网络中移动对象的二维时空索引

基于2DSTMON(2-Dimensional spatio-temporal indexfor moving objects in network)二维时空数据模型,提出了一种新的二维网络中移动对象的时空索引2DSTI及其时空查询算法。这种二维时空索引机制简单且易于实现,支持当前轨迹数据和历史轨迹数据的大量时空查询操作。在此基础上,通过实验实现并验证了二维时空索引机制及其时空查询算法。     

时空数据模型在城市测绘数据库的应用

城市测绘数据库更新面临的问题之一是如何建立有效的时空数据模型组织城市测绘时空数据.本文目的是建立适合城市测绘数据库的时空数据模型并应用这些模型存储、查询、管理城市测绘数据.     

差分式多时态多尺度空间数据库模型研究

针对如今多尺度地理空间数据库更新困难的问题,通过综合考虑时空数据模型与多尺度数据模型的特点,并根据多尺度地理数据差分式更新模式,设计出一种"层次—快照"式多尺度时空数据库模型。并通过数据关联表建立起多尺度多时空数据之间的数据联系。实验结果表明,该模型有利于多尺度数据的更新以及对地理数据的数据挖掘。     

森林资源空间数据更新的时空数据模型研究

地理信息系统(GIS)技术可以实现空间数据和属性数据统一存贮、管理和数据更新,但往往缺失时间数据,较难实现历史查询。本文以森林小班数据为例,分析林木自然生长以外数据更新的空间形态变化,使用图论对森林资源小班数据更新的时空信息进行描述,在此基础上构建森林资源更新的时空数据模型,此模型能够方便地进行历史查询分析。     

第二次土地调查农村土地利用数据库更新机制研究

从时空数据模型方面探讨了第二次土地调查农村土地利用数据库的更新机制,通过对不同时空数据模型的对比分析,采用连续快照和基态修正两种时空数据模型相结合的方式实现对数据库的更新,为第二次土地调查各级数据中心的农村土地利用数据库更新提供参考。     

多维空间联机分析处理模型及其应用

针对空间联机分析处理对于连续型时空数据缺乏从概念模型到代数理论形式化定义的一体化模型表达和计算范式的研究现状,以联机分析处理、时空地图代数和时空数据挖掘理论为基础,构建时空维分离的六元组立方体数据模型,定义时空维层次上度量集合的聚集计算,设计时间维空间聚集和非空间聚集两类函数,并在此基础上实现多维空间联机分析的基础操作原语.并以"城市大气污染物浓度时空变化特征"的探索性分析场景为例,验证该模型的有效性.     

时态GIS在森林资源更新管理方面的研究进展

随着地理信息系统(GIS)应用领域的不断深入,基于时态GIS技术的森林资源更新管理成为一个重要的研究方向。本文在森林资源小班数据更新分析的基础上,对比总结了当前主流的时空数据模型,介绍了时空数据库的构建形式和森林资源数据的更新流程,为时态GIS技术在森林资源管理中的进一步研究提供了方向。     

海洋GIS时空数据模型与应用

本文主要对已有的时空数据模型、海洋时空数据模型研究进展进行了评述,指出了各种模型的优点以及存在的问题;在此基础上,着重阐述了ArcGIS海洋数据模型中针对海洋要素产品时空数据的组织方法,并通过建立多维时空索引的方式对其进行改进,并运用到了"数字海洋"原型系统项目中。在一定程度上实现了"数字海洋"原型系统中海洋要素产品时空数据的组织与存储和可视化表达,取得了良好的效果。     

虚拟战场GIS数据模型研究

虚拟GIS数据模型是战场环境仿真GIS的最基本问题。本文在考虑到战场虚拟GIS表达地学虚拟环境中的各种数据需要的同时,借鉴当前GIS数据模型最新进展,以及虚拟现实和仿真领域的相关数据模型,设计了一个新的面向对象虚拟GIS数据模型(OOVRGDM)。OOVRGDM能同时表达二维空间地学数据和三维空间中的地学数据,并提供离散地理对象和战场地理对象的表达能力。     

基于过程的空间环境数据模型

针对现有时空数据模型的不足之处,该文在分析空间环境数据特点的基础上,将过程思想引入空间环境数据模型的研究中,将空间环境过程概括为点过程、线过程、面过程和场过程四类过程对象,并以此建立其逻辑模型和物理模型,并给出时空过程插值与聚合、查询和提取等时空分析方法,最后实例验证的结果表明:基于过程的空间环境数据模型可有效组织、管理空间环境数据。     

Modeling spatiotemporal topological relationships between moving object trajectories along road networks based on region connection calculus

Considering the attempts to model spatiotemporal topological relationships between moving object trajectories, the conceptual and computational framework for moving objects along a road network has not received much attention. This paper aims to draw an improved model based on Region Connection Calculus (RCC) theory to represent the spatiotemporal topological relationships between moving object trajectories along road networks. This paper first uses a dimension reduction method based on a linear-reference transformation to model the moving object trajectories segments, and then defines new time–connection and space–connection relations between two trajectory segments. On this basis, the paper proposes an extension to the RCC-based spatiotemporal binary relationship set so that the combined semantics of the spatiotemporal predicates can be described completely. A case study was carried out using Floating Car Data in Guangzhou city. The computational results show that in a real application, the occurrence frequencies of the RCC-based binary relationships are distributed nonuniformly and the semantics of some binary relationships with the highest occurrence are coarse. Therefore, the partition of the spatiotemporal connection relations and the finer aspects of the spatiotemporal relationship model may require further research work.     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

Traj2Traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration

In transportation, the trajectory data generated by various mobile vehicles equipped with GPS modules are essential for traffic information mining. However, collecting trajectory data is susceptible to various factors, resulting in the lack and even error of the data. Missing trajectory data could not correctly reflect the actual situation and also affect the subsequent research work related to the trajectory. Although increasing efforts are paid to restore missing trajectory data, it still faces many challenges: (1) the difficulty of data restoration because traffic trajectories are unstructured spatiotemporal data and show complex patterns; and (2) the difficulty of improving trajectory restoration efficiency because traditional trajectory interpolation is computationally arduous. To address these issues, a novel road network constrained spatiotemporal interpolation model, namely Traj2Traj, is proposed in this work to restore the missing traffic trajectory data. The model is constructed with a seq2seq network and integrates a potential factor module to extend environmental factors. Significantly, the model uses a spatiotemporal attention mechanism with the road network constraint to mine the latent information in time and space dimensions from massive trajectory data. The Traj2Traj model completes the road-level restoration according to the entire trajectory information. We present the first attempt to omit the map-matching task when the trajectory is restored to solve the time-consuming problem of map matching. Extensive experiments conducted on the provincial vehicle GPS data sets from April 2018 to June 2018 provided by the Fujian Provincial Department of Transportation show that the Traj2Traj model outperforms the state-of-the-art models.     

MASTER: A multiple aspect view on trajectories

For many years trajectory data have been treated as sequences of space‐time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all this information with trajectories. In this article we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vast range of applications, and, differently from state‐of‐the‐art methods, we propose a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against the state‐of‐the‐art spatio‐temporal database SECONDO extension for symbolic trajectories.     

利用道路网眼实现路网的增量式更新

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。     

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.     

Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes

Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.     

User-centered road network traffic analysis with MobilityDB

Performance indicators of road networks are a long-lasting topic of research. Existing schemes assess network properties such as the average speed on road segments and the queuing time at intersections. The increasing availability of user trajectories, collected mainly using mobile phones with a variety of applications, creates opportunities for developing user-centered performance indicators. Performing such an analysis on big trajectory data sets remains a challenge for the existing data management systems, because they lack support for spatiotemporal trajectory data. This article presents an end-to-end solution, based on MobilityDB, a novel moving object database system that extends PostgreSQL with spatiotemporal data types and functions. A new class of indicators is proposed, focused on the users' experience. The indicators address the network design, the traffic flow, and the driving comfort of the motorists. Furthermore, these indicators are expressed as analytical MobilityDB queries over a big set of real vehicle trajectories.     

网络地图缓存技术研究与应用进展

随着网络地图服务应用的延伸及并发访问数不断增加,网络地图服务平台的性能需求越来越高。地图缓存技术有效地减轻了服务器计算负载和网络传输负担,极大地改进了客户端和服务器端的交互效率,提升了网络地图服务应用水平,改善了客户端用户体验,是网络地图服务领域的研究热点之一。但其仍存在地图缓存制作时间长、占用空间大以及瓦片数据更新困难等技术难点。本文通过对目前主流的研究成果进行整理与归纳,重点分析了地图缓存技术各个环节的研究内容和现状以及分布式环境下的地图缓存技术研究进展,挖掘出了地图缓存技术在静态缓存瓦片数据更新、静动相结合的缓存策略切换、按需切图响应效率优化、文件系统与数据库组织模型同步、混合模式存储设计、大范围瓦片数据更新以及分布式环境下的地图瓦片数据弹性存储等方面可进一步提升的研究内容和技术环节,期望为后续的研究提供参考。