An innovative GPS trajectory data based model for geographic recommendation service

Geographic services based on GPS trajectory data, such as location prediction and recommender services, have received increasing attention because of their potential social and commercial benefits. In this study, a Geographic Service Recommender Model (GSRM) is proposed, which loosely comprises three essential steps. Firstly, location sequences are obtained through a clustering operation on GPS locations. To improve efficiency, a programming model with a distributed algorithm is employed to accelerate the clustering. Secondly, in order to mine spatial and temporal information from the cluster trajectory, an algorithm (MiningMP) is designed. Last but not least, the next possible location to which the user will travel is predicted. An integrated framework of GSRM could then be constructed and provide the appropriate geographic recommendation service by considering location sequences as well as other related semantic information. Experiments were conducted based on real GPS trajectories from Microsoft Research Asia (182 users within a period of five years). The experimental results clearly demonstrate that our proposed GSRM model is effective and efficient at predicting locations and can provide users with personalized smart recommendation services in the following possible position with excellent performance in scalability, adaptability, and quality of service.     

室内用户语义位置模式挖掘研究——以商场为例

结合室内轨迹数据的特点,研究了室内移动对象语义轨迹序列处理方法,以及利用LDA主题模型与用户历史轨迹进行室内商场用户分类的方法。提出了通过关联规则分析挖掘用户语义位置模式的方法,并以北京某大型商场的实际用户轨迹数据为例,对所提出的方法进行了实验验证。     

Multidimensional Similarity Measuring for Semantic Trajectories

Most existing approaches aiming at measuring trajectory similarity are focused on two‐dimensional sequences of points, called raw trajectories. However, recent proposals have used background geographic information and social media data to enrich these trajectories with a semantic dimension, giving rise to the concept of semantic trajectories. Only a few works have proposed similarity measures for semantic trajectories or multidimensional sequences, having limitations such as predefined weight of the dimensions, sensitivity to noise, tolerance for gaps with different sizes, and the prevalence of the worst dimension similarity. In this article we propose MSM, a novel similarity measure for multidimensional sequences that overcomes the aforementioned limitations by considering and weighting the similarity in all dimensions. MSM is evaluated through an extensive experimental study that, based on a seed trajectory, creates sets of semantic trajectories with controlled transformations to introduce different kinds and levels of dissimilarity. For each set, we compute the similarity between the seed and the transformed trajectories, using different measures. The results showed that MSM was more robust and efficient than related approaches in the domain of semantic trajectories.     

Mobile user identification based on road networks and multiple distance measures

Mobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.     

顾及相似用户特征的个人位置预测算法

个体位置预测在传染病精准防控、公共设施科学规划等应用中具有重要意义,既有位置预测算法主要侧重对个体纵向历史轨迹特征进行挖掘建模,从而实现位置预测,对横向相似性用户的规律特性考虑较少。因此,基于图卷积和长短期记忆模型（long short-term memory,LSTM）框架,提出顾及横向相似用户轨迹特征以及纵向历史规律性特征的个体位置预测算法。首先,构建用户轨迹相似性算法并筛选高相似度用户;然后,利用图卷积模型提取待预测用户相似高的用户轨迹特征;最后,利用LSTM框架提取历史轨迹特征,集成相似用户轨迹特征,从而实现个体位置预测。基于某市8万多个用户连续4个工作日的数据进行实验,结果表明,所提算法的准确率随预测时间步长增加而下降,而夜间预测准确率明显高于白天,但相比于既有模型均有10%以上提高;以15 min为预测时间步长时,模型准确率达80.45%。     

Effects of reverse geocoding on OpenStreetMap tag quality assessment

OpenStreetMap (OSM), a widely-used open-source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.     

视频与GIS协同的交通违规行为分析方法

针对人工监测无法及时、高效地发现视频中车辆和行人违规情况的不足,构建了地理空间视角下,兼顾动态目标在地理场景下的属性、时空关系和语义信息的轨迹模型(trajectory model,Tra-Model),设计了视频与GIS协同的交通违规行为分析方法,基于轨迹与规则几何约束条件对目标逆行、压线、禁止进入3类违规情况进行实时检测。以某高校为实验场地,分析不同交通场景下目标的3类违规情况。实验结果表明：（1）Tra-Model模型提取的轨迹包含目标类型、轨迹序列等语义信息,相比于现有动态目标跟踪算法,精准率提高15.6%;(2)所提方法对多个摄像机序列违规分析准确率均在70%以上,相对于现有方法具有更好的性能;（3）实现了地理场景下多种交通违规行为综合分析的全局性、高精度和探测类型多样性。     

Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey

Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.     

移动轨迹聚类方法研究综述

轨迹数据是人类移动行为的表征,能够映射出人的出行模式和社会属性等信息。怎样有效挖掘轨迹数据蕴藏的人类活动规律一直是研究的热点。通过轨迹聚类发现行为相似的类簇,从而探究群体的移动模式是轨迹挖掘和深度应用常见的方法之一。本文首先根据轨迹数据的特点,将轨迹数据模型分为轨迹点模型和轨迹段模型,并据此定义相应的相似性度量：空间相似性度量和时空相似性度量;然后,对两类模型的聚类方法进行了综述,并总结不同聚类算法的优缺点,以期为不同应用选取聚类算法提供科学依据;最后对移动轨迹数据聚类方法研究的发展趋势进行了讨论。     

The hypermedia data model based on the infinity image

This paper presents the hypermedia data model based on the infinity RS image information system we have developed. The hypermedia data model consists of different semantic units called nodes, and the associations between nodes are called links. This paper proposes three kinds of nodes (interior node, physical node and complex node) and two kinds of links (plane network structure link, hyper-cube network structure links). The hypermedia information system, based on the model and the basic data layer (the infiniy RS image), represents a digital globe. An approach to the “Getting Lost in the Hyper-space” problem is presented. The approach using the hypermedia data model is an efficient way of handling a large number of RS images in various geographical information systems.     

The hypermedia data model based on the infinity image

This paper presents the hypermedia data model based on the infinity RS image information system we have developed.The hypermedia data model consists of different semantic units called nodes,and the associations between nodes are called links.This paper proposes three kinds of nodes (interior node,physical node and complex node) and two kinds of links (plane network structure link,hyper-cube network structure links).The hypermedia information system,based on the model and the basic data layer (the infiniy RS image),represents a digital globe.An approach to the "Getting Lost in the Hyper-space" problem is presented.The approach using the hypermedia data model is an efficient way of handling a large number of RS images in various geographical information systems.     

基于地名树的最佳空间尺度新闻事件地点提取方法

从新闻纯文本数据中识别地名信息并确定对应的最佳空间尺度与事件所属地点，是准确抽取新闻事件发生地点的关键。针对上述目标，提出了基于隶属关系地名树的最佳空间尺度新闻事件地点提取方法。在完成地名实体识别和歧义消除的文本数据预处理工作的基础上，提出了一种顾及新闻结构的方法消除语义干扰等噪声的影响；通过引入虚父节点构建合理准确的隶属关系地名树，结合最小包围盒的概念实现了最佳空间尺度的选取，使用地名实体权重和实体相关性完成了事件地候选集推荐排序，从而合理定位事件发生地。实验证明，所提出的新闻文本地理信息抽取方法可以较高的准确率获取新闻所对应的最佳空间尺度和相应的事件地点。讨论和解决了新闻文本地理信息抽取涉及的空间尺度问题，使得新闻文本中抽取的地理信息具有更好的可用性和可解释性，在丰富地理信息数据来源的同时，可实现数量呈几何级增长的网络新闻自动地域划分，有助于人们对各类事件空间态势的关注与认知。     

移动轨迹的尺度特征

移动轨迹是移动对象在地理空间环境中移动而留下的痕迹。移动轨迹数据是一类特殊的地理时空数据，它具有时间尺度、空间尺度和语义尺度特征。本文首先分析了轨迹数据的空间尺度特征与时间尺度特征，建立了轨迹空间尺度与时间尺度转换关系式；然后论述了轨迹的语义内涵和语义尺度特征，将轨迹语义分为移动对象语义、地理空间环境语义、采集设备语义、移动方式语义，并从地理空间环境语义的角度分析了轨迹的语义多尺度；最后探讨了轨迹语义尺度与时空尺度之间的一致性关系。     

轨迹数据的非关系管理及相似性分析

针对高动态、半结构化的轨迹数据，充分利用文档型非关系数据库MongoDB的特性，本文首先提出了一套分层、分区、分片的存储策略，设计了以整条轨迹为基本粒度的非关系组织模型，能够有效应对轨迹数据的海量性和动态性挑战。然后据此开展轨迹相似性分析的研究，提出了一种兼顾时间维和轨迹形状的轨迹相似性度量方法DTWEUCLI，可计算长短不一且含有噪声的轨迹数据之间的相似性。最后基于轨迹的非关系存储和相似性计算，开展了轨迹簇生成的试验与分析，设计实现了基于轨迹相似性计算的轨迹聚类计算框架。基于3个轨迹数据集的试验表明，DTWEUCLI算法能够对多源轨迹数据集进行有效聚类，输出轨迹簇。     

检索应用中地理信息资源相似度计算方法研究

在地理信息资源检索应用中,资源属性间相似关系的计算还不够深入,地理信息资源的精准推荐及检索可视化应用尚难以实现。针对该问题,统一采用"相似度"对资源属性间的相似关系进行表达。资源的属性类型大部分为文本,其中具有语义差异,文中提出复合文本的相似度算法对文本类型属性进行匹配,并给出数值类型属性的"相似度"映射方案。在此基础上,实现资源时间、空间属性的相似度计算。基于所提出的相似度算法进行地理信息资源检索实验,实现地理信息资源属性间相似关系的量化,促进信息可视化在地理信息资源检索中的应用。     

无需相似性量测的多片影像匹配

讨论一个摄影测量网络的三维自动重建。其中,重点讨论了无需相似性量测的多片影像匹配。该匹配算法包括了所有影像作为参考影像的连续考虑和整个网络同时匹配。本文中,三维坐标矢量只用于剔除多余候选点,对精度提高无任何贡献。本方法的精度是通过子像素的特征点提取和多片交会得到的。     

Towards semantic‐aware multiple‐aspect trajectory similarity measuring

The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories.     

基于语义相似度的海量遥感图像可视化研究

针对海量遥感图像如何有效的传达分类结果以实现有效的可视化问题,本文按照分类的语义标注结果的相似度并运用现有的信息可视化技术来实现图像的可视化。首先采用了贝叶斯网络学习的方法进行图像的自动分类标注,然后利用基于图像布局的多维标度算法(Multi-dimensional Scale)以及无需降维的Value and Relation(VaR)技术实现可视化。实验表明本文的方法能够填补图像低层视觉特征和高层语义之间鸿沟,对大量的图像在一个视图内进行有效的浏览,而不造成图像的混乱,并能实现高层次的图像分析。实验的可视化结果是十分有效的。     

Trajectory Ontologies and Queries

Many real world applications today are built on analyses of movement and related features. Examples of such applications include transportation management, urban planning, tourism services, and animal migration monitoring, among others. Recent database modeling and management research prototypes have the capability to store and manipulate movement data in terms of point or region geometries that evolve over time (moving point or moving and deforming region). This captures the spatio‐temporal trace left by a moving object, but ignores its links with non‐geometric information that enable a semantic interpretation of the movement of moving objects. The concept of trajectory has been introduced to express a more semantic understanding of movement, taking it closer to the perception of applications. This article describes a framework for a semantics‐oriented structuring, modeling and querying of trajectory data. The framework relies on the definition of trajectory‐related ontologies, addressing domain‐independent and application‐specific geometric and semantic facets. Last we briefly discuss how the proposed approach has been applied for a traffic management application.     

利用作战任务本体计算战场环境要素语义相似性

如何根据作战任务需求为用户快速提供有针对性的有效数据，是战场环境数据保障实现主动服务必须解决的问题之一。针对作战任务与战场环境数据关联关系复杂，目前方法上存在缺少数据匹配语义表达、语义相关度及不同要素类语义权重量化等问题，提出了一种基于作战任务本体的战场环境要素语义相似性计算方法。利用本体方法确定作战任务与战场环境数据在概念语义上的逻辑关系，按照作战任务语义相关度划分战场环境要素，采用规则与案例相结合的推理技术建立语义相似性计算，从而量化战场环境要素实体语义权重。实验结果表明，该方法能有效提升战场环境数据组织的效率与精度。