民族人口分布专题信息表达的标签云布局方法

针对传统的民族人口分布专题图的表示方法往往不能同时兼顾人口规模和分布位置两类信息的表达的问题,该文基于标签云技术,提出一种面向民族人口分布专题信息表达的标签云布局方法。通过标签的构型和尺寸分别表示民族人口的类型和数量特征,并对标签的位置设置地理特征关联和制图空间约束,实现顾及空间分布特征的标签云布局。该文以云南省民族人口分布标签云地图为案例,验证了标签云布局算法的可行性,并对生成结果进行了测评。实验表明,采用标签云方法可获得更加直观的可视化效果,便于读者快速获取民族人口分布信息。该文提出的标签云布局方法可以有效地生成民族人口分布标签云专题图,算法执行效率较高。     

路网约束条件下热力规则设计与试验

热力图是一种能直观准确展示空间观测值的有效工具，在多个领域具有广泛应用。本文在分析设定不同道路层权重、道路技术等级分类的基础上，以反距离权重、直方图均衡化、密度补偿、参数多次迭代等方法，研究构建了以热力方式展示道路网分布及发达程度的相关规则体系。热力规则通过道路赋权、路线曲面化、密度协调、图面综合等多套规则的有机结合，实现了道路网总体发达水平信息的提取与展示，并以全球地理信息资源建设项目路网成果中的亚洲和非洲部分国家数据为例，进行了信息提取与热力分布试验。     

一种基于标签云的位置关联文本信息可视化方法

互联网的广泛应用产生了越来越多与地理空间位置关联的文本信息。现有地理信息系统一般通过外部链接来浏览这些数据,需要频繁的缩放、漫游和点击操作,而其他方法又难以有效表达出空间位置关系。提出了一种基于标签云的位置关联文本信息可视化方法———标签云地图,给出了标签云地图的设计思路和实现流程,并以腾讯微博的真实数据集为例建立了原型,重点研究了点状和面状地理要素的Cartogram生成算法,关键字和词频的提取算法,面向不同尺度和不同时间的标签云显示规则的标签位置生成算法。实验表明,该方法能够帮助用户从大量的位置关联文本信息中快速感知并把握信息的总体特征和发展趋势。     

地理标签数据的获取与组织管理相关技术的研究

针对地理标签数据和地理标签数据平台的特点,采用主题爬虫技术与API接口技术相结合的方法进行地理标签数据的获取;利用谬值处理、重复事件处理,并构建基于主题和位置的层次结构等方法对地理标签数据进行处理;设计相应的存储模型。最后还通过相应的实验进行对比分析,以此来验证本文相关技术的优越性。     

基于Geodatabase的小区热力管网几何网络建模及应用

Geodatabase几何网络模型是表达地理现象、进行空间分析的有效方法。针对传统的热力管网数据和基于图的数据模型描述地理对象拓扑关系时存在的缺点,在对热力管网原始数据分析的基础上,基于Geodatabase数据模型建立小区热力管网的几何网络模型及其约束规则。最后,基于所建立的几何网络模型,对小区热力管网进行网络关联分析,验证该模型应用的可行性。     

一种个人地理标记数据的可视化方法

个人地理标记数据是个人通过笔记本、平板电脑或者手机等设备发布的包含地理位置且与个人相关的文本、照片和视频等信息。本文针对个人地理标记数据的特点,提出了一种适用于地理标签数据的可视化方法——个人地理标记数据拓扑图,并设计和实现了核心算法。为了比较和评估该方法的效能,对"文本列表""普通地图"和"标记拓扑图"3种用户界面进行了可用性测试,并对测试数据进行了方差分析。测试结论表明,3种不同用户界面在查找个人地理标记数据的效率方面存在显著性差异,其中文本列表和标记拓扑图均优于普通地图,文本列表和标记拓扑图在查找时间上的差异并不显著,标记拓扑图在查找时间均值上略优于文本列表。     

基于DFT幅度的矢量地理空间数据数字水印算法

基于矢量地理空间数据自身的特点,运用离散傅立叶变换技术,提出了一种基于离散傅立叶变换的矢量地理空间数据数字水印算法。首先根据矢量地理空间数据的顶点序列构造复数序列,然后对该复数序列做离散傅立叶变换,将水印信息转换为符合N(0,1)分布的伪随机水印序列嵌入到变换后的幅度中,再进行离散傅立叶逆变换得到含水印信息的矢量地理空间数据。提取水印时,通过比较嵌入水印的数据与原始数据之间的差异提取出原始水印信息。实验分析表明,该算法在抗矢量地理空间数据处理中常见的删点、数据格式转换、平移、旋转等方面具有较好的效果。     

面向不平衡POI类别分布的电子地图多层次标签预测模型EI北大核心CSCD

兴趣点(POI)是电子地图、导航等应用关注的主要要素之一,其数据质量直接影响地理信息服务的智能化水平。鉴于OpenStreetMap(OSM)等众源地理信息数据的非专业收集特征,其POI数据标签常存在缺失、标记错误等质量问题,亟须对POI标签进行智能化推断和增强处理。常规神经网络模型直接从单一层次预测多类别数据,未考虑POI类别在数量上分布不平衡的问题,其预测标签倾向于包含较多数据的类别,学习算法难以泛化小规模样本规则。本文考虑到不同POI类别间的数据规模差异较大,提出基于多层次POI类别组织的神经网络预测方法,通过小样本类别的层次化聚合,建立POI类别树结构,在树结构的不同层次上实现数据规模相对平衡的类别划分,支持神经网络高精度的标签预测。试验表明,本文方法仅需利用POI基础位置信息与邻近关系,其预测精度高于传统方法。     

空间尺度与语义尺度联合控制下的地理国情地图综合

地理国情数据具有精度高、数据量大、语义信息丰富的基本特征,是地图产品表达与服务应用的基础数据.为应对多层次服务、多领域应用、多粒度表达与分析的需求,在基础调查数据基础上,通过尺度变换获得多比例尺表达的数据版本很有必要.地理国情地图综合属于专题地图综合范畴,与以实现空间特征简化为目的的普通地图综合相比,其地图综合要更多地关注语义特征的概括,并强调综合结果语义特征的一致.因此,空间特征约束与语义特征约束联合控制将成为地理国情地图综合的主要特点.论文以现有地图综合理论及方法为基础,根据地理国情数据特点及综合需求,对专题地图综合理论和方法进行拓展,针对地理国情数据尺度变换的综合规则构建、综合操作算法设计、综合结果质量评价等问题进行研究,为工程化应用中快速、高效、准确地实施数据多尺度变换提供技术支撑.     

城市地理空间数据集成应用探讨

城市地理空间数据是表达城市地理要素的数量、质量、分布特征、相互关系、变化规律的位置信息和属性信息。分析城市地理空间数据的组成结构、组织管理、集成利用,并将这些数据有效应用于城市地理信息系统建设之中,将在城市信息化道路上起到举足轻重的作用。该文以开发的"兰州市城关区地理信息集成系统"为例,重点阐述了城市地理空间数据的构成与获取方式,研究了城市地理空间数据与业务部门数据的集成方法,并对城市地理空间数据在晕渲图制作、三维景观生成、数据可视化等主要应用上进行了分析和探讨。     

Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference

As mapping is costly and labor‐intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC‐based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps.     

Modeling Use of Space from Social Media Data Using a Biased Random Walker

Individuals and other entities move through space as a function of local characteristics of place, their internal behavioral models, and the topological structure of the underlying space. When a collection of locations (i.e. geotagged photos or other geotagged social media information) from a large number of individuals is assembled, it becomes possible to understand the interrelationship between the individuals and the space they occupy. This research systematically considers this interrelationship through an examination of the effect of the intersection of behavioral and spatial characteristics on individuals moving on street networks. The research illustrates how social media data, in combination with a biased random walker, can be used to understand and model the interaction of spatial structure and social‐environmental factors on influencing individuals' use of their environment. The biased walker offers a flexible approach to incorporate consideration of both social‐environmental and structural factors into a model and we demonstrate this through a case study wherein we are able to use the random walker to model the characteristics of Flickr users in New York City.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.     

Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.     

Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information

Mobile in‐situ sensor platforms such as Unmanned Aerial Vehicles can be used in environmental monitoring. In time‐critical monitoring scenarios as for example in emergency response, and in the exploration of highly dynamic phenomena, obtaining the relevant data with one or few mobile sensors is challenging. It requires an intelligent sampling strategy that integrates prior information and adapts to the dynamics of the observed phenomenon, based on the collected sensor data. Available information about the observed phenomenon may be incomplete or imprecise and therefore insufficient for quantitative modeling. We address this problem by reasoning about the plume movement and size on a qualitative level and present an algorithm for tracking a dynamic plume that integrates this qualitative information with the collected sensor data. We evaluate our algorithm using simulated data sets of three different moving and expanding gas plumes. By means of simulations we show that the qualitative methods can be used to infer new information about the properties of a moving plume and to adapt the sensor movement for tracking the plume. Both can be done with low computational effort, without absolute positioning capability of the sensor, and with less input information than required by quantitative approaches.     

Crowd sensing and spatiotemporal analysis in urban open space using multi-viewpoint geotagged videos

Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi-viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single-camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi-viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3-DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi-viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos.     

神经网络辅助的GPS/INS组合导航自适应UKF算法

GPS/INS组合导航非线性系统最优估计算法中,基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声。本文针对观测异常会影响残差向量的数字特性分布,提出了一种神经网络辅助的多重渐消因子自适应SVD-UKF算法。该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响,利用奇异值分解抑制UKF中先验协方差矩阵负定性变化,同时构造多重渐消因子对预测状态协方差阵进行调整,使得不同的滤波通道具有不同的调节能力,高效地应用于多变量复杂系统。最后利用车载实测数据进行了验证。结果表明,神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响,拓展了多重渐消因子的应用范围,使其能在观测值含有粗差的条件下自适应调节不同滤波通道,消除滤波状态中的异常,提高组合导航解的精度和可靠性。