Voronoi划分;基于空间离散化、邻近道路分析的空间Voronoi划分及其矢量化处理算法。计算实验结果表明本文提出算法可靠和高效,能够模拟出具有预期精度和形态复杂的网络Voronoi面域图形。 相似文献
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车辆轨迹数据的道路信息提取是地理信息领域的热点也是难点之一,深度学习的快速发展为该问题的解决提供了一种思路与方法。本文针对车辆轨迹数据的车行道级道路提取问题,引入深度学习领域的生成式对抗网络,利用残差网络构建深层网络和多尺度感受野感知轨迹数据不同细节特征,构建了基于条件生成式对抗网络的轨迹方向约束下车行道级道路提取模型。首先提出了朝向-颜色映射栅格化转换方法,实现轨迹朝向信息向HSV颜色空间的转换;然后利用样本数据学习模型参数;最后将训练模型应用到郑州、成都、南京3个试验区域提取车行道级道路数据。试验结果表明,本文方法能够有效地提取完整的车行道级道路数据。 相似文献
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道路网络背景下的距离度量（如道路网络距离、旅行时间）是在空间分析或空间统计过程中常用的距离度量,但在科研过程中由于道路数据的可获得性和精度等方面的限制,该类距离的计算可能较为困难。Minkowski距离函数是欧氏空间中的广义距离函数,其参数p值的不同代表着对空间不同的度量。利用Minkowski的通用性和灵活性（参数p不同的取值）,研究如何更好地逼近道路网络距离。同时,探索不同道路网络的部分计量特征（如密度、弯曲度等）与最优p值之间的关系。实验证明,相对于最常用的欧氏距离度量,优选p值后的Minkowski距离函数能够更大程度上逼近道路距离。而通过对道路网络计量特征与最优p值之间的关系的分析,指出了弯曲度与最优p值之间的对应关系,它对于p值的选择具有重要的指导意义。此外,为了验证Minkowski距离逼近算法的可行性,以地理加权回归分析为例,通过对比传统的欧氏距离度量、最优Minkowski距离度量和道路网络距离（旅行时间）对模型解算结果的影响,指出优选后Minkowski距离一定程度上更接近于采用旅行时间对模型解算的结果。 相似文献
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针对当前的存量要素构建地理实体的流程和方法存在较多的人工编辑工作量的问题，该文设计了一种城市存量道路要素自动构建地理实体的方法。该方法首先基于道路连通度和道路名称确定道路实体完整中心线，其次利用道路面片段和完整中心线的道路名称、道路类型、最大距离、相交长度、Hausdorff距离小于50 m的部分占比等指标综合匹配生成道路面片集，最后根据空间关系完成道路实体的概略图形、实体标识码、图元和属性自动组装。基于上海存量道路数据自动构建道路实体的实验表明：该方法构建效率高，准确率达到94.6%,召回率达到95.0%,可以有效地缩短构建周期和节约成本，研究结果可以应用于各省市道路实体构建，也可应用于河流类地理实体构建。 相似文献
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大比例尺地图中双线道路的识别与提取是路网综合的重要组成部分。针对双线道路识别问题,根据双线道路的结构特点,结合国内外研究提出了一种可用于提取双线道路的距离度量方法——正对投影距离。首先通过缓冲区分析构建可能构成双线道路的候选线对集,然后利用正对投影距离构造约束参数从线对候选集中精确识别出双线道路线对。通过与其他距离度量方法的对比试验,表明正对投影距离能较准确表达双线道路线对的空间邻近性,可以准确识别出双线道路,符合人类空间认知特点。 相似文献
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道路网络通达性是区域交通体系建设所要考虑的重要方面。本文以GIS为技术手段,通过重力度量模型、道路加权和密度模型计算得出经济联系指数和道路加权核密度指数,并构建综合通达性指数来分析湖北省县域道路网络综合通达性及其空间特征分布。结果表明:(1)湖北省103个县经济联系指数以武汉市主城区最高;(2)道路加权核密度指数以武汉市东西湖区最高;(3)道路网络综合通达性最高的5个县级行政单元为硚口区、汉阳区、江岸区、江汉区、东西湖区。本文填补了目前国内省级交通网络通达性方面的研究空白,为湖北省道路交通网络的建设和布局优化提供了参考依据。 相似文献
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《武汉大学学报(信息科学版)》2016,(9)
地图上道路的移位、变形或符号化往往会产生道路与其周边建筑物的空间冲突。为了尽量保持道路旁的建筑物群沿线分布的基本规律,提出了把建筑物群移位转换为线移位问题的一种移位方法,能协同处理好道路与建筑物之间的空间冲突,并利用实验验证了所提出方法的合理性和有效性。该方法首先需要确定潜在的空间冲突区域,并依据空间冲突区内的地图要素类别确定空间冲突类型;然后,判断落入空间冲突区内的建筑物群,用建筑物中心点到道路的垂线表示建筑物与道路的关联关系,并把与这些落入空间冲突区域内的建筑物最邻近的距离小于阈值的建筑物归类到相应的建筑物子群,对每个这样的建筑物子群,建立其中心点的最小生成树(minimum spanning tree,MST);最后,把这些垂线、MST和道路看成一个线状要素网络,利用Snake模型进行协同移位处理。 相似文献
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道路网变化较快,对其匹配和更新受到广泛关注。目前道路网匹配算法多集中于离散弧段间的匹配,匹配整体性不强,匹配精度不高。本文在分析道路匹配关系的基础上,借鉴人类认知客观世界从整体到局部的思想,对离散道路弧段通过Stroke模型进行聚合处理,然后采用线、面相对位置关系探测候选匹配集,进行道路筛选及整体匹配。针对当前匹配方法中各相似权值确定较为随意的问题,本文在分析各相似指标关系的基础上,借鉴层次分析法中的决策思维方式,对道路网匹配中的各相似指标进行定性和定量分析,并自动分配权值,进而根据获得的整体相似度来自动匹配。实验结果表明,采用层次分析法进行道路网整体匹配,可有效提高匹配效率、正确率及自动化水平。 相似文献
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获取现势性的交通道路数据是数字城市和智慧城市建设的基础，基于传统测绘的道路网更新方法存在一定局限性，而基于众源数据及行车轨迹数据更新道路网近年来则倍受关注。首先提出了一种新的道路变化增量更新方法，该方法先对历史道路网建立面拓扑结构，生成由道路网组成的最小闭合面域（道路网眼）；然后以道路网眼为基本控制单元，综合利用轨迹点上下文距离信息和隐马尔可夫模型（hidden Markov model，HMM），提取失配轨迹点和失配轨迹段；最后采用缓冲区分析和最大密度法对失配轨迹提取骨架线，创建新增道路，增量更新历史道路网。实验结果表明，以道路网眼为控制单元，利用轨迹点上下文距离分析和HMM捕获失配轨迹点，可提高失配轨迹点的提取效率，改善道路网更新效果。该方法可用于大规模路网的增量式更新。 相似文献
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Network Density and the Delimitation of Urban Areas 总被引：3，自引：0，他引：3
Giuseppe Borruso 《Transactions in GIS》2003,7(2):177-191
This paper examines network analysis for urban areas. The research is focused on the problem of definition and visualisation of network geography and network spaces at different scales. The urban scale of analysis is examined and different spatial indices are considered. The built environment of the city is considered as a reference environment for a road network density index. The latter is implemented in order to study the spatial interactions between network phenomena and spaces and to provide further elements for the analysis of urban shape. The study is focused in particular on understanding spatial patterns drawn by networks and in helping with the delimitation of city centres. Different approaches are used to obtain the two indices: a grid–based analysis and a spatial density estimator based on Kernel Density Estimation. The two methodologies are analysed and compared using point data for the urban road network junctions and street numbers as house location identifiers in the Trieste (Italy) Municipality area. The density analysis is also used on road network junctions' data for the city of Swindon (UK) in order to test the methodology on a different urban area. 相似文献
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The network K‐function in context: examining the effects of network structure on the network K‐function 
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下载免费PDF全文 The flaws of using traditional planar point‐pattern analysis techniques with network constrained points have been thoroughly explored in the literature. Because of this, new network‐based measures have been introduced for their planar analogues, including the network based K‐function. These new measures involve the calculation of network distances between point events rather than traditional Euclidean distances. Some have suggested that the underlying structure of a network, such as whether it includes directional constraints or speed limits, may be considered when applying these methods. How different network structures might affect the results of the network spatial statistics is not well understood. This article examines the results of network K‐functions when taking into consideration network distances for three different types of networks: the original road network, topologically correct networks, and directionally constrained networks. For this aim, four scenarios using road networks from Tampa, Florida and New York City, New York were used to test how network constraints affected the network K‐function. Depending on which network is under consideration, the underlying network structure could impact the interpretation. In particular, directional constraints showed reduced clustering across the different scenarios. Caution should be used when selecting the road network, and constraints, for a network K‐function analysis. 相似文献
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Ran Tao Daniel Strandow Michael Findley Jean‐Claude Thill James Walsh 《Transactions in GIS》2016,20(3):413-425
Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time. 相似文献
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Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained. 相似文献
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道路网是最重要的地理空间要素之一,空间数据融合能够把不同来源道路空间数据或信息加以结合,以获得信息量更丰富或更适于处理、分析、决策的新的数据集。传统的方法受限于道路网数据模型、属性数据类型以及缺少唯一标识的属性信息,道路网融合方法多以各个弧段或道路的位置、形状、方向等几何特征进行匹配,而忽略了道路的语义匹配。本文在数据来源与技术分析的基础上,提出了一种在工程化应用中可行的语义与几何相结合的道路网匹配方法,并通过FME实现空间数据融合,旨在为两个或多个道路网数据融合、联动更新提供方法参考。 相似文献