Figure 7 - uploaded by Willem Robert van Hage
Content may be subject to copyright.
Map of Rotterdam harbor with a trajectory of a passenger ship created with Presto, GeoNames features as placemarks, and polygons with harbor types (L=Liquid bulk, D=Distribution, G=General Cargo, DB=Dry bulk, O=Other cargo, and P=Passenger).
Source publication
We present an integrated and multidisciplinary approach for analyzing the behavior of moving objects. The results originate from an ongoing research of four different partners from the Dutch Poseidon project (Embedded Systems Institute (200714.
Embedded Systems Institute, 2007. The Poseidon project. [online] http://www.esi.nl/poseidon (http://www.e...
Context in source publication
Context 1
... Port of Rotterdam Authority has published maps 2 with a classification of their different harbors (e.g. liquid bulk, food), which we have converted to Google Earth KML (see Fig. 7). We used the SWI-Prolog Space package to convert the KML placemarks with polygon shapes into RDF with GeoRSS polygon literals. The SWI-Prolog Semantic web package can then be used to enrich events related to this ...
Similar publications
Based on OMTDR technology, the first embedded Smart Connector (SmartCo) which could locate very small soft defects resulting from partial degradation of cables is introduced in this paper. To do so, the SmartCo injects the generated OMTDR signal at an extremity of the cable and then listens to the echoes created at each discontinuity of the cable c...
Citations
... It reflects changing patterns over time relative to the nonspatial information of research objects, where characteristics of specific attributes are usually encoded by visual elements (Cuenca et al. 2018;Willems et al. 2010;Ryoo et al. 2018;Scheepens et al. 2014;Guo et al. 2011;Adrienko and Adrienko 2010;Andrienko and Andrienko 2008;Hu et al. 2019;Wallner et al. 2019;Zhu and Guo 2014). For example, Scheepens et al. (2014) suggested the design of a glyph. ...
... In addition to explicit attributes, such as visual encoding and visual models, there are also implicit attributes, such as path shape as a continuous geometric attribute feature. Another example is the safety monitoring system designed by Willems et al. (2010). It displays the correlation between a pair of attributes in a trajectory contingency table. ...
Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis, a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness.
... Within the first group, we identified a set of works that made use of data come from official bodies (Brodeur & Bédard, 2002;Balley et al., 2004;Chen et al., 2005;Gomez-Perez et al., 2008;Baru & Lin, 2009;Sheeren et al., 2009;Khatami et al., 2010;Willems et al., 2010;Prager & Barber, 2012 The aforementioned contributions used mainly data sources associated with the U.S. Geological Survey, U.S. Census Bureau, Spanish or French National Mapping Agencies. In this context, some resource types were databases, topographic maps, aerial photographic surveys, digital elevation models, digital orthophoto maps, images from satellites or Unmanned Aerial Vehicles (UAVs), thematic data, or gazetteers (e.g. ...
... Niestroj et al., 2018;Ajmar et al., 2019;M.G. Niestroj et al., 2019), streets (Peng, 2005), railroads (Lin et al., 2018), marine (Willems et al., 2010) and aviation routes (Khodizadeh-Nahari et al., 2021), or a combination of several network elements (such as streets, roads, and railway) and domains (infrastructure and hydrographic networks) (Costes & Perret, 2019). ...
... elements, the OntoRoad ontology, described in (Chaabane & Jaziri, 2015), and the ontologies from National Cartography Center and the Transportation and Terminal Organization, respectively. Furthermore, Willems et al. (2010) built an ontology based on other models such as the Event Ontology (Raimond & Abdallah, 2007), LODE (Shaw et al., 2009), and the F model (Scherp, 2009). Moreover, in (Chaabane & Jaziri, 2017), the authors used WordNet as an additional semantic resource in their work. ...
Manifold providers from a wide range of initiatives (private organizations, volunteered efforts, social media, etc.) offer enormous data amounts with geospatial characteristics. These efforts of many data providers entail multiple data scenarios and imply many viewpoints about the same feature, involving different representations, accuracy, models, vocabularies, etc. Various techniques or processes are employed to deal with these heterogeneity problems related to diverse data sources within the conflation research area. However, semantic conflation has not been addressed widely in the literature, unlike geometrical conflation. Hence, it is unclear what issues semantic conflation tries to solve and what activities, methods, metrics, and techniques have been used in existing GIScience investigations. In this article, we carry out a systematic review of approaches that focus on semantic aspects for geospatial data conflation. Besides, we analyze a wide selection of contributions following different criteria to depict a detailed semantic conflation status in GIScience. Our contributions are: (i) an overview of semantic conflation application domains, (ii) a characterization of semantic issues within these domains, (iii) the recognition of gaps and weaknesses of collected researches, and (iv) several open challenges and opportunities for next steps in this GIScience research area.
... Works have developed various methodologies to deal with trajectory data of moving objects. Some of them have dealt with detection and analysis of clusters of moving objects and their movement patterns (Demšar and Virrantaus 2010;Etienne et al. 2012;Ferreira et al. 2013;Kalnis et al. 2005;Willems et al. 2010), and estimation of building populations from behaviors of individuals (Greger 2015). Other studies have examined spatio-temporal dynamics and mobility patterns of moving objects by using particular types of data collected from mobile phone calls, sensors, and agricultural hen (Abedi et al. 2014;Daigle et al. 2014;Gao 2015;Hosseinyalamdary et al. 2015). ...
This study provides geographical counterpoint to existing knowledge of a dance piece through approaches from GIScience and visualization by focusing on spatio-temporal movement of dancers in a large dataset of the dance. The goal of this study is to introduce a new application to bridging art and science in the domain of dance and geography disciplines. The study utilizes existing methodologies in GIScience, including exploratory spatial data analysis (ESDA), spatial analysis, Relative Motion (REMO) analysis, and Qualitative Trajectory Calculus (QTC) analysis for the reasoning of the dance data. The results of the study demonstrate the following. First, spatio-temporal information in the dance can be better understood by using approaches in geography, including ESDA, spatial analysis, REMO analysis, QTC analysis, and visualization. Second, the REMO analysis measured relative azimuth, speed, and δ-speed of the dancers per space and time and intuitively visualized their interactions. Third, the QTC analysis showed an example of measuring similarity and difference between repetitive movements of the dancers. The study exhibits how approaches of GIScience in geography could contribute to finding new knowledge of choreographic information that has been, in general, hard to recognize through other disciplines such as dance and statistics.
... Another visualization (see Figure 3.3) was created by Willems et al. [56]. This tool also focuses on understanding the movement of vessels like [55]; more specifically, it aims to detect spatiotemporal patterns by visually testing hypotheses. ...
... Willems et al.[56] visualization tool. ...
With the recent increase in sea transportation usage, the importance of maritime
surveillance to detect unusual vessel behavior related to several illegal activities has
also risen. Unfortunately, the data collected by the surveillance systems are often
incomplete, creating a need for the data gaps to be filled using techniques such as
interpolation methods. However, such approaches do not decrease the uncertainty of
ship activities. Depending on the frequency of the data generated, they may even
make the operators more confused, inducing them to errors when evaluating ship
activities to tag them as unusual. Using domain knowledge to classify activities as
anomalous is essential in the maritime navigation environment since there is a wellknown
lack of labeled data in this domain. In an area where finding which trips
are anomalous is a challenging task using solely automatic approaches, we use visual
analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In
the current work, we investigate existing work that focuses on finding anomalies in
vessel trips and how they improve the user understanding of the interpolated data.
We then propose and develop a visual analytics tool that uses spatial segmentation
to divide trips into subtrajectories and give a score for each subtrajectory. We then
display these scores in tabular visualization where users can rank by segment to find
local anomalies. We also display the amount of interpolation in subtrajectories with
the score so users can use their insight and the trip display on the map to make sense
if the score is reliable. We did a user study to assess our tool’s usability and the
preliminary results showed that users were able to identify anomalous trips.
... When processing trajectory data from different sources, different scales, or different types, data fusion should be performed to prevent potential misunderstanding caused by incomplete information display and facilitate an observation with regard to the changing process of certain data and attributes in a context of other data and attributes and their correlation. For example, the security monitoring system designed by Willems et al. [37] displays the correlation between a pair of attributes in a trajectory contingency table (middle part of Fig. 13a). When the fusion of multiple datasets derives various types of multivariate data, a hybrid-rendering method is generally employed considering these data characteristics. ...
As a frontier research topic in the field of scientific visualization, trajectory data visualization extracts valuable patterns and knowledge from trajectory data for decision support via spatiotemporal trajectory visualization techniques. We propose the concept of multivariate trajectory data and interpret two categories of attributes that are based on geographical space and abstract space. Properly analyzing multivariate trajectory data depends on many factors such as visualization task and data sparsity. Therefore, we generalize rich interactions to explore the evolution of trajectory events and transform the issue into a more intelligibly perceptual task, which derives our discussion regarding advantages and limitations of the analytical methods. This review endeavors to provide a quick and thorough cognition and comprehension with regard to fundamental features and numerous outcomes in visual analytics for trajectory data, seeks to promote comparisons and criticisms about the descriptive framework for multivariate spatiotemporal trajectory data visualization, and aims to encourage the exploration of emerging methods and techniques.
... provides the steps and tools required to create a ship density map using historical AIS data [20]. The visualization also aims to help expert users discover the relationship between spatial, temporal, and vessel-type attributes [21]. ...
The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.
... A semantic trajectory ontology has been proposed by Baglioni et al. [3] who decompose trajectories in sub-trajectories including a series of stops and moves. This representation has been adopted in several other trajectory ontologies, and the stops and moves concepts are often associated to geographic and application domain information to improve understanding domain facts (Yan et al., 2014), (Vandecasteele, et al., 2014), (Willems et al., 2010). Trajectory with ROI has been also proposed to enrich trajectory data with geo-data i.e., POI and ROI (Yan., 2009). ...
Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.
... ? in Willems et al. (2009), Willems et al. (2010), and Willems et al. ...
The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection.
This article is categorized under:
• Application Areas > Government and Public Sector
• Algorithmic Development > Spatial and Temporal Data Mining
... The drawback of this method is spatial distortion, occlusion, and clutter, all due to projection of a 3D space into a 2D visualization. To avoid the clutter, Willems et al. (2010) proposed a trajectory contingency map: a matrix in which each element displays different attributes and different time intervals. ...
We provide an automatic algorithm proceeding from raw automatic identification system (AIS) data, through filters, highlighting vessel types, cargo, or regions, to discrete transport patterns, describing the geographic, directional, and total volume of transport. The designed algorithm automatically detects major ports or zones, where vessels are stationary and close to land, and groups them hierarchically in order to determine the end nodes in a transport matrix. In conjunction with vessel type, size, and draught information, the transport matrix is then used to compile transported tonnage and vessel count, to display as trade flows between nodes. In addition, by clicking on individual routes, the system also provides quantitative details, such as summed tonnage, or passing vessel counts. We use trades carried out by VLCC and Supramax bulk carriers as examples to demonstrate the results. The contribution of the research is threefold. First, we use AIS data to aggregate “real-time” trade flows. This substantially reduces latency from the traditional way of mapping flows and generating trade statistics. Second, the ability to visualize changes in seasonal or regional trading patterns enables economists and policy makers to monitor changes at a macro level. Finally, the ability to dripple down to the individual ship level allows commercial traders to monitor vessels or company performances, which provides valuable information in trading and decision-making.
... The several methods of geovisual analytics that have been proposed by researchers until now constitute a solid background for exploring various questions and problems dealing with exploration of spatio-temporal events; as it is the case with the behavior of ships in maritime surveillance. The use of such techniques can therefore bring new solutions, or new outcomes, to the actors using it [24], [28], [33]. ...
This paper presents an original approach for supporting the resilience in Maritime Domain Awareness, based on geovisual analytics. While many research projects focus on developing rules for detecting anomalies at by automated means, there is no support to visual exploration led by human operators. We investigate the use of visual methods for analyzing mobility data of ships. Behaviors of interest can be known (modeled) or unknown, asking for various ways of visualizing and studying the information. We assume that supporting the use of geovisual analytics will make the exploration and the analysis process easier, reducing the cognitive load of the tasks led by the actors of maritime surveillance. The detection and the identification of threats at sea are improved by using adequate visualization methods, regarding the context of use. Our suggested framework is based on ontologies for maritime domain awareness and geovisual analytics environments, coupled to rules.
