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.     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.     

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.     

实时GIS时空数据模型

为满足动态目标与传感器等实时观测数据获取、存储、管理、分析与可视化的要求,需要发展一种新型地理信息系统—实时GIS。本文根据实时GIS中各种地理要素的特点以及存贮管理要求,提出了一种面向动态地理对象与动态过程模拟的实时GIS时空数据模型,它将时空过程、地理对象、事件、事件类型、状态、观测等相关要素整合在一个时空数据模型中。基于该模型研发了新一代实时GIS,并以四种动态地理对象（包括移动对象、原位传感器对象、视频对象和过程模拟对象）的时空数据的实时接入、存储与可视化为例,验证的模型的可行性。     

Modeling Alternative Sequences of Events in Dynamic Geographic Domains

A model that emphasizes possible alternative sequences of events that occur over time is presented in this article. Representing alternative or branching events captures additional semantics unrealized by linear or non‐branching approaches. Two basic elements of branching, divergence and convergence are discussed. From these elements, many complex branching models can be built capturing a perspective of events that will take place in the future or have occurred in the past. This produces likely sequences of events that a user may compare and analyze using spatial or temporal criteria. The branching events model is especially useful for spatiotemporal decision support systems, as decision‐makers are able to identify alternative locations and times of events and, depending on the context, also identify regions of multiple possible events. A branching events viewer application is presented illustrating a case study based on a tornado response.     

单目智能视觉监视应用中小运动目标的检测

提出了以Cauchy分布作为图像像素比值的统计分布模型 ,并融合图像局部线性相关技术实现背景建模。该方法同时利用了像素点的统计特性和邻近像素点所蕴涵的丰富空间区域信息。实验表明 ,本文提出的算法可以抗背景中全局或局部光照的渐变和突变 ,有效地抑制背景中活动物体和阴影的杂波干扰。     

Time‐geographic Derivation of Feasible Co‐presence Opportunities from Network‐constrained Episodic Movement Data

Certain datasets on moving objects are episodic in nature – that is, the data is characterized by time gaps during which the position of the object is unknown. In this article, a model is developed to study the sparsely sampled network‐constrained movement of several objects by calculating both potential and feasible (i.e. more likely) co‐presence opportunities over time. The approach is applied to the context of a static sensor network, where the location of an object is only registered when passing a sensor location along a road network. Feasibility is incorporated based on the deviation from the shortest path. As an illustration, the model is applied to a large Bluetooth tracking dataset gathered at a mass event. The model output consists of maps showing the temporal evolution of the distribution of feasible co‐presence opportunities of tracked visitors over the network (i.e. the number of visitors that could have been present together). We demonstrate the model's usefulness in studying the movement and distribution of a crowd over a study area with relatively few sampling locations. Finally, we discuss the results with a special emphasis on the distinction between feasible and actual presence, the need for further validation and calibration, and the performance of the implementation.     

一种基于关键点的轨迹-区域拓扑过程模型

定位技术的广泛应用带来了铺天盖地的移动数据,为诸如时空查询和数据挖掘等各种时空的研究及应用提供了重要素材,使得对于轨迹数据的研究成为当前的一个热点。当前,无论是对于原始轨迹数据的研究,还是对语义化轨迹数据的研究,都较少考虑轨迹移动过程中所潜藏的拓扑不变量。本文提出二维空间上基于关键点的轨迹-区域拓扑过程模型,以矩阵描述轨迹与区域的14种基本点集拓扑交叠类型,既而组织交叠序列描述轨迹和区域对象间的拓扑关联关系。模型不仅描述了轨迹与区域之间的拓扑不变量,而且结合轨迹特有行为的语义关联模型,描述轨迹相对区域的复杂拓扑过程。同时,本文还以模型中相邻两次交叠的相接交叠模式,探讨了区域间拓扑关系对于轨迹移动描述的约束。     

Measuring Dynamic Interaction in Movement Data

The emergence of technologies capable of storing detailed records of object locations has presented scientists and researchers with a wealth of data on object movement. Yet analytical methods for investigating more advanced research questions from such detailed movement datasets remain limited in scope and sophistication. Recent advances in the study of movement data has focused on characterizing types of dynamic interactions, such as single‐file motion, while little progress has been made on quantifying the degree of such interactions. In this article, we introduce a new method for measuring dynamic interactions (termed DI) between pairs of moving objects. Simulated movement datasets are used to compare DI with an existing correlation statistic. Two applied examples, team sports and wildlife, are used to further demonstrate the value of the DI approach. The DI method is advantageous in that it measures interaction in both movement direction (termed azimuth) and displacement. Also, the DI approach can be applied at local, interval, episodal, and global levels of analysis. However the DI method is limited to situations where movements of two objects are recorded at simultaneous points in time. In conclusion, DI quantifies the level of dynamic interaction between two moving objects, allowing for more thorough investigation of processes affecting interactive moving objects.     

Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT

It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.     

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

GIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial-social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home-to-restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.     

Augmented virtual environment: fusion of real-time video and 3D models in the digital earth system

ABSTRACT

An Augmented virtual environment (AVE) is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment. In this paper, a new approach to establish an AVE with a wide field of view is proposed, including real-time video projection, multiple video texture fusion and 3D visualization of moving objects. A new diagonally weighted algorithm is proposed to smooth the apparent gaps within the overlapping area between the two adjacent videos. A visualization method for the location and trajectory of a moving virtual object is proposed to display the moving object and its trajectory in the 3D virtual environment. The experimental results showed that the proposed set of algorithms are able to fuse multiple real-time videos with 3D models efficiently, and the experiment runs a 3D scene containing two million triangles and six real-time videos at around 55 frames per second on a laptop with 1GB of graphics card memory. In addition, a realistic AVE with a wide field of view was created based on the Digital Earth Science Platform by fusing three videos with a complex indoor virtual scene, visualizing a moving object and drawing its trajectory in the real time.     

移动目标空间拓扑关系的度量描述

针对传统9交模型对移动目标空间拓扑关系表达及量化描述的不足,基于格网化思想,提出了度量参数量化描述的方法。首先对移动目标和参考目标进行格网化,划分为固定大小的格网阵列;然后利用9交模型确定空间目标间拓扑关系的形式化描述;最后引入内部分量、边界分量、外部分量3种度量参数对移动目标空间拓扑关系进行度量化描述。试验证明,该方法计算简单、直观,能更加有效地度量移动目标间的拓扑关系。     

基于地理事件的时空数据模型及其在土地利用中的应用

本文通过分析基于位置和基于对象的空间数据表达方法的特点,顾及与空间相关的属性与地理实体空间特征之间的相依关系,将“地理事件”引入模型,提出一种顾及“隐含”地理事件和“显式”地理事件的时空数据模型(GESTDM模型).本文提出的模型能够同时提供基于位置、基于对象和基于时间的时空表达和查询功能.将该模型应用于土地利用领域,...     

A time‐geographic approach to quantifying wildlife–road interactions

Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed.     

How to compare movement? A review of physical movement similarity measures in geographic information science and beyond

In geographic information science, a plethora of different approaches and methods is used to assess the similarity of movement. Some of these approaches term two moving objects similar if they share akin paths. Others require objects to move at similar speed and yet others consider movement similar if it occurs at the same time. We believe that a structured and comprehensive classification of movement comparison measures is missing. We argue that such a classification not only depicts the status quo of qualitative and quantitative movement analysis, but also allows for identifying those aspects of movement for which similarity measures are scarce or entirely missing.

In this review paper we, first, decompose movement into its spatial, temporal, and spatiotemporal movement parameters. A movement parameter is a physical quantity of movement, such as speed, spatial path, or temporal duration. For each of these parameters we then review qualitative and quantitative methods of how to compare movement. Thus, we provide a systematic and comprehensive classification of different movement similarity measures used in geographic information science. This classification is a valuable first step toward a GIS toolbox comprising all relevant movement comparison methods.     

基于多重运动特征的轨迹相似性度量模型

针对目前只能对单一运动特征（速度、方向等）进行轨迹相似性分析的不足,提出了基于多重运动特征的轨迹相似性度量,该度量对于分析和理解移动对象的运动行为和规律具有重要意义。将其应用于基于多重运动特征的运动序列模式发现。该相似性度量借鉴数据立方体的思想,将多重运动特征时间序列进行量化和符号化表示,在多重运动特征值域空间中计算两字符间的距离作为字符间替换代价,最终以加权编辑距离作为相似性度量。将该相似性度量与谱聚类方法相结合进行运动序列模式发现。实验以飓风数据为例,通过气象文献中飓风的发生与运动规律验证了模型的有效性。     

Indexing and querying moving objects with uncertain speed and direction in spatiotemporal databases

Efficient processing of spatiotemporal queries over moving objects with uncertainty has become imperative due to the increasing need for real-time information in highly dynamic environments. Most of the existing approaches focus on designing an index structure for managing moving objects with uncertainty and then utilize it to improve the query performance. All the proposed indexes, however, have their own limitations. In this paper, we devote to developing an efficient index, named the R ^lsd -tree, to index moving objects with uncertain speed and direction varying within respective known ranges. We design several pruning criteria combined with the R ^lsd -tree to answer the probabilistic range queries. Moreover, two models, the sampling-based probability model and the ER-based probability model, are proposed to quantify the possibility of each object being the query result. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.