Schreck, Tobias


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Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters

2022, Koh, Elliot, Blumenschein, Michael, Shao, Lin, Schreck, Tobias

Visualizing high-dimensional (HD) data is a key challenge for data scientists. The importance of this challenge is to properly map data properties, e.g., patterns, outliers, and correlations, from a HD data space onto a visualization. Parallel coordinate plots (PCPs) are a common way to do this. However, a PCP visualization can be arranged in several ways by reordering its axes, which may lead to different visual representations. Many methods have been developed with the aim of evaluating the quality of reorderings of given PCP view. A high-dimensional data set can be divided into multiple classes, and being able to identify differences between the classes is important. Then, besides overlaying the groups in a single PCP, we can show the different groups in individual PCPs in a small multiple fashion. This raises the problem of jointly reordering sets of PCPs to create meaningful reorderings of the set of plots. We propose a joint reordering strategy, based on maximizing the pairwise visual difference in PCPs, such as to support their contrastive comparison. We present an implementation and an evaluation of the reordering strategy to assess the effectiveness of the method. The approach shows feasible in bringing out pairwise difference in PCP plots and hence support comparison of grouped data.


Visual analytics of urban environments using high-resolution geographic data

2010, Bak, Peter, Omer, Itzhak, Schreck, Tobias

High-resolution urban data at house level are essential for understanding the relationship between objects of the urban built environment (e.g. streets, housing types, public resources and open spaces). However, it is rather difficult to analyze such data due to the huge amount of urban objects, their multidimensional character and the complex spatial relation between them. In this paper we propose a methodology for assessing the spatial relation between geo-referenced urban environmental variables, in order to identify typical or significant spatial configurations as well as to characterize their geographical distribution. Configuration in this sense refers to the unique combination of different urban environmental variables. We structure the analytic process by defining spatial configurations, multidimensional clustering of the individual configurations, and identifying emerging patterns of interesting configurations. This process is based on the tight combination of interactive visualization methods with automatic analysis techniques. We demonstrate the usefulness of the proposed methods and methodology in an application example on the relation between street network topology and distribution of land uses in a city.


A visual analytics approach for assessing pedestrian friendliness of urban environments

2013, Schreck, Tobias, Omer, Itzhak, Bak, Peter, Lerman, Yoav

The availability of efficient transportation facilities is vital to the function and development of modern cities. Promoting walking is crucial for supporting livable communities and cities. Assessing the quality of pedestrian facilities and constructing appropriate pedestrian walking facilities are important tasks in public city planning. Additionally, walking facilities in a community affect commercial activities including private investment decisions such as those of retailers. However, analyzing what we call pedestrian friendliness in an urban environment involves multiple data perspectives, such as street networks, land use, and other multivariate observation measurements, and consequently poses significant challenges. In this study, we investigate the effect of urban environment properties on pedestrian movement in different locations in the metropolitan region of Tel Aviv. The first urban area we investigated was the inner city of the Tel Aviv metropolitan region, one of the central regions in Tel Aviv, a city that serves many non-local residents. For simplicity, we refer to this area as Tel Aviv. We also investigated Bat Yam, a small city, whose residents use many of the services of Tel Aviv. We apply an improved tool for visual analysis of the correlation between multiple independent and one dependent variable in geographical context. We use the tool to investigate the effect of functional and topological properties on the volume of pedestrian movement. The results of our study indicate that these two urban areas differ greatly. The urban area of Tel Aviv has much more correspondence and interdependency among the functional and topological properties of the urban environment that might influence pedestrian movement. We also found that the pedestrian movements as well as the related urban environment properties in this region are distributed geographically in a more equal and organized form.


Visual search and analysis in Complex information spaces : approaches and research challenges

2012, Landesberger, Tatiana von, Schreck, Tobias, Fellner, Dieter, Kohlhammer, Jörn

One of the central motivations for visual analytics research is the so-called information overload – implying the challenge for human users in understanding and making decisions in presence of too much information [37]. Visual-interactive systems, integrated with automatic data analysis techniques, can help in making use of such large data sets [35]. Visual Analytics solutions not only need to cope with data volumes that are large on the nominal scale, but also with data that show high complexity. Important characteristics of complex data are that the data items are difficult to compare in a meaningful way based on the raw data. Also, the data items may be composed of different base data types, giving rise to multiple analytical perspectives. Example data types include research data compound of several base data types, multimedia data composed of different media modalities, etc. In this paper, we discuss the role of data complexity for visual analysis and search, and identify implications for designing respective visual analytics applications. We first introduce a data complexity model, and present current example visual analysis approaches based on it, for a selected number of complex data types.We also outline important research challenges for visual search and analysis we deem important.