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.     

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.     

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.     

Towards a Better Understanding of Dynamic Interaction Metrics for Wildlife: a Null Model Approach

The ability to measure dynamic interactions, such as attraction or avoidance, is crucial to understanding socio‐spatial behaviors related to territoriality and mating as well as for exploring resource use and the potential spread of infectious epizootic diseases. In spite of the importance of measuring dynamic interactions, it has not been a main research focus in movement pattern analysis. With very few exceptions (see Benhamou et al. 2014), no new metrics have been developed in the past 20 years to accommodate the fundamental shift in the type of animal movement data now being collected and there have been few comparison or otherwise critical studies of existing dynamic interaction metrics (but see Long et al. 2014; Miller 2012). This research borrows from the null model approach commonly used in community ecology to compare six currently used dynamic interaction metrics using data on five brown hyena dyads in Northern Botswana. There was disconcerting variation among the dynamic interaction results depending on which metric and which null model was used, and these results highlight the need for more extensive research on measuring and interpreting dynamic interactions in order to avoid making potentially misleading inferences about socio‐spatial behaviors.     

三维虚拟地球中移动对象的时空数据组织方法

针对三维虚拟地球中实时GIS数据组织、管理与动态可视化的要求,设计了三维虚拟地球中移动对象的时空数据组织方法。提出了基于HR-tree扩展的时空索引方法,实现了对虚拟地球中移动对象的检索与查询,并基于三维虚拟地球平台对本文方法进行了实验,结果验证了本文方法的有效性和可行性。     

Using Spatially Explicit Simulated Data to Analyze Animal Interactions: A Case Study with Brown Hyenas in Northern Botswana

New developments in global positioning systems (GPS) and related satellite tracking technologies have facilitated the collection of highly accurate data on moving objects, far surpassing the ability to analyze them. Within geographic information science, ‘movement pattern analysis’ (MPA) has developed as a subfield that addresses concepts and theories used to explore the spatio‐temporal structure in data, although the methodological and analytical framework associated with MPA is new and still evolving. Interactions between individuals can be considered a second order property of movement and have been far less studied. The nature of interactions between individuals in a population is a fundamental aspect of a species' behavioral ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behavior, resource use, and infectious disease epidemiology. The focus of this work was to explore how spatially explicit simulated data can be used to analyse dynamic interactions between individuals. Five different techniques that have been used to quantify dynamic interactions based on GPS data of pairs of individuals were utilised, and all were compared in the context of spatially explicit simulated data intended to represent biologically realistic null models for individual movement, and subsequently paired interactions.     

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。     

面向全时段查询的移动对象时空数据模型研究

大量移动对象时空数据的有效管理是移动空间信息服务的一个新兴的研究内容。本文提出一个面向全时段查询的移动对象时空数据模型,并给出基本的模型定义;探讨移动对象过去、现在和将来的运动位置估算方法,这些方法包括延时法、滑动平均法、三次指数法等;研究了移动对象的状态更新策略,提出一个适应于更新时间间隔的变动阈值法的移动对象的状态更新策略,该策略采用时间滑动窗口形式建立移动对象状态更新间隔的一元指数回归方程,通过求取回归参数获得移动对象状态更新趋势,利用该参数动态调整状态更新闽值。最后,给出实验模拟结果,并对今后的研究工作进行了展望。     

Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method

Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.     

基于图像语义分割的动态场景下的单目SLAM算法

在恢复场景信息和相机运动时,传统的SLAM算法是基于静态环境假设的。场景中的动态物体会降低算法的稳健性和最终的定位精度。本文提出将基于深度学习的图像语义分割技术与传统的视觉SLAM算法结合,以减少动态物体对定位结果的干扰。首先,构建有监督的卷积神经网络对输入图像中的动态物体进行分割,获得语义图像;然后,从原始图像中提取特征点,并根据语义图像剔除动态物体特征点,保留静态物体特征点;最后,利用静态物体特征点采用基于特征点的单目视觉SLAM算法对相机运动进行跟踪。在ApolloScape自动驾驶数据集上的试验表明,与传统方法相比,本文算法在动态场景中定位精度提升约17%。     

Enrichment of topographic road database for the purpose of routing and navigation

With growing demand on multi-purpose or multi-modal navigation, the route calculation needs to traverse semantically enriched road networks for different transportation modes. Currently, operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the underlying geo-data that are separately maintained in different spatial databases. To overcome this limitation, a novel approach has been proposed to integrate the routing-relevant information from different data sources, which involves three processes: (1) automatic matching to identify the corresponding road objects between different datasets; (2) interaction to refine the automatic matching result; and (3) transferring the routing-relevant information from one data-set to another. In process (1), the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets, which has revealed a high matching rate and certainty. However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent. The remaining unmatched or wrongly matched objects are treated in process (2), with the help of a series of interaction tools. On the basis of refined matching results after the interaction, process (3) is dedicated to automatic integration of the routing-relevant information from different data sources.     

Assessment of the image misregistration effects on object-based change detection

High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ‘real’ changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ‘change’ objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used.     

校园典型地物波谱特性数据库系统研究

通过对山东科技大学若干典型地物如植被、水体、岩石的波谱特征测定、波谱图像处理,获得了较详细的地物波谱数据。结合当前地物波谱的研究现状及成果,建立了一个便于查询山东科技大学地物波谱特性的数据库系统。同时研究了地物波谱的动态变化规律,为进一步遥感数据分类等应用提供基础数据,为学生进行遥感实验提供基础资料。     

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。     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

运动目标轨迹的时空数据模型研究

运动目标的轨迹数据是空间信息系统的重要信息源。只有高效地利用运动目标的轨迹数据,才能更好地掌握多维动态变化的空间环境。本文在介绍了运动目标的概念和国内外研究现状之后,重点分析了运动目标的建模、存储、查询、索引及可视化技术并提出相应的解决方案。最终,实现对运动目标全过程的管理和应用。